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Question: Most research on lifelong learning applies to images or games, but not language. We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling. LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity. Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples. When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task. The show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model. Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound. The source code is available at https://github.com/jojotenya/LAMOL. The current dominant paradigm for machine learning is to run an algorithm on a given dataset to produce a trained model specifically for a particular purpose; this is isolated learning . In isolated learning, the model is unable to retain and accumulate the knowledge it has learned before. When a stream of tasks are joined to be trained sequentially, isolated learning faces catastrophic forgetting due to a non-stationary data distribution that biases the model (left figure of Figure 1). In contrast, lifelong learning is designed to address a stream of tasks by accumulating interconnected knowledge between learned tasks and retaining the performance of those tasks. A human easily achieves lifelong learning, but this is nontrivial for a machine; thus lifelong learning is a vital step toward artificial general intelligence. In this paper, we focus on lifelong language learning, where a machine achieves lifelong learning on a stream of natural language processing (NLP) tasks. To the best of our knowledge, lifelong language learning has been studied in only a few instances; for sentiment analysis (b;), conversational agents , word representation learning , sentence representation learning , text classification, and question answering (d'). However, in all previous work, the tasks in the stream are essentially the same task but in different domains. To achieve lifelong language learning on fundamentally different tasks, we propose LAMOL -LAnguage MOdeling for Lifelong language learning. It has been shown that many NLP tasks can be considered question answering (QA) . Therefore, we address multiple NLP tasks with a single model by training a language model (LM) that generates an answer based on the context and the question. Treating QA as language modeling is beneficial because the LM can be pre-trained on a large number of sentences without any labeling ; however, this does not directly solve the problem of LLL. If we train an LM on a stream of tasks, catastrophic forgetting still occurs. However, as an LM is intrinsically a text generator, we can use it to answer questions while generating pseudo-samples of Figure 1: Left: After learning Task 2, the learner has already forgetten how to solve Task 1. This is "catastrophic forgetting". Middle: The basic idea of the data-based LLL approach. A generator is learned to generate examples it has seen before. Using the generator, the learner also learns from examples from the previous task to prevent it from forgetting. Right: A language model that simultaneously takes on the roles of learner and generator. the previous task to be replayed later. LAMOL is inspired by the data-based approach for LLL in which a generator learns to generate samples in previous tasks (middle of Figure 1) . In contrast to previous approaches, LAMOL needs no extra generator (right of Figure 1). LAMOL is also similar to multitask training, but the model itself generates data from previous tasks instead of using real data. Our main contributions in this paper are: • We present LAMOL, a simple yet effective method for LLL. Our method has the advantages of no requirements in terms of extra memory or model capacity. We also do not need to know how many tasks to train in advance and can always train on additional tasks when needed. • Experimental show that our methods outperform baselines and other state-of-the-art methods by a considerable margin and approaches the multitasking upper bound within 2-3%. • Furthermore, we propose adding task-specific tokens during pseudo-sample generation to evenly split the generated samples among all previous tasks. This extension stabilizes LLL and is particularly useful when training on a large number of tasks. • We analyze how different amounts of pseudo-samples affect the final performance of LAMOL, considering both with and without the task-specific tokens. • We open-source our code to facilitate further LLL research. Lifelong learning research is based on regularization, architecture, or data. Here is a brief survey of works in these three categories. In this approach, a constraint, i.e., a regularization term, is added to minimize deviation from trained weights while updating the weights in a new task. Most regularization based methods estimate the importance of each parameter and add the importance as a constraint to the loss function. Elastic weight consolidation (EWC) calculates a Fisher information matrix to estimate the sensitivity of parameters as importance. Online EWC is a transformed version of EWC. Instead of tracking the importance of parameters for each