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Question: Many machine learning algorithms represent input data with vector embeddings or discrete codes. When inputs exhibit compositional structure (e.g. objects built from parts or procedures from subroutines), it is natural to ask whether this compositional structure is reflected in the the inputs’ learned representations. While the assessment of compositionality in languages has received significant attention in linguistics and adjacent fields, the machine learning literature lacks general-purpose tools for producing graded measurements of compositional structure in more general (e.g. vector-valued) representation spaces. We describe a procedure for evaluating compositionality by measuring how well the true representation-producing model can be approximated by a model that explicitly composes a collection of inferred representational primitives. We use the procedure to provide formal and empirical characterizations of compositional structure in a variety of settings, exploring the relationship between compositionality and learning dynamics, human judgments, representational similarity, and generalization. Figure 1: Representations arising from a communication game. In this game, an observation (b) is presented to a learned speaker model (c), which encodes it as a discrete character sequence (d) to be consumed by a listener model for some downstream task. The space of inputs has known compositional structure (a). We want to measure the extent to which this structure is reflected (perhaps imperfectly) in the structure of the learned codes. The success of modern representation learning techniques has been accompanied by an interest in understanding the structure of learned representations. One feature shared by many humandesigned representation systems is compositionality: the capacity to represent complex concepts (from objects to procedures to beliefs) by combining simple parts BID18. While many machine learning approaches make use of human-designed compositional analyses for representation and prediction BID44 BID16, it is also natural to ask whether (and how) compositionality arises in learning problems where compositional structure has not been built in from the start. Consider the example in Figure 1, which shows a hypothetical character-based encoding scheme learned for a simple communication task (similar to the one studied by). Is this encoding scheme compositional? That is, to what extent can we analyze the agents' messages as being built from smaller pieces (e.g. pieces xx meaning blue and bb meaning triangle)?A large body of work, from early experiments on language evolution to recent deep learning models BID24 ), aims to answer questions like this one. But existing solutions rely on manual (and often subjective) analysis of model outputs BID32, or at best automated procedures tailored to the specifics of individual problem domains BID7. They are difficult to compare and difficult to apply systematically. We are left with a need for a standard, formal, automatable and quantitative technique for evaluating claims about compositional structure in learned representations. The present work aims at first steps toward meeting that need. We focus on an oracle setting where the compositional structure of model inputs is known, and where the only question is whether this structure is reflected in model outputs. This oracle evaluation paradigm covers most of the existing representation learning problems in which compositionality has been studied. The first contribution of this paper is a simple formal framework for measuring how well a collection of representations (discrete-or continuous-valued) reflects an oracle compositional analysis of model inputs. We propose an evaluation metric called TRE, which provides graded judgments of compositionality for a given set of (input, representation) pairs. The core of our proposal is to treat a set of primitive meaning representations as hidden, and optimize over them to find an explicitly compositional model that approximates the true model as well as possible. For example, if the compositional structure that describes an object is a simple conjunction of attributes, we can search for a collection of "attribute vectors" that sum together to produce the observed object representations; if it is a sparse combination of (attribute, value) pairs we can additionally search for "value vectors" and parameters of a binding operation; and so on for more complex compositions. Having developed a tool for assessing the compositionality of representations, the second contribution of this paper is a survey of applications. We present experiments and analyses aimed at answering four questions about the relationship between compositionality and learning:• How does compositionality of representations evolve in relation to other measurable model properties over the course of the learning process? (Section 4) • How well does compositionality of representations track human judgments about the compositionality of model inputs? (Section 5) • How does compositionality constrain distances between representations, and how does TRE relate to other methods that analyze representations based on similarity? (Section 6) • Are compositional representations necessary for generalization to out-of-distribution inputs?(Section 7)We conclude with a discussion of possible applications and generalizations of TRE-based analysis. Arguments about whether distributed (and other non-symbolic) representations could model compositional phenomena were a staple of 1980s-era connectionist-classicist debates. BID42 provides an overview of this discussion and its relation to learnability, as well as a concrete implementation of a compositional encoding scheme with distributed representations. Since then, numerous other approaches for compositional representation learning have been proposed, with BID30 BID43 and without BID15 BID22 ) the scaffolding of explicit composition operations built into the model. The main experimental question is thus when and how compositionality arises "from scratch" in the latter class of models. In order to answer this question it is first necessary to determine whether compositional structure is present at all. Most existing proposals come from linguistics and and philosophy, and offer evaluations of compositionality targeted at analysis of formal and natural languages BID8 BID28. Techniques from this literature are specialized to the details of linguistic representations-particularly the algebraic structure of grammars BID31. It is not straightforward to apply these techniques in more general settings, particularly those featuring non-string-valued representation spaces. We are not aware of existing work that describes a procedure suitable for answering questions about compositionality in the general case. Machine learning research has responded to this absence in several ways. One class of evaluations BID32 BID11 derives judgments from ad-hoc manual analyses of representation spaces. These analyses provide insight into the organization of representations but are time-consuming and non-reproducible. Another class of evaluations BID6 BID0 BID4 ) exploits task-specific structure (e.g. the ability to elicit pairs of representations known to feature particular relationships) to give evidence of compositionality. Our work aims to provide a standard and scalable alternative to these model-and task-specific evaluations. Other authors refrain from measuring compositionality directly, and instead base analysis on measurement of related phenomena, for which more standardized evaluations exist. Examples include correlation between representation similarity and similarity of oracle compositional analyses BID7 and generalization to structurally novel inputs BID26. Our approach makes it possible to examine the circumstances under which these surrogate measures in fact track stricter notions of compositionality; similarity is discussed in Sec. 6 and generalization in Sec. 7.A long line of work in natural language processing BID13 BID2 BID12 BID19 focuses on learning composition functions to produce distributed representations of phrases and sentences-that is, for purposes of modeling rather than evaluation. We use one experiment from this literature to validate our own approach (Section 5). On the whole, we view work on compositional representation learning in NLP as complementary to the framework presented here: our approach is agnostic to the particular choice of composition function, and the aforementioned references provide well-motivated choices suitable for evaluating data from language and other sources. Indeed, one view of the present work is simply as a demonstration that we can take existing NLP techniques for compositional representation learning, fit them to representations produced by other models (even in non-linguistic settings), and view the ing training loss as a measure of the compositionality of the representation system in question. Consider again the communication task depicted in Figure 1. Here, a speaker model observes a target object described by a feature vector. The speaker sends a message to a listener model, which uses the message to complete a downstream task-for example, identifying the referent from a collection of distractors based on the content of the message BID17 ). Messages produced by the speaker model serve as representations of input objects; we want to know if these representations are compositional. Crucially, we may already know something about the structure of the inputs themselves. In this example, inputs can be identified via composition of categorical shape and color attributes. How might we determine whether this oracle analysis of input structure is reflected in the structure of representations? This section proposes an automated procedure for answering the question. Representations A representation learning problem is defined by a dataset X of observations x (Figure 1b); a space Θ of representations θ (Figure 1d); and a model f: X → Θ (Figure 1c). We assume that the representations produced by f are used in a larger system to accomplish some concrete task, the details of which are not important for our analysis. Derivations The technique we propose additionally assumes we have prior knowledge about the compositional structure of inputs. In particular, we assume that inputs can be labeled with treestructured derivations d (Figure 1a), defined by a finite set D 0 of primitives and a binary bracketing operation ·, ·, such that if DISPLAYFORM0 Compositionality In intuitive terms, the representations computed by f are compositional if each f (x) is determined by the structure of D(x). Most discussions of compositionality, following BID31, make this precise by defining a composition operation θ a * θ b → θ in the space of representations. Then the model f is compositional if it is a homomorphism from inputs to representations: we require that for any x with DISPLAYFORM1 