task, online EWC simply accumulates the importance of the stream of tasks. Synaptic intelligence (SI) assigns importance to each parameter according to its contribution to the change in the total loss. Memory aware synapses (MAS) estimate importance via the gradients of the model outputs. In contrast to estimating the importance of weights, incremental moment matching (IMM) matches the moment of weights between different tasks. For this category, the main idea is to assign a dedicated capacity inside a model for each task. After completing a task, the weights are frozen and may not be changed thereafter. Some methods allow models to expand, whereas some fix the size but must allocate capacity for tasks at the beginning. Progressive neural networks utilize one column of the neural network per task. Once a new task is trained, progressive neural networks augment a new column of the neural network for the task while freezing the past trained columns. Columns that have been frozen are not allowed to change but are connected to the new column to transfer knowledge from old tasks. Towards Training Recurrent Neural Networks for Lifelong Learning unifies Gradient episodic memory and Net2Net (a). Using the curriculumbased setting, the model learns the tasks in easy-to-hard order. The model alleviates the forgetting problem by GEM method, and if it fails to learn the current task and has not been expanded yet, the model will expand to a larger model by the Net2Net approach. PathNet reuses subsets of a neural network to transfer knowledge between tasks. Unlike progressive neural networks, PathNet does not allow the model to expand. Instead, it builds a huge fixed-size model composed of a neural network and paths between different layers of the neural networks. While training a task, it selects the best combination of neural networks and paths for that particular task. Similar to progressive neural networks, selected parts are fixed to allow only inference and not training. Inspired by network pruning, PackNet prunes and re-trains the network iteratively to pack numerous tasks into a single huge model. This category has some drawbacks. When resources are limited, model expansion is prohibited. Also, some architecture-based methods require the number of tasks in advance to allocate the capacity for the tasks, which greatly reduces their practicality. This method restricts weights through the data distribution of old tasks. One data-based approach keeps a small amount of real samples from old tasks, and the other distills the knowledge from old data and imagines pseudo-data of old tasks later on. While training a new task, the data or pseudo-data is used to prevent weights from greatly deviating from the previous status. Gradient episodic memory (GEM) preserves a subset of real samples from previous tasks. Utilizing these real samples during optimization helps somewhat to constrain parameter gradients. Averaged-GEM (A-GEM) is a more efficient version of GEM which achieves the same or even better performance than the original GEM. Learning without forgetting minimizes the alteration of shared parameters by recording the outputs from old task modules on data from the new task before updating. and encode data from old tasks into a generative model system. The latter imitates the dual-memory system of the human brain, in that the model automatically decides which memory should be consolidated. Both methods replay pseudo-data of previous tasks using the generative model during training. d' investigates the performance of the episodic memory system on NLP problems. It distills the knowledge of previous tasks into episodic memory and replays it afterward. This work evaluates the method on two streams of tasks: question answering and text classification. A pre-trained LM can generate a coherent sequence of text given a context. Thus, we propose LAMOL, a method of training a single LM that learns not only to answer the question given the context but also to generate the context, the question, and the answer given a generation token. That is, in LAMOL, a model plays the role of both LM and QA model. Hence, answering questions and generating pseudo-old samples can both be done by a single model. During LLL, these pseudo-old samples are trained with new samples from new tasks to help mitigate catastrophic forgetting. Inspired by the protocol used by decaNLP , samples from the datasets we used are framed into a SQuAD-like scheme, which consists of context, question, and answer. Although the LM is simultaneously a QA model, the data format depends on the training objective. When training as a QA model, the LM learns to decode the answer after reading the context and question. On the other hand, when training as an LM, the LM learns to decode all three parts given a generation token. In addition to context, question, and answer, we add three special tokens: ANS Inserted between question and answer. As the context and question are known during inference, decoding starts after inputting ANS. EOS The last token of every example. Decoding stops when EOS is encountered. The first token during pseudo-sample generation. Decoding starts after inputting GEN. The data formats for QA and LM training are shown in Figure 2. Assume a stream of tasks {T 1, T 2, . . .