In the linguistic contexts for which this definition was originally proposed, it is straightforward to apply. Inputs x are natural language strings. Their associated derivations D(x) are syntax trees, and composition of derivations is syntactic composition. Representations θ are logical representations of meaning (for an overview see BID47 . To argue that a particular fragment of language is compositional, it is sufficient to exhibit a lexicon D 0 mapping words to their associated meaning representations, and a grammar for composing meanings where licensed by derivations. Algorithms for learning grammars and lexicons from data are a mainstay of semantic parsing approaches to language understanding problems like question answering and instruction following BID49 BID9 BID1 .But for questions of compositionality involving more general representation spaces and more general analyses, the above definition presents two difficulties: In the absence of a clearly-defined syntax of the kind available in natural language, how do we identify lexicon entries: the primitive parts from which representations are constructed? What do we do with languages like the one in Figure 1d, which seem to exhibit some kind of regular structure, but for which the homomorphism condition given in Equation 1 cannot be made to hold exactly?Consider again the example in Figure 1. The oracle derivations tell us to identify primitive representations for dark, blue, green, square, and triangle. The derivations then suggest a process for composing these primitives (e.g. via string concatenation) to produce full representations. The speaker model is compositional (in the sense of Equation 1) as long as there is some assignment of representations to primitives such that for each model input, composing primitive representations according to the oracle derivation reproduces the speaker's prediction. In Figure 1 there is no assignment of strings to primitives that reproduces model predictions exactly. But predictions can be reproduced approximately-by taking xx to mean blue, aa to mean square, etc. The quality of the approximation serves as a measure of the compositionality of the true predictor: predictors that are mostly compositional but for a few exceptions, or compositional but for the addition of some noise, will be well-approximated on average, while arbitrary mappings from inputs to representations will not. This suggests that we should measure compositionality by searching for representations that allow an explicitly compositional model to approximate the true f as closely as possible. We define our evaluation procedure as follows:Tree Reconstruction Error (TRE)First choose: DISPLAYFORM2, a compositional approximation to f with parameters η, as: DISPLAYFORM3 f η has one parameter vector η i for every d i in D 0; these vectors are members of the representation space Θ.Given a dataset X of inputs x i with derivations d i = D(x i), compute: DISPLAYFORM4 Then we can define datum-and dataset-level evaluation metrics: DISPLAYFORM5 DISPLAYFORM6 TRE and compositionality How well does the evaluation metric TRE(X) capture the intuition behind Equation 1? The definition above uses parameters η i to witness the constructability of representations from parts, in this case by explicitly optimizing over those parts rather than taking them to be given by f. Each term in Equation 2 is analogous to an instance of Equation 1, measuring how wellf η * (x i), the best compositional prediction, matches the true model prediction f (x i). In the case of models that are homomorphisms in the sense of Equation 1, TRE reduces to the familiar case: DISPLAYFORM7 Learnable composition operators The definition of TRE leaves the choice of δ and * up to the evaluator. Indeed, if the exact form of the composition function is not known a priori, it is natural to define * with free parameters (as in e.g. BID2, treat these as another learned part off, and optimize them jointly with the η i . However, some care must be taken when choosing * (especially when learning it) to avoid trivial solutions:Remark 2. Suppose D is injective; that is, every x ∈ X is assigned a unique derivation. Then there is always some * that achieves TRE(X) = 0: DISPLAYFORM8 as in the preceding definition, and setf = f.In other words, some pre-commitment to a restricted composition function is essentially inevitable: if we allow the evaluation procedure to select an arbitrary composition function, the will be trivial. This paper features experiments with * in both a fixed functional form and a learned parametric one. Implementation details For models with continuous Θ and differentiable δ and *, TRE(X) is also differentiable. Equation 2 can be solved using gradient descent. We use this strategy in Sections 4 and 5. For discrete Θ, it may be possible to find a continuous relaxation with respect to which δ(θ, ·) and * are differentiable, and gradient descent again employed. We use this strategy in Section 7 (discussed further there). An implementation of an SGD-based TRE solver is provided in the accompanying software release. For other problems, task-specific optimizers (e.g. machine translation alignment models; BID4 or general-purpose discrete optimization toolkits can be applied to Equation 2.The remainder of the paper highlights ways of using TRE to answer questions about compositionality that arise in machine learning problems of various kinds. We begin by studying the relationship between compositionality and learning dynamics, focusing on the information bottleneck theory of representation learning proposed by BID45 . This framework proposes that learning in deep models consists of an error minimization phase followed by a compression phase, and that compression is characterized by a decrease in the mutual information between inputs and their computed representations. We investigate the hypothesis that the compression phase finds a compositional representation of the input distribution, isolating decision-relevant attributes and discarding irrelevant information. FIG1). We predict classifiers in a meta-learning framework BID39 BID37: for each sub-task, the learner is presented with two images corresponding to some compositional visual concept (e.g. "digit 8 on a black " or "green with heavy stroke") and must determine whether a held-out image is an example of the same visual concept. Given example images x 1 and x 2, a test image x *, and label y *, the model computes: DISPLAYFORM0 We use θ as the representation of a classifier for analysis. The model is trained to minimize the logistic loss between logitsŷ and ground-truth labels y *. More details are given in Appendix A.Compositional structure Visual concepts used in this task are all single attributes or conjunctions of attributes; i.e. their associated derivations are of the form attr or attr 1, attr 2. Attributes include color, digit color, digit identity and stroke type. The composition function * is addition and the distance δ(θ, θ) is cosine similarity 1 − θ θ /(θ θ).Evaluation The training dataset consists of 9000 image triplets, evenly balanced between positive and negative classes, with a validation set of 500 examples. At convergence, the model achieves validation accuracy of 75.2% on average over ten training runs. (Perfect accuracy is not possible because the true classifier is not fully determined by two training examples). We explore the relationship between the information bottleneck and compositionality by comparing TRE(X) to the mutual information I(θ; x) between representations and inputs over the course of training. Both quantities are computed on the validation set, calculating TRE(X) as described in Section 3 and I(θ; X) as described in BID41. (For discussion of limitations of this approach to computing mutual information between inputs and representations, see .) FIG2 shows the relationship between TRE(X) and I(θ; X). Recall that small TRE is indicative of a high degree of compositionality. It can be seen that both mutual information and reconstruction error are initially low (because representations initially encode little about distinctions between inputs). Both increase over the course of training, and decrease together after mutual information reaches a maximum FIG2. This pattern holds if we plot values from multiple training runs at the same time FIG2 ), or if we consider only the postulated compression phase FIG2 ). These are consistent with the hypothesis that compression in the information bottleneck framework is associated with the discovery of compositional representations. Next we investigate a more conventional representation learning task. High-dimensional embeddings of words and phrases are useful for many natural language processing applications BID46, and many techniques exist to learn them from unlabeled text BID14 BID29. The question we wish to explore is not whether phrase vectors are compositional in aggregate, but rather how compositional individual phrase representations are. Our hypothesis is that bigrams whose representations have low TRE are those whose meaning is essentially compositional, and well-explained by the constituent words, while bigrams with large reconstruction error will correspond to non-compositional multi-word expressions BID33 ).This task is already well-studied in the natural language processing literature BID36, and the analysis we present differs only in the use of TRE to search for atomic representations rather than taking them to be given by pre-trained word representations. Our goal is to validate our approach in a language processing context, and show how existing work on compositionality (and representations of natural language in particular) fit into the more general framework proposed in the current paper. We train embeddings for words and bigrams using the CBOW objective of BID29 using the implementation provided in FastText BID5 with 100-dimensional vectors and a context size of 5. Vectors are estimated from a 250M-word subset of the Gigaword dataset BID34. More details are provided in Appendix A.Compositional structure We want to know how close phrase embeddings are to the composition of their constituent word embeddings. We define derivations for words and phrases in the natural way: single words w have primitive derivations d = w; bigrams w 1 w 2 have derivations of the form w 1, w 2. The composition function is again vector addition and distance is cosine distance. (Future work might explore learned composition functions as in e.g. BID21, for future work.) We compare bigram-level judgments of compositionality computed by TRE with a dataset of human judgments about noun-noun compounds BID35. In this dataset, humans rate bigrams as compositional on a scale from 0 to 5, with highly conventionalized phrases like gravy train assigned low scores and graduate student assigned high ones. Results We reproduce the of BID36 within the tree reconstruction error framework: for a given x, TRE(x) is anticorrelated with human judgments of compositionality (ρ = −0.34, p < 0.01). Collocations rated "most compositional" by our approach (i.e. with lowest TRE) are: application form, polo shirt, research project; words rated "least compositional" are fine line, lip service, and nest egg. The next section aims at providing a formal, rather than experimental, characterization of the relationship between TRE and another perspective on the analysis of representations with help from oracle derivations. BID7 introduce a notion of topographic similarity, arguing that a learned representation captures relevant domain structure if distances between learned representations are correlated with distances between their associated derivations. This can be viewed as providing a weak form of evidence for compositionality-if the distance function rewards pairs of representations that share overlapping substructure (as might be the case with e.g. string edit distance), edit distance will be expected to correlate with some notion of derivational similarity.In this section we aim to clarify the relationship between the two evaluations. To do this we first need to equip the space of derivations described in Section 3 with a distance function. As the derivations considered in this paper are all tree-structured, it is natural to use a simple tree edit distance BID3 for this purpose. We claim the following: Proposition 1. Letf =f η * be an approximation to f estimated as in Equation 2, with all TRE(x) ≤ for some. Let ∆ be the tree edit distance (defined formally in Appendix B, Definition 2), and let δ be any distance on Θ satisfying the following properties: DISPLAYFORM0, where 0 is the identity element for *. DISPLAYFORM1 (This condition is satisfied by any translation-invariant metric.)Then ∆ is an approximate upper bound on δ: for any DISPLAYFORM2 In other words, representations cannot be much farther apart than the derivations that produce them. Proof is provided in Appendix B.We emphasize that small TRE is not a sufficient condition for topographic similarity as defined by BID7: very different derivations might be associated with the same representation (e.g. when representing arithmetic expressions by their ). But this does demonstrate that compositionality imposes some constraints on the inferences that can be drawn from similarity judgments between representations. In our final set of experiments, we investigate the relationship between compositionality and generalization. Here we focus on communication games like the one depicted in Figure 1 and in more detail in FIG3. As in the previous section, existing work argues for a relationship between compositionality and generalization, claiming that agents need compositional communication protocols to generalize to unseen referents BID26 BID11. Here we are able to evaluate this claim empirically by training a large number of agents from random initial conditions, measuring the compositional structure of the language that emerges, and seeing how this relates to their performance on both familiar and novel objects. A speaker model observes a pair of target objects, and sends a description of the objects (as a discrete code) to a listener model. The listener attempts to reconstruct the targets, receiving fractional reward for partially-correct predictions. Our experiment focuses on a reference game BID20. Two policies are trained: a speaker and a listener. The speaker observes pair of target objects represented with a feature vector. The speaker then sends a message (coded as a discrete character sequence) to the listener model. The listener observes this message and attempts to reconstruct the target objects by predicting a sequence of attribute sets. If all objects are predicted correctly, both the speaker and the listener receive a reward of 1 (partial credit is awarded for partly-correct objects; FIG3).Because the communication protocol is discrete, policies are jointly trained using a policy gradient objective BID48. The speaker and listener are implemented with RNNs; details are provided in Appendix A.Compositional structure Every target referent consists of two objects; each object has two attributes. The derivation associated with each communicative task thus has the tree structure attr 1a, attr 1b, attr 2a, attr 2b. We hold out a subset of these object pairs at training time to evaluate generalization: in each training run, 1/3 of possible reference candidates are never presented to the agent at training time. Where the previous examples involved a representation space of real embeddings, here representations are fixed-length discrete codes. Moreover, the derivations themselves have a more complicated semantics than in Sections 4 and 5: order matters, and a commutative operation like addition cannot capture the distinction between green, square, blue, triangle and green, triangle, blue, square. We thus need a different class of composition and distance operations. We represent each agent message as a sequence of one-hot vectors, and take the error function δ to be the 1 distance between vectors. The composition function has the form: DISPLAYFORM0 with free composition parameters η * = {A, B} in Equation 2. These matrices can redistribute the tokens in θ and θ across different positions of the input string, but cannot affect the choice of the tokens themselves; this makes it possible to model non-commutative aspects of string production. To (b) However, compositional languages also exhibit lower absolute performance (r = 0.57, p < 1e−9). Both facts remain true even if we restrict analysis to "successful" training runs in which agents achieve a reward > 0.5 on held-out referents (r = 0.6, p < 1e−3 and r = 0.38, p < 0.05 respectively). Figure 6: Fragment of languages ing from two multiagent training runs. In the first section, the left column shows the target referent, while the remaining columns show the message generated by speaker in the given training run after observing the referent. The two languages have substantially different TRE, but induce similar listener performance (Train and Test reward).compute TRE via gradient descent, we allow the elements of D 0 to be arbitrary vectors (intuitively assigning fractional token counts to string indices) rather than restricting them to one-hot indicators. With this change, both δ and * have subgradients and can be optimized using the same procedure as in preceding sections. Results We train 100 speaker-listener pairs with random initial parameters and measure their performance on both training and test sets. Our suggest a more nuanced view of the relationship between compositionality and generalization than has been argued in the existing literature. TRE is significantly correlated with generalization error (measured as the difference between training accuracies, FIG4). However, TRE is also significantly correlated with absolute model reward (FIG4)-"compositional" languages more often from poor communication strategies than successful ones. This is largely a consequence of the fact that many languages with low TRE correspond to trivial strategies (for example, one in which the speaker sends the same message regardless of its observation) that in poor overall performance. Moreover, despite the correlation between TRE and generalization error, low TRE is by no means a necessary condition for good generalization. We can use our technique to automatically mine a collection of training runs for languages that achieve good generalization performance at both low and high levels of compositionality. Examples of such languages are shown in Figure 6. We have introduced a new evaluation method called TRE for generating graded judgments about compositional structure in representation learning problems where the structure of the observations is understood. TRE infers a set of primitive meaning representations that, when composed, approximate the observed representations, then measures the quality of this approximation. We have applied TRE-based analysis to four different problems in representation learning, relating compositionality to learning dynamics, linguistic compositionality, similarity and generalization. Many interesting questions regarding compositionality and representation learning remain open. The most immediate is how to generalize TRE to the setting where oracle derivations are not available; in this case Equation 2 must be solved jointly with an unsupervised grammar induction problem BID25. Beyond this, it is our hope that this line of research opens up two different kinds of new work: better understanding of existing machine learning models, by providing a new set of tools for understanding their representational capacity; and better understanding of problems, by better understanding the kinds of data distributions and loss functions that give rise to compositionalor non-compositional representations of observations. Code and data for all experiments in this paper are provided at https://github.com/jacobandreas/tre. Thanks to Daniel Fried and David Gaddy for feedback on an early draft of this paper. The author was supported by a Facebook Graduate Fellowship at the time of writing. Few-shot classification The CNN has the following form:Conv(out=6, kernel=5) ReLU MaxPool(kernel=2) Conv(out=16, kernel=5) ReLU MaxPool(kernel=2) Linear(out=128) ReLU Linear(out=64) ReLUThe model is trained using ADAM BID23 ) with a learning rate of.001 and a batch size of 128. Training is ended when the model stops improving on a held-out set. Word embeddings We train FastText BID5 on the first 250 million words of the NYT section of Gigaword BID34. To acquire bigram representations, we pre-process this dataset so that each occurrence of a bigram from the BID35 dataset is treated as a single word for purposes of estimating word vectors. Communication The encoder and decoder RNNs both use gated recurrent units BID10 with embeddings and hidden states of size 256. The size of the discrete vocabulary is set to 16 and the maximum message length to 4. Training uses a policy gradient objective with a scalar baseline set to the running average reward; this is optimized using ADAM BID23 ) with a learning rate of.001 and a batch size of 256. Each model is trained for 500 steps. Models are trained by sampling from the decoder's output distribution, but greedy decoding is used to evaluate performance and produce Figure 6. First, some definitions:Definition 1. The size of a derivation is given by: DISPLAYFORM0 Definition 2. The tree edit distance between derivations is defined by: Proof. For d ∈ D 0 this follows immediately from Condition 2 in the proposition. For composed derivations it follows from Condition 3 taking θ k = θ = 0 and induction on |d|. DISPLAYFORM1

This paper proposes a simple procedure for evaluating compositional structure in learned representations, and uses the procedure to explore the role of compositionality in four learning problems.