}, where the number of tasks may be unknown. Directly training the LM on these tasks sequentially in catastrophic forgetting. Thus, before beginning training on a new task T i, i > 1, the model first generates pseudo samples T i by top-k sampling that represent the data distribution of previous tasks T 1,..., T i−1. Then, the LM trains on the mixture of T i and T i. To balance the ratio between |T i | and |T i |, the LM generates γ|T i | pseudo samples, where |T i | denotes the number of samples in task T i and γ is the sampling ratio. If the generated sample does not have exactly one ANS in it, then the sample is discarded. This happens in only 0.5%-1% of generated samples. During training, each sample is formatted into both the QA format and the LM format. Then, in the same optimization step, both formats are fed into the LM to minimize the QA loss L QA and LM loss L LM together. Overall, the loss is L = L QA + λL LM, where λ is the weight of the LM loss. Using the same GEN token for all tasks is problematic when training for many tasks because the portion of old tasks decreases exponentially in theory. For instance, if γ = 0.01, then the portion of the first task when training the second task is about 1%, but is only about 0.01% when training the third task. This issue is definitely harmful to LLL. To mitigate this, we can choose to replace the GEN token with a task-specific token for each task to inform the model to generate pseudo-samples belonging to the specific task. Under this setup, all previous tasks have the same share of the γ|T i | generated pseudo samples. That is, when beginning training for the i-th task T i, we generate We do not train on all datasets from both papers due to a lack of computational resources. For each task, there is a corresponding evaluation metric. Table 1 contains a summary of tasks, datasets, and metrics. Additional details are provided in Appendix A. Note that the score of any metric lies between 0 and 100%. All methods use the smallest pre-trained GPT-2 model 1 as the LM. Each task is trained for nine epochs; greedy decoding is applied during inference. • LAMOL In all experiments, k = 20 in top-k sampling and λ = 0.25 for weight of the LM loss are set. LAMOL γ GEN denotes LAMOL with a sampling ratio of γ, and the same GEN token is used for all tasks. If the task-specific tokens are used, GEN is replaced by TASK. • Keep real data Pseudo-samples are replaced by real samples from previous tasks. The quantity of real samples is equally split between previous tasks. This approach can be considered the upper bound of LAMOL. We denote it as LAMOL Table 4: Summary of averaged score on five tasks. The scores are reported as the averaged score over all tasks of the models after training on every task. The rightmost three columns -LAMOL with γ = 0.05 and γ = 0.2 of real samples from previous tasks and Multitasked -are upper bounds for comparison. Best performance in boldface. • Fine-tune The model is directly fine-tuned on the stream of tasks, one after another. • Multitask learning All tasks are trained simultaneously. Multitask learning is often seen as an upper bound of lifelong learning. In addition, it is also used to determine whether forgetting is caused by a lack of model capacity. • Regularization-based methods Online EWC and MAS ) are compared. They are chosen because they are more computationally efficient than SI and more memory efficient than IMM. Additionally, experiments such as show that MAS has better performance overall. • Gradient Episodic Memory (GEM) When training each task, we randomly sample data from previous task with the amount equivalent to 5% of the current task size into the memory. In each optimization step, the GEM approach retrieves all the data in the memory to calculate the gradients for the previous tasks. • Improved memory-based parameter adaptation (MBPA++) Sparse experience replay and local adaptation for LLL as proposed in d'. We also re-implement the paper and report better scores using different hyperparameters. To establish a reference on the capability of the GPT-2 model on every dataset, we trained the model on each dataset independently. The are shown in Table 2. We observe that the performance of the GPT-2 model is actually quite good, even beating the BERT-based model (d') on text classification datasets by a large margin. Thus, the GPT-2 model has the potential for superior LLL performance, as long as we can prevent catastrophic forgetting. For an initial understanding of the performance on all of the methods and the effect of task order, we first conducted a small-scale experiment on three small datasets: SST, QA-SRL, and WOZ. We trained all but the the multitasked method on all six permutations of the task order. The final score for each order was obtained by evaluating the model at the of the training process. The are shown in Table 3; we make several observations. Note that LAMOL with γ = 0 is not the same as Fine-tuned, as the LM loss is still optimized. • Fine-tuned, EWC, MAS, and LAMOL with γ = 0 show similar performance and are much worse than LAMOL with γ > 0. • LAMOL 0.2 GEN, our best performing method, is only 1.8 percent away from Multitasked, which implies almost no forgetting during LLL. • The order of the tasks is crucial to the performance. For instance, the WOZ score drops significantly after training other tasks. Thus, if WOZ is not the last task, the performance is usually noticeably worse. • When using LAMOL, the performance of old tasks maintains almost the same level throughout the training process. When the sampling ratio γ is increased, the performance also increases, especially when increased from 0 to 0.05. • When γ = 0, adding task-specific tokens harms performance, because the model must fit additional special tokens that are useless. Adding task-specific tokens is also not helpful if γ = 0.2. We believe that 0.2 is enough for three tasks; thus task-specific tokens are redundant. However, when γ = 0.05, task-specific tokens are beneficial because the tokens are needed to help retain a substantial presence of the first task when training the third task. • We see that a better LLL method usually has a smaller standard deviation, which implies that it is effected less by task order. Adding task-specific tokens also has a stabilizing effect. The complete forgetting progress is illustrated in Appendix B. Clearly, Fine-tuned, EWC, MAS, LAMOL 0 GEN, and LAMOL 0 TASK reveal similar patterns. However, the proposed LAMOL with γ > 0 displays the ability to retain its learned knowledge. In the case of WOZ → SRL → SST, the WOZ score even increases after training the third task using LAMOL with γ = 0.2. Here, we train the following five tasks sequentially: SQuAD, WikiSQL, SST, QA-SRL, and WOZ. Given the limited computing resources, we explore only one task order: from large to small tasks, according to the number of training samples. As shown in Table 4, LAMOL outperforms all baselines by a large margin and on average approaches within 2-3% of the multitasked upper bound. Also, as expected, the performance of LAMOL improves as the sampling ratio γ increases and task-specific tokens are used. There is also a gap between our method and the method of keeping real samples. As shown in the table, using real samples is much more sample-efficient, as 5% of real samples beats 20% of pseudo-samples. This may be due to the less-than-ideal quality of the pseudo-data. The longer the paragraphs are, the harder it is for the model to create high-quality samples. After observing the samples generated when using task-specific tokens, we discover some "chaos". That is, some examples generated by the model do not exactly correspond to the task-specific token. This implies that the task-specific tokens are sometimes too weak to constrain the model; thus their influence is overshadowed by other tokens. We believe that solving this problem will bring the performance when using task-specific tokens closer to using real samples; however, we leave this as future work. Figure 3 illustrates the test scores of each method on each task throughout the training. We clearly see that when using LAMOL, the model remembers nearly perfectly. We make several observations: • When training SQuAD, QA-SRL has not been trained yet, but the score of QA-SRL is already around 40. Also, when training QA-SRL, the SQuAD score revives if the model has forgotten SQuAD. These two facts imply that SQuAD and SRL are similar tasks, such that the model is capable of transferring knowledge from one to the other. • If forward transfer exists, replaying pseudo-data also retains the forward transfer. That is, the QA-SRL score does not drop after training on WikiSQL and SST when LAMOL is used but drops significantly for other methods. • The transferability between SQuAD and QA-SRL is expected. On the other hand, the transferability between WikiSQL and QA-SRL is quite surprising; the WikiSQL score improves considerably when training on QA-SRL for Fine-tuned and MAS after WikiSQL is forgotten during SST training. We compared the proposed method against the state-of-the-art MBPA++ proposed in d', both by citing their original numbers and also by reproducing their methods. We chose text classification as opposed to QA because we believe that LM has more of a disadvantage in text classification than in QA. We compared with LAMOL 0.2 TASK due to its good performance and stability. Following their paper and testing our model on the same four kinds of task orders, the are shown in Table 5. Our implementation in much higher scores than the original ones. However, the proposed LAMOL 0.2 TASK still outperforms our implementation of MBPA++. As the value of γ determines the performance of LLL, we conducted a medium-scale experiment to understand the influence of γ with and without task-specific tokens. In this experiment we used TASK is smaller than 1%, which shows that there is significant difference. Our implementation of MBPA++ is available at https://github.com/Daikon-Sun/EM-in-LLL. WikiSQL (blue color), SST (orange), QA-SRL (green), and WOZ (red), in that training order. The are shown in Figure 4