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It's the quantum algorithm for Expectation Maximization. It's fast: the runtime depends only polylogarithmically on the number of elements in the dataset.  == The Expectation-Maximization (EM) algorithm is a fundamental tool in unsupervised machine learning. It is often used as an efficient way to solve Maximum Likelihood (ML) and Maximum A Posteriori estimation problems, especially for models with latent variables. It is also the algorithm of choice to fit mixture models: generative models that represent unlabelled points originating from $k$ different processes, as samples from $k$ multivariate distributions. In this work we define and use a quantum version of EM to fit a Gaussian Mixture Model. Given quantum access to a dataset of $n$ vectors of dimension $d$, our algorithm has convergence and precision guarantees similar to the classical algorithm, but the runtime is only polylogarithmic in the number of elements in the training set, and is polynomial in other parameters - as the dimension of the feature space, and the number of components in the mixture. We generalize further the algorithm by fitting any mixture model of base distributions in the exponential family. We discuss the performance of the algorithm on datasets that are expected to be classified successfully by those algorithms, arguing that on those cases we can give strong guarantees on the runtime. Over the last few years, the effort to find real world applications of quantum computers has greatly intensified. Along with chemistry, material sciences, finance, one of the fields where quantum computers are expected to be most beneficial is machine learning. A number of different algorithms have been proposed for quantum machine learning (; ; ; ; Subaşı et al., 2019;), both for the supervised and unsupervised setting, and despite the lack of large-scale quantum computers and quantum memory devises, some quantum algorithms have been demonstrated in proof-of-principle experiments (; ;). Here, we look at Expectation-Maximization (EM), a fundamental algorithm in unsupervised learning, that can be used to fit different mixture models and give maximum likelihood estimates with the so-called latent variable models. Such generative models are one of the most promising approaches for unsupervised problems. The goal of a generative model is to learn a probability distribution that is most likely to have generated the data collected in a training set V ∈ R n×d of n vectors of d features. Fitting the model consists in learning the parameters of a probability distribution p in a certain parameterized family that best describes our vectors v i. We will see that, thanks to this formulation, we can reduce a statistical problem into an optimization problem using maximum likelihood estimation (ML) estimation. The likelihood is the function that we use to measure how good a model is for explaining a given dataset. For a given machine learning model with parameters γ, the likelihood of our data set V is the probability that the data have been generated by the model with parameters γ, assuming each point is independent and identically distributed. We think the likelihood as a function of γ, holding the dataset V fixed. For p(v i |γ) the probability that a point v i comes from model γ, the likelihood is defined as L(γ; V):= n i=1 p(v i |γ). From this formula, we can see that in order to find the best parameters γ * of our model we need to solve an optimization problem. For numerical and analytical reasons, instead of maximizing the likelihood L, it is common practice to find the best model by maximizing the log-likelihood function (γ; V) = log L(γ; V) = n i=1 log p(v i |γ). In this context, we want to find the model that maximizes the log-likelihood: γ * M L:= arg max γ n i=1 log p(v i |γ). The procedure to calculate the log-likelihood depends on the specific model under consideration. A possible solution would be to use a gradient based optimization algorithm on. Unfortunately, due to the indented landscape of the function, gradient based techniques often do not perform well. Therefore, it is common to solve the maximum likelihood estimation (or maximum a priori) problem using the Expectation-Maximization (EM) algorithm. EM is an iterative algorithm which is guaranteed to converge to a (local) optimum of the likelihood. This algorithm has a striking variety of applications, and has been successfully used for medical imaging , image restoration , problems in computational biology , and so on. EM has been proposed in different works by different authors, but has been formalized as we know it only in 1977 . For more details, we refer to . In this work, we introduce Quantum Expectation-Maximization (QEM), a new algorithm for fitting mixture models. We detail its usage in the context of Gaussian Mixture Models, and we extend the to other distributions in the exponential family. We also generalize the by showing how to compute the MAP: the Maximum A Posteriori estimate of a mixture model. MAP estimates can be seen as the Bayesian version of maximum likelihood estimation problems. MAP estimates are often preferred over ML estimates, due to a reduced propensity to overfit. Our main can be stated as: Result (Quantum Expectation-Maximization). (see Theorem 3.9) For a data matrix V ∈ R n×d stored in an appropriate QRAM data structure and for parameters δ θ, δ µ > 0, Quantum Expectation-Maximization (QEM) fits a Maximum Likelihood (or a Maximum A Posteriori) estimate of a Gaussian Mixture Model with k components, in running time per iteration which is dominated by: where Σ is a covariance matrix of a Gaussian distribution, η is a parameter of the dataset related to the maximum norm of the vectors, δ θ, δ µ are error parameters in the QEM algorithm, µ(< √ d) is a factor appearing in quantum linear algebra and κ is the condition number of a matrix. Here we only kept the term in the running time that dominates for the range of parameters of interest. In Theorem 3.9 we explicate the running time of each step of the algorithm. The QEM algorithm runs for a number of iterations until a stopping condition is met (defined by a parameter τ > 0) which implies a convergence to a (local) optimum. Let's have a first high-level comparison of this with the standard classical algorithms. The runtime of a single iteration in the standard implementation of the EM algorithm is at least O(knd 2) . The advantage of the quantum algorithm is an exponential improvement with respect to the number of elements in the training set, albeit with a worsening on other parameters. It is crucial to find datasets where such a quantum algorithm can offer a speedup. For a reasonable range of parameters (d = 40, k = 10, η = 10, δ = 0.5, κ(V) = 25, κ(Σ) = 5, µ(Σ) = 4) which is motivated by some experimental evidence reported in Section 4, datasets where the number of samples in the order of O(10 12) might be processed faster on a quantum computer. One should expect that some of the parameters of the quantum algorithm can be improved, especially the dependence on the condition numbers and the errors, which can make enlarge the type of datasets where QEM can offer an advantage. Note that we expect the number of iterations of the quantum algorithm to be proportional to the number of iteration of the classical case. This is to be expected since the convergence rate does not change, and it is corroborated by previous experimental evidence in a similar scenario: the number of iterations needed by q-means algorithm for convergence, is proportional to the number of iterations of the classical k-means algorithm . Expectation-Maximization is widely used for fitting mixture models in machine learning . Most mixture models use a base distribution in the exponential family: Poisson , Binomial, Multinomial, log-normal , exponential (, Dirichlet multinomial , and others. EM is also used to fit mixtures of experts, mixtures of the student T distribution (which does not belong to the exponential family, and can be fitted with EM using ) and for factor analysis, probit regression, and learning Hidden Markov Models . There is a fair amount of quantum algorithms that have been proposed in the context of unsupervised learning. (Aïmeur et al., 2013; b;). Recently, classical machine learning algorithms were obtained by "dequantizing" quantum machine learning algorithms (b; a; c; Gilyén et al., 2018a;). The runtime of these classical algorithm is poly-logarithmic in the dimensions of the dataset. However, the polynomial dependence on the rank, the error, and the condition number, make these new algorithms impractical on interesting datasets, as shown experimentally by . Fast classical algorithm for GMM exists, albeit assuming only one shared covariance matrix , and without a polylogarithmic dependence in the number of elements in the training set. Independently of us, Miyahara, Aihara, and Lechner extended the q-means algorithm for Gaussian Mixture Models , using similar techniques. The main difference is that in their work the update step is performed using a hard-clustering approach (as in the k-means algorithm), that is for updating the centroid and the covariance matrices of a cluster j, only the data points for which cluster j is nearest are taken into account. In our work, we use the soft clustering approach (as in the classical EM algorithm), that is for updating the centroid and the covariance matrices of cluster j, all the data points weighted by their responsibility for cluster j are taken into account. Both approaches have merits and can offer advantages . As is common in machine learning literature, we introduce the Expectation-Maximization algorithm by using it to fit Gaussian Mixture Models (GMM). Mixture models are a popular generative model in machine learning. The intuition behind mixture models is to model complicated distributions by using a group of simpler (usually uni-modal) distributions. In this setting, the purpose of the learner is to model the data by fitting the joint probability distribution which most likely have generated our samples. In this section we describe GMM: probably the most used mixture model used to solve unsupervised classification problems. In fact, given a sufficiently large number of mixture components, it is possible to approximate any density defined in R d . In unsupervised settings, we are given a training set of unlabeled vectors v 1 · · · v n ∈ R d which we represent as rows of a matrix V ∈ R n×d. Let y i ∈ [k] one of the k possible labels for a point v i. We posit that the joint probability distribution of the data p(v i, y i) = p(v i |y i)p(y i), is defined as follow: The θ j are the mixing weights, i.e. the probabilities that y i = j, and N (µ j, Σ j) is the Gaussian distribution centered in µ j ∈ R d with covariance matrix Σ j ∈ R d×d. Note that the variables y i are unobserved, and thus are called latent variables. There is a simple interpretation for this model. We assume the data is created by first selecting an index j ∈ [k] by sampling according to Multinomial(θ), and then a vector v i is sampled from N (µ j, Σ j). Fitting a GMM to a dataset reduces to finding an assignment for the parameters: that best maximize the log-likelihood for a given dataset. Note that the algorithm used to fit GMM can return a local minimum which might be different than γ *: the model that represents the global optimum of the likelihood function. We use the letter φ to represent our base distribution, which in this case is the probability density function of a multivariate Gaussian distribution N (µ, Σ). Using this formulation, a GMM is expressed as: p(v) = k j=1 θ j φ(v; µ j, Σ j) where θ j are the mixing weights of the multinomial distribution such that k j=1 θ j = 1. The probability for an observation v i to be assigned to the component j is given by:. This value is called responsibility, and corresponds to the posterior probability of the sample i being assigned label j by the current model. As anticipated, to find the best parameters of our generative model, we maximize the log-likelihood of the data. For GMM, the likelihood is given by the following formula Alas, it is seldom possible to solve maximum likelihood estimation analytically (i.e. by finding the zeroes of the derivatives of the log-like function, and this is one of those cases. Fortunately, Expectation-Maximization is an iterative algorithm that solves numerically the optimization problem of ML estimation. To complicate things, the likelihood function for GMM is not convex, and thus we might find some local minima . If we were to know the latent variable y i, then the log-likelihood for GMM would be: This formula can be easily maximized with respect to the parameters θ, µ, and Σ. In the Expectation step we calculate the missing variables y i's, given a guess of the parameters (θ, µ, Σ) of the model. Then, in the Maximization step, we use the estimate of the latent variables obtained in the Expectation step to update the estimate of the parameters. While in the Expectation step we calculate a lower bound on the likelihood, in the Maximization step we maximize it. Since at each iteration the likelihood can only increase, the algorithm is guaranteed to converge, albeit possibly to a local optimum (see for the proof). During the Expectation step all the responsibilities are calculated, while in the Maximization step we update our estimate on the parameters γ t+1 = (θ t+1, µ t+1, Σ t+1). The stopping criterion for GMM is usually a threshold on the increment of the log-likelihood: if the log-likelihood changes less than a threshold between two iterations, then the algorithm stops. Notice that, since the value of the log-likelihood significantly depends on the amount of data points in the training sets, it is often preferable to adopt a scale-free stopping criterion, which does not depend on the number of samples. For instance, in the toolkit scikit-learn the stopping criterion is given by a tolerance on the average increment of the log-probability, which is chosen to be smaller than a certain τ, say 10 −3. More precisely, the stopping criterion is |E[log p(v i ; γ Dataset assumptions in GMM As in q-means , we have an assumption on the dataset that all elements of the mixture contribute proportionally to the total responsibility: i, e This is equivalent to requiring that clusters share a comparable amount of points in the "well-clusterability" assumption in q-means . It is also equivalent to assuming that θ j /θ l = Θ ∀j, l ∈ [k]. For convenience, in this work, we also assume that the dataset is normalized such that the shortest vector has norm 1 and define η:= max i v i 2 to be the maximum norm squared of a vector in the dataset. This is not a necessary requirement for our dataset, but it will simplify the analysis of our algorithm, allowing us to give strict bounds on the runtime. Preliminaries We assume a basic understanding of quantum computing, we recommend Nielsen and Chuang where σ is the smallest singular value which is greater than τ. With nnz(V) we mean the number of non-zero elements of the rows of V. When we say κ(V) we mean the condition number of the matrix V, that is the ratio between the biggest and the smallest (non-zero) singular value. All the tools used in this work, like quantum algorithms for computing distances and linear algebraic operations, are reported in the Supplementary Material section. In this section, we present a quantum Expectation-Maximization algorithm to fit a GMM. The algorithm can also be adapted fit other mixtures models where the probability distributions belong to the exponential family. As the GMM is both intuitive and one of the most widely used mixture models, our are presented for the GMM case. A robust version of the EM algorithm Similar to the work of , we define a ∆-robust version of the EM algorithm which we use to fit a GMM. The difference between this formalization and the original EM for GMM is simple. Here we explain the numerical error introduced in the training algorithm. Quantum access to the mixture model As in the classical algorithm, we use some subroutines to compute the responsibilities and update our current guess of the parameters. The classical algorithm has clearly two separate steps for Expectation and Maximization. In contrast, the quantum algorithm uses a subroutine to compute the responsibilities inside the step that performs the Maximization, that is the subroutines for computing responsibilities are called multiple times during the quantum Maximization step. During the quantum Maximization step, the algorithm updates the model parameters γ t by creating quantum states corresponding to parameters γ t+1 and then recovering classical estimates for these parameters using quantum tomography or amplitude amplification. In order for this subroutines to be efficient, the values of the GMM are stored in QRAM data structures and are updated following each maximization step. Definition 1 (Quantum access to a GMM). We say that we have quantum access to a GMM if the dataset V ∈ R n×d and model parameters θ j ∈ R, µ j ∈ R d, Σ j ∈ R d×d for all j ∈ [k] are stored in QRAM data structures which allow us to perform in time O(polylog(d)) the following mappings: • |j |i |0 → |j |i |σ Require: Quantum access to a GMM model, precision parameters δ θ, δ µ, and threshold τ. Ensure: A GMM γ t that maximizes locally the likelihood (γ; V), up to tolerance τ. 1: Use a heuristic described at the beginning of this section to determine an initial guess for γ 0 = (θ 0, µ 0, Σ 0), and store these parameters in the QRAM. 2: Use Lemma 3.1 to estimate the log determinant of the matrices {Σ Step 1: Get an estimate of θ t+1 such that θ t+1 − θ t+1 ≤ δ θ using Lemma 3.4. Step 2: Get an estimate {µ j t+1} k j=1 by using Lemma 3.6 to estimate each µ t+1 j and |µ Step 3: Get an estimate {Σ j t+1} k j=1 by using Lemma 3.7 to estimate Σ t+1 j F and |Σ t+1 j such that Step 4: Estimate E[p(v i ; γ t+1)] up to error τ /2 using Theorem 3.8. Step 5: Store γ t+1 in the QRAM, and use Lemma 3.1 to estimate the determinants {log det( . 10: Quantum initialization strategies exists, and are described in the Appendix. In this step of the quantum algorithm we are just showing how to compute efficiently the responsibilities as a quantum state. First, we compute the responsibilities in a quantum register, and then we show how to put them as amplitudes of a quantum state. We start by a classical algorithm used to efficiently approximate the log-determinant of the covariance matrices of the data. At each iteration of Quantum Expectation-Maximization we need to compute the determinant of the updated covariance matrices, which is done thanks to Lemma 3.1. We will see from the error analysis that in order to get an estimate of the GMM, we need to call Lemma 3.1 with precision for which the runtime of Lemma 3.1 gets subsumed by the running time of finding the updated covariance matrices through 3.7. Thus, we do not explicitly write the time to compute the determinant from now on in the algorithm and when we say that we update Σ we include an update on the estimate of log(det(Σ)) as well. Lemma 3.1 (Determinant evaluation). There is an algorithm that, given as input a matrix Σ and a parameter 0 < δ < 1, outputs an estimate log(det(Σ)) such that |log(det(Σ)) − log(det(Σ))| ≤ with probability 1 − δ in time: Now we can state the main brick used to compute the responsability: a quantum algorithm for evaluating the exponent of a Gaussian distribution. Lemma 3.2 (Quantum Gaussian Evaluation). Suppose we have stored in the QRAM a matrix V ∈ R n×d, the centroid µ ∈ R d and the covariance matrix Σ ∈ R d×d of a multivariate Gaussian distribution φ(v|µ, Σ), as well as an estimate for log(det(Σ)). Then for 1 > 0, there exists a quantum algorithm that with probability 1 − γ performs the mapping, ) is the exponent for the Gaussian probability density function. The running time of the algorithm is Using controlled operations it is simple to extend the previous Theorem to work with multiple Gaussians distributions (µ j, Σ j). That is, we can control on a register |j to do |j |i |0 → |j |i |φ(v i |µ j, Σ j). In the next Lemma we will see how to obtain the responsibilities r ij using the previous Theorem and standard quantum circuits for doing arithmetic, controlled rotations, and amplitude amplification. The Lemma is stated in a general way, to be used with any probability distributions that belong to an exponential family. Lemma 3.3 (Calculating responsibilities). Suppose we have quantum access to a GMM with parameters There are quantum algorithms that can: 1. Perform the mapping |i |j |0 → |i |j |r ij such that |r ij − r ij | ≤ 1 with probability 1 − γ in time: ij with high probability in time: Now we need to get a new estimate for the parameters of our model. This is the idea: at each iteration we recover the new parameters of the model as quantum states, and then recover it using tomography, amplitude estimation, or sampling. Once the new model has been recovered, we update the QRAM such that we get quantum access to the model γ t+1. The possibility to estimate θ comes from a call to the unitary we built to compute the responsibilities, and postselection. Lemma 3.4 (Computing θ t+1). We assume quantum access to a GMM with parameters γ t and let δ θ > 0 be a precision parameter. There exists an algorithm that estimates We use quantum linear algebra to transform the uniform superposition of responsibilities of the j-th mixture into the new centroid of the j-th Gaussian. Let R t j ∈ R n be the vector of responsibilities for a Gaussian j at iteration t. The following claim relates the vectors R t j to the centroids µ t+1 j. Claim 3.5. Let R t j ∈ R n be the vector of responsibilities of the points for the Gaussian j at time The proof is straightforward. Lemma 3.6 (Computing µ t+1 j). We assume we have quantum access to a GMM with parameters γ t. For a pre- From the ability to calculate responsibility and indexing the centroids, we derive the ability to reconstruct the covariance matrix of the Gaussians as well. Again, we use quantum linear algebra subroutines and tomography to recover an approximation of each Σ j. Recall that we have defined the matrix V ∈ R n×d Lemma 3.7 (Computing Σ t+1 j). We assume we have quantum access to a GMM with parameters γ t. We also have computed estimates µ j t+1 of all centroids such that µ j t+1 − µ t+1 j ≤ δ µ for precision parameter δ µ > 0. Then, there exists a quantum algorithm that outputs estimates for the new covariance matrices {Σ with high probability, in time, Now we are going to show how it is possible to get an estimate of the log-likelihood using a quantum procedure and access to a GMM model. A good estimate is crucial, as it is used as stopping criteria for the quantum algorithm as well. Classically, we stop to iterate the EM algorithm when | (γ t ; V) − (γ t+1 ; V)| < n, or equivalently, we can set a tolerance on the average increase in log probability:. From this we can estimate an upper bound on the log-likelihood as n log Lemma 3.8 (Quantum estimation of likelihood). We assume we have quantum access to a GMM with parameters γ t. For τ > 0, there exists a quantum algorithm that estimates E[p(v i ; γ t)] with absolute error τ in time Putting together all the previous Lemmas, we write the main of the work. Theorem 3.9 (QEM for GMM). We assume we have quantum access to a GMM with parameters γ t. For parameters δ θ, δ µ, τ > 0, the running time of one iteration of the Quantum Expectation-Maximization (QEM) algorithm is For the range of parameters of interest, the running time is dominated by T Σ. The proof follows directly from the previous lemmas. Note that the cost of the whole algorithm is given by repeating the Estimation and the Maximization steps several times, until the threshold on the log-likelihood is reached. Note also that the expression of the runtime can be simplified from the observation that the cost of performing tomography on the covariance matrices Σ j dominates the cost. In this section, we present the of some experiments on real datasets to bound the condition number and the other parameters of the runtime. Let's discuss the value of κ(Σ), κ(V), µ(Σ), and µ(V). We can thresholding the condition number by discarding small singular values of the matrix, as used in quantum linear algebra, might be advantageous. This is indeed done often in classical machine learning models, since discarding the eigenvalues smaller than a certain threshold might even improve upon the metric under consideration (i.e. often the accuracy), and is a form of regularization (, Section 6.5). This is equivalent to limiting the eccentricity of the Gaussians. We can have a similar consideration on the condition number of the dataset κ(V). As shown before, the condition number of the matrix V appearing in Lemma 3.2 is κ 2 (V). Similarly, we can claim that the value of µ(V) will not increase significantly as we add vectors to the training set. Remember that we have some choice in picking the function µ: in previous experiments we have found that choosing the maximum 1 norm of the rows of V lead to values of µ around 10, and also in this case we expect the samples of a well-clusterable dataset to be constant. Also, µ is bounded by the Frobenius norm of V. In case the matrix V can be clustered with high-enough accuracy by k-means, it has been showed that the Frobenius norm of the matrix is proportional to √ k. Given that EM is a more powerful extension of k-means, we can rely on similar observations too. Usually, the number of features d is much more than the number of components in the mixture, i.e. d k, so we expect d 2 to dominate the k 3.5 term in the cost needed to estimate the mixing weights. This makes the runtime of a single iteration proportional to: As we said, the quantum running time saves the factor that depends on the number of samples and introduces a number of other parameters. Using our experimental we can see that when the number of samples is large enough one can expect the quantum running time to be faster than the classical one. Note as well that one can expect to save some more factors from the quantum running time with a more careful analysis. Experiments. In the algorithm, we need to set the parameters δ µ and δ θ to be small enough such that the likelihood is perturbed less than τ /4. We have reasons to believe that on well-clusterable data, the value of these parameters will be large enough, such as not to impact dramatically the runtime. A quantum version of k-means algorithm has already been simulated on real data under similar assumptions . There, the authors analyzed on the MNIST dataset the performances of q-means, the δ-resistant version of the classical k-means algorithm. The experiment concluded that, for datasets that are expected to be clustered nicely by this kind of clustering algorithms, the value of the parameters δ µ, δ θ did not decrease by increasing the number of samples nor the number of features. We expect similar behaviour in the EM case, namely that for large datasets the impact on the runtime of the errors (δ µ, δ θ) does not cancel out the exponential gain in the dependence on the number of samples. For instance, in all the experiments of q-means on the MNIST dataset the value of δ µ (which in their case was called just δ) has been between 0.2 and 0.5. The value of τ is usually (for instance in scikit-learn ) chosen to be 10 −3. The value of η has always been below 11. We also analyzed some other real-world dataset, which can be fitted well with the EM algorithm (; APPML; Voxforge.org) to perform speaker recognition: the task of recognizing a speaker from a voice sample, having access to a training set of recorded voices of all the possible speakers. Details of the measurements are reported in the Supplementary Material section, here we report only the in Table 1. After this, we also experimented the impact of errors on the mixing weights in the accuracy of a ML estimate of a GMM by perturbing the trained model, by adding some random noise. With a value of δ θ = 0.035, δ µ = 0.5 we correctly classified 98.2% utterances. Table 1: We estimate some of the parameters of the VoxForge (Voxforge.org) dataset. The averages for the matrix V are taken over 34 samples, while for Σ is over 170 samples. The accuracy reported in the experiments is measured on 170 samples in the test set, after the threshold on the eigenvalues of Σ. Each model is the of the best of 3 different initializations of the EM algorithm. The first and the second column are the maximum singular values of all the covariance matrices, and the absolute value of the log-determinant. The column κ * (Σ) consist in the thresholded condition number for the covariance matrices. In , the experimental suggest that the influence of the extra parameters in the quantum running time (condition thresholds, errors, etc.) is moderate. This allows us to be optimistic that, when quantum computers with quantum access to data become a reality, our algorithm (and improved versions that reduce even more the complexity with respect to these extra parameters) could be useful in analyzing large datasets. 6 SUPPLEMENTARY MATERIAL Here we report useful Theorems, Lemmas, and Claims that we use in the main text. Theorem 6.1 (Determinant estimation ). Let M ∈ R d×d be a positive definite matrix with eigenvalues in the interval (σ min, 1). Then for all δ ∈ and > 0 there is a classical algorithm that outputs log(det(M)) such that |log(det(M)) − log(det(M))| ≤ 2 | log(det(M))| with probability at least 1 − δ in time Require: Dataset V, tolerance τ > 0. Ensure: A GMM γ t = (θ t, µ t, Σ t) that maximizes locally the likelihood (γ; V) up to tolerance τ. 1: Select γ 0 = (θ 0, µ 0, Σ 0) using classical initialization strategies described in Subsection 6.3. 2: t = 0 3: repeat Expectation ∀i, j, calculate the responsibilities as: 5: Maximization Update the parameters of the model as: 6: t=t+1 7: until 8: 9: Return Proof. We need to find the K such that for all x, y ∈ R d, we have that σ j (y) − σ j (x) ≤ K y − x. Observing that σ j is differentiable and that if we apply Cauchy-Schwarz to the statement of the Mean-Value-Theorem we derive that ∀x, y ∈ U, ∃c such that f (x) − f (y) ≤ ∇f (c) F x − y. So to show Lipschitz continuity it is enough to select K ≤ ∇σ j * The partial derivatives In our case we can deduce that: Claim 6.4. (b) Let θ be the angle between vectors x, y, and assume that θ < π/2. Then, We will also use Claim 4.5 from . Claim 6.5. Let b be the error we commit in estimating |c such that |c − |c < b, and a the error we commit in the estimating the norms, Definition 2 (Exponential Family). A probability density function or probability mass function p(v|ν), where V ⊆ R, ν ∈ R p is said to be in the exponential family if can be written as: where: • ν ∈ R p is called the canonical or natural parameter of the family, • o(ν) is a function of ν (which often is just the identity function), • T (v) is the vector of sufficient statistics: a function that holds all the information the data v holds with respect to the unknown parameters, • A(ν) is the cumulant generating function, or log-partition function, which acts as a normalization factor, • h(v) > 0 is the base measure which is a non-informative prior and de-facto is scaling constant. To prove our , we are going to use the quantum procedures listed hereafter. Theorem 6.6 (Amplitude estimation and amplification ). If there is unitary operator U such that U |0 l = |φ = sin(θ) |x, 0 + cos(θ) |G, 0 ⊥ then sin 2 (θ) can be estimated to multiplicative error η in time η sin(θ) ) and |x can be generated in expected time O(We also need some state preparation procedures. These subroutines are needed for encoding vectors in v i ∈ R d into quantum states |v i . An efficient state preparation procedure is provided by the QRAM data structures. We stress the fact that our continues to hold, no matter how the efficient quantum loading of the data is provided. For instance, the data can be accessed through a QRAM, through a block encoding, or when the data can be produced by quantum circuits. Theorem 6.7 (QRAM data structure (a) ). Let V ∈ R n×d, there is a data structure to store the rows of V such that, 1. The time to insert, update or delete a single entry v ij is O(log 2 (n)). In our algorithm we will also use subroutines for quantum linear algebra. For a symmetric matrix M ∈ R d×d with spectral norm M = 1 stored in the QRAM, the running time of these algorithms depends linearly on the condition number κ(M) of the matrix, that can be replaced by κ τ (M), a condition threshold where we keep only the singular values bigger than τ, and the parameter µ(M), a matrix dependent parameter defined as et al., 2018; Gilyén et al., 2018b). Theorem 6.8 (Quantum linear algebra (; Gilyén et al., 2018b) ). Let M ∈ R d×d such that M 2 = 1 and x ∈ R d. Let, δ > 0. If M is stored in appropriate QRAM data structures and the time to prepare |x is T x, then there exist quantum algorithms that with probability at least 1 − 1/poly(d) return a state |z such that Theorem 6.9 (Quantum linear algebra for matrix products ). Let M 1, M 2 ∈ R d×d such that M 1 = M 2 = 1 and x ∈ R d, and a vector x ∈ R d stored in QRAM. Let > 0. Then there exist quantum algorithms that with probability at least 1 − 1/poly(d) returns a state |z such that |z − |M x ≤ in time O((κ(M)(µ(M 1)T M1 + µ(M 2)T M2 )) log(1/)), where T M1, T M2 is the time needed to index the rows of M 1 and M 2. The linear algebra procedures above can also be applied to any rectangular matrix V ∈ R N ×d by considering instead The final component needed for the q-means algorithm is a linear time algorithm for vector state tomography that will be used to recover classical information from the quantum states corresponding to the new centroids in each step. Given a unitary U that produces a quantum state |x, by calling O(d log d/ 2) times U, the tomography algorithm is able to reconstruct a vector x that approximates |x such that | x − |x ≤. Theorem 6.10 (Vector state tomography ). Given access to unitary U such that U |0 = |x and its controlled version in time T (U), there is a tomography algorithm with time complexity O(T (U) ) that produces unit vector x ∈ R d such that x − x 2 ≤ with probability at least (1 − 1/poly(d)). Lemma 6.11 (Distance / Inner Products Estimation (; ; a) ). Assume for a data matrix V ∈ R N ×d and a centroid matrix C ∈ R k×d that the following unitaries |i |0 → |i |v i, and |j |0 → |j |c j can be performed in time T and the norms of the vectors are known. For any ∆ > 0 and > 0, there exists a quantum algorithm that computes | with probability at least 1 − 2∆ in time O vi cj T log(1/∆). Unlike k-means clustering, choosing a good set of initial parameters for a mixture of Gaussian is by no means trivial, and in multivariate context is known that the solution is problem-dependent. There are plenty of proposed techniques, and here we describe a few of them. Fortunately, these initialization strategies can be directly translated into quantum subroutines without impacting the overall running time of the quantum algorithm. The simplest technique is called random EM, and consists in selecting initial points at random from the dataset as centroids, and sample the dataset to estimate the covariance matrix of the data. Then these estimates are used as the starting configuration of the model, and we may repeat the random sampling until we get satisfactory . A more standard technique borrows directly the initialization strategy of k-means++ proposed in , and extends it to make an initial guess for the covariance matrices and the mixing weights. The initial guess for the centroids is selected by sampling from a suitable, easy to calculate distribution. This heuristic works as following: Let c 0 be a randomly selected point of the dataset, as first centroid. The other k −1 centroids are selected by selecting a vector v i with probability proportional to d 2 (v i, µ l(vi) ), where µ l(vi) is the previously selected centroid that is the closest to v i in 2 distance. These centroids are then used as initial centroids for a round of k-means algorithm to obtain µ 0 1 · · · µ 0 j. Then, the covariance matrices can be initialized as Σ T, where C j is the set of samples in the training set that have been assigned to the cluster j in the previous round of k-means. The mixing weights are estimated as C j /n. Eventually Σ 0 j is regularized to be a PSD matrix. There are other possible choices for parameter initialization in EM, for instance, based on Hierarchical Agglomerative Clustering (HAC) and the CEM algorithm. In CEM we run one step of EM, but with a so-called classification step between E and M. The classification step consists in a hard-clustering after computing the initial conditional probabilities (in the E step). The M step then calculates the initial guess of the parameters . In the small EM initialization method we run EM with a different choice of initial parameters using some of the previous strategies. The difference here is that we repeat the EM algorithm for a few numbers of iterations, and we keep iterating from the choice of parameters that returned the best partial . For an overview and comparison of different initialization techniques, we refer to (Blömer & ;). Quantum initialization strategies For the initialization of γ 0 in the quantum algorithm we can use the same initialization strategies as in classical machine learning. For instance, we can use the classical random EM initialization strategy for QEM. A quantum initialization strategy can also be given using the k-means++ initializion strategy from , which returns k initial guesses for the centroids c, where is the average squared distance between two points of the dataset, and is the tolerance in the distance estimation. From there, we can perform a full round of q-means algorithm and get an estimate for µ 0 1 · · · µ 0 k. With q-means and the new centroids store in the QRAM we can create the state Where l(v i) is the label of the closest centroid to the i-th point. By sampling S ∈ O(d) points from this state we get two things. First, from the frequency f j of the second register we can have an guess of θ 0 j ← |C j |/n ∼ f j /S. Then, from the first register we can estimate Σ Sampling O(d) points and creating the state in Equation takes time O(dkη) by Theorem 6.11 and the minimum finding procedure described in . Techniques illustrated in can also be used to quantize the CEM algorithm which needs a hardclustering step. Among the different possible approaches, the random and the small EM greatly benefit from a faster algorithm, as we can spend more time exploring the space of the parameters by starting from different initial seeds, and thus avoid local minima of the likelihood. What we presented in the previous section is the most general model of GMM. For simple datasets, it is common to assume some restrictions on the covariance matrices of the mixtures. The translation into a quantum version of the model should be straightforward. We distinguish between these cases: 1. Soft k-means. This algorithm is often presented as a generalization of k-means, but it can actually be seen as special case of EM for GMM -albeit with a different assignment rule. In soft k-means, the assignment function is replaced by a softmax function with stiffness parameter β. This β represents the covariance of the clusters. It is assumed to be equal for all the clusters, and for all dimensions of the feature space. Gaussian Mixtures with constant covariance matrix (i.e. Σ j = βI for β ∈ R) can be interpreted as a kind of soft or fuzzy version of k-means clustering. The probability of a point in the feature space being assigned to a certain cluster j is: where β > 0 is the stiffness parameter. 2. Spherical. In this model, each component has its own covariance matrix, but the variance is uniform in all the directions, thus reducing the covariance matrix to a multiple of the identity matrix (i.e. Σ j = σ 2 j I for σ j ∈ R). 3. Diagonal. As the name suggests, in this special case the covariance matrix of the distributions is a diagonal matrix, but different Gaussians might have different diagonal covariance matrices. 4. Tied. In this model, the Gaussians share the same covariance matrix, without having further restriction on the Gaussian. 5. Full. This is the most general case, where each of the components of the mixture have a different, SDP, covariance matrix. Lemma 6.12 (Determinant evaluation). There is an algorithm that, given as input a matrix Σ and a parameter 0 < δ < 1, outputs an estimate log(det(Σ)) such that |log(det(Σ)) − log(det(Σ))| ≤ with probability 1 − δ in time: Proof. In order to apply Theorem 6.1, we need to be sure that all the eigenvalues lie in (σ min, 1). In order to satisfy this condition, we can scale the matrix by a constant factor c, such that Σ = Σ/c. In this way, log det(Σ) = log. This will allow us to recover the value of log det(Σ) by using Theorem 6.1. We apply the Theorem with precision = 1/4 to get an estimate γ such that 2. Then, to have an estimate with absolute error, we apply Theorem 6.1 with precision = 4γ. This gives us an estimate for log(det(Σ)) with error 2 log(det(Σ)) ≤ in time: Lemma 6.13 (Error in the responsibilities of the exponential family). Let v i ∈ R n be a vector, and let {p(v i |ν j)} k j=1 be a set of k probability distributions in the exponential family, defined as p(Then, if we have estimates for each exponent with error, then we can compute each r ij such that |r ij −r ij | ≤ √ 2k for j ∈ [k]. Proof. The proof follows from rewriting the responsibility of Equation as: In this form, it is clear that the responsibilities can be seen a softmax function, and we can use Theorem 6.3 to bound the error in computing this value. Let T i ∈ R k be the vector of the exponent, that is In an analogous way we define T i the vector where each component is the estimate with error. The error in the responsibility is defined Because the function σ j is Lipschitz continuous, as we proved in Theorem 6.3 with a Lipschitz constant K ≤ √ 2, we have that, |σ j (Lemma 6.14 (Quantum Gaussian Evaluation). Suppose we have stored in the QRAM a matrix V ∈ R n×d, the centroid µ ∈ R d and the covariance matrix Σ ∈ R d×d of a multivariate Gaussian distribution φ(v|µ, Σ), as well as an estimate for log(det(Σ)). Then for 1 > 0, there exists a quantum algorithm that with probability 1 − γ performs the mapping, • U G, 1: |i |0 → |i |s i such that |s i − s i | < 1, where ) is the exponent for the Gaussian probability density function. The running time of the algorithm is, Proof. We use quantum linear algebra and inner product estimation to estimate the quadratic form µ and separately approximate each term in the sum to error 1 /4. We describe the procedure to estimate Recall that (through Lemma 3.1) we also have an estimate of the log-determinant to error 1. Thus we obtain an approximation for − + log(det(Σ))) within error 2 1. We have the upper bound, for Σ stored in a QRAM data structure. Further observing that u ≤ √ η and v i ≤ √ η, the running time for this computation is O Lemma 6.15 (Calculating responsibilities). Suppose we have quantum access to a GMM with parameters γ t = (θ t, µ t, Σ t). There are quantum algorithms that can: 1. Perform the mapping |i |j |0 → |i |j |r ij such that |r ij − r ij | ≤ 1 with probability 1 − γ in time: ij with high probability in time: Proof. For the first statement, let's recall the definition of responsibility:. With the aid of U G, 1 of Lemma 3.2 we can estimate log(φ(v i |µ j, Σ j)) for all j up to additive error 1, and then using the current estimate of θ t, we can calculate the responsibilities create the state, The estimate r ij is computed by evaluating a weighted softmax function with arguments log(φ( The estimates log(φ(v i |µ j, Σ j) are then uncomputed. The runtime of the procedure is given by calling k times Lemma 3.2 for Gaussian estimation (the arithmetic operations to calculate the responsibilities are absorbed). Let us analyze the error in the estimation of r ij. The responsibility r ij is a softmax function with arguments log(φ(v i |µ j, Σ j)) that are computed upto error 1 using Lemma 3.2. As the softmax function has a Lipschitz constant K ≤ √ 2 by Lemma 6.13, we choose precision for Lemma 3.2 to be 1 / √ 2k to get the guarantee |r ij − r ij | ≤ 1. Thus, the total cost of this step is T R1, 1 = k 1.5 T G, 1. Now let's see how to encode this information in the amplitudes, as stated in the second claim of the Lemma. We estimate the responsibilities r ij to some precision and perform a controlled rotation on an ancillary qubit to obtain, We then undo the circuit on the second register and perform amplitude amplification on the rightmost auxiliary qubit being |0 to get |R j:= Let us analyze the precision required to prepare |R j such that |R j − |R j ≤ 1. As we have estimates |r ij − r ij | < for all i, j, the 2 -norm error n. Applying Claim 6.4, the error for the normalized vector |R j can be bounded as Rj. By the Cauchy-Schwarz inequality we have that. We can use this to obtain a bound, using the dataset assumptions in section 2. If we choose such that Lemma 6.16 (Computing θ t+1). We assume quantum access to a GMM with parameters γ t and let δ θ > 0 be a precision parameter. There exists an algorithm that estimates θ t+1 ∈ R k such that θ t+1 − θ t+1 ≤ δ θ in time T, and compute a state |µ j t+1 along with an estimate for the norm V T R t j = µ j t+1 with error norm. The last step of the algorithm consists in estimating the unit vector |µ j t+1 with precision tom using tomography. Considering that the tomography depends on d, which we expect to be bigger than the precision required by the norm estimation, we can assume that the runtime of the norm estimation is absorbed. Thus, we obtain: O k Let's now analyze the total error in the estimation of the new centroids, which we want to be δ µ. For this purpose, we use Claim 6.5, and choose parameters such that 2 √ η(tom + norm) = δ µ. Since the error 3 for quantum linear algebra appears as a logarithmic factor in the running time, we can choose 3 tom without affecting the runtime. Let µ be the classical unit vector obtained after quantum tomography, and |µ be the state produced by the quantum linear algebra procedure starting with an approximation of |R t j. Using the triangle inequality we have |µ − µ < µ − |µ + |µ − |µ < tom + 1 < δ µ /2 √ η. The errors for the norm estimation procedure can be bounded We therefore choose parameters Since the amplitude estimation step we use for estimating the norms does not depends on d, which is expected to dominate the other parameters, we omit the amplitude estimation step. Substituting for T R2,δµ, we have the more concise expression for the running time of: Lemma 6.18 (Computing Σ t+1 j). We assume we have quantum access to a GMM with parameters γ t. We also have computed estimates µ j t+1 of all centroids such that µ j t+1 − µ t+1 j ≤ δ µ for precision parameter δ µ > 0. Then, there exists a quantum algorithm that outputs estimates for the new covariance matrices {Σ with high probability, in time, Proof. It is simple to check, that the update rule of the covariance matrix during the maximization step can be reduced to (, Exercise 11.2): First, note that we can use the estimates of the centroids to compute µ t+1 j (µ t+1 j) T with error δ µ µ ≤ δ µ √ η in the update rule for the Σ j. This follows from the fact that µ = µ + e where e is a vector of norm δ µ. Therefore does not depend on d, we consider it smaller than the runtime for performing tomography. Thus, the runtime for this operation is: Let's analyze the error of this procedure. We want a matrix Σ j that is √ ηδ µ -close to the correct one: Again, the error due to matrix multiplication can be taken as small as necessary, since is inside a logarithm. From Claim 6.5, we just need to fix the error of tomography and norm estimation such that η(unit + norms) < √ ηδ µ where we have used η as an upper bound on Σ j F. For the unit vectors, we require where |Σ j is the error due to tomography and |Σ j is the error due to Lemma 3.3. For this inequality to be true, we choose tom = 1 < δ µ /4 √ η. The same argument applies to estimating the norm Σ j with relative error: √ η (where here is the error of the amplitude estimation step used in Theorem 6.8 and 1 is the error in calling Lemma 3.3. Again, we choose . This can be derived from the fact that κ(A ⊗ B) = κ(A)κ(B), κ(AB) ≤ κ(A)κ(B), and Since the tomography is more costly than the amplitude estimation step, we can disregard the runtime for the norm estimation step. As this operation is repeated k times for the k different covariance matrices, the total runtime of the whole algorithm is given by O Let us also note that for each of new computed covariance matrices, we use Lemma 3.1 to compute an estimate for their log-determinant and this time can be absorbed in the time T Σ. Lemma 6.19 (Quantum estimation of likelihood). We assume we have quantum access to a GMM with parameters γ t. For τ > 0, there exists a quantum algorithm that estimates E[p(v i ; γ t)] with absolute error τ in time Proof. We obtain the likelihood from the ability to compute the value of a Gaussian distribution and quantum arithmetic. Using the mapping of Lemma 3.2 with precision 1, we can compute φ(v i |µ j, Σ j) for all the Gaussians, that is |i k−1 j=0 |j |p(v i |j; γ j). Then, by knowing θ, and by using quantum arithmetic we can compute in a register the mixture of Gaussian's: p(v i ; γ) = j∈[k] θ j p(v i |j; γ). We now drop the notation for the model γ and write p(v i) instead of p(v i ; γ). Doing the previous calculations quantumly leads to the creation of the state |i |p(v i). We perform the mapping |i |p(v i) → |i p(v i)| |0 + 1 − p(v i) |1 and estimate p(|0) E[p(v i)] with amplitude estimation on the ancilla qubit. To get a τ -estimate of p we need to decide the precision parameter we use for estimating p(v i |j; γ) and the precision required by amplitude estimation. Let p be the 1 -error introduced by using Lemma 3.2 and p the error introduced by amplitude estimation. Using triangle inequality we set p − p < p − p + p − p < τ. To have |p − p| < τ, we should set 1 such that |p − p| < τ /4, and we set the error in amplitude estimation and in the estimation of the probabilities to be τ /2. The runtime of this procedure is therefore: recognition community to extract the so-called Mel Frequency Cepstrum Coefficients (MFCCs) features . We selected d = 40 features for each speaker. Then, each speaker is modeled with a mixture of 16 different Gaussians. The test set consists of other 5 unseen utterances of the same 34 speakers (i.e. the training set and the test set have the same size). The task is to correctly label the unseen utterances with the name of the correct speaker. This is done by testing each of the GMM fitted during the training with the new voice sample, and selecting the model with the highest likelihood. Due to the differences in the speakers' audio data, the different dataset V 1... V 34 are made of a variable number of points which ranges from n = 2000 to 4000. In the vanilla configuration without thresholding, 169 among 170 utterances (5 utterances for all 34 speakers) were correctly labeled by EM with ML estimate, while all the elements in the test set were correctly recognized using the Bayesian (MAP) framework. During the training, we measured all the values of κ(Σ), κ(V), µ(V), µ(Σ). For almost all GMM fitted (choosing a diagonal covariance matrix), there is at least a Σ j (among the 16 used to model a single speaker) with bad condition number (i.e up to 2500 circa). As in we took a threshold on the matrix by discarding singular values smaller than a certain value m. Practically, we discarded any singular value smaller than 2 × 10 −2. Thresholding the covariance matrices did not impact the accuracy (the generalization error) significantly. In the MAP estimate, only one element is not correctly classified, while this number goes up to two in the case of ML estimates. The are in Table 1. To check the resistence to noise, we perturbed each of the GMM γ t, once fitted. Then, we measured variations of the accuracy on the test set. For each model, the perturbation consists of three things. First we add to θ a uniform vector (with randomly selected negative or positive entries) of 2 norm of δ θ = 0.035. Then we perturb each centroid µ j with a vector of norm smaller than δ µ = 0.5. In this error vector the sign of each noise component is chosen randomly, and the magnitude is sampled uniformly in the interval (0,). Then, we perturbed also the diagonal matrices Σ j with a vector of norm smaller than δ µ √ η, where η = 10. As we are using a diagonal GMM, this reduces to perturbing each singular value by some random noise upper bounded by ±δ µ √ η/ √ d, making sure that each of the singular value stays positive, as covariance matrices are SPD. Last, the matrices are thresholded. Since the representation of the model used by our software stores the Cholesky's decomposition of the inverse, we worked with that representation. Notably, thresholding the Σ j help to mitigate the error of noise and regularize the model. With these parameters, we were able to correctly label 167 utterances over 170. We leave for the future further experiments with bigger and different types of datasets or where the noise is also added during the training process. We used scikit-learn to run all the experiments. Maximum Likelihood is not the only way to estimate the parameters of a model, and in certain cases might not even be the best one. For instance, in high-dimensional spaces, it is pretty common for ML estimates to overfit. Moreover, it is often the case that we have prior information on the distribution of the parameters, and we would like our models to take this information into account. These issues are often addressed using a Bayesian approach, i.e. by using a so-called Maximum A Posteriori estimate (MAP) of a model (, Section 14.4.2.8). MAP estimates work by assuming the existence of a prior distribution over the parameters γ. The posterior distribution we use as objective function to maximize comes from the Bayes' rule applied on the likelihood, which gives the posterior as a product of the likelihood and the prior, normalized by the evidence. More simply, we use the Bayes' rule on the likelihood function, as p(γ; V) = p(V ;γ)p(γ) p(V). This allows us to treat the model γ as a random variable, and derive from the ML estimate a MAP estimate: Among the advantages of a MAP estimate over ML is that it avoids overfitting by having a kind of regularization effect on the model (, Section 6.5). Another feature consists in injecting into a maximum likelihood model some external information, perhaps from domain experts. This advantage comes at the cost of requiring "good" prior information on the problem, which might be non-trivial. In terms of labelling, a MAP estimates correspond to a hard

We construct a theoretical framework for weakly supervised disentanglement and conducted lots of experiments to back up the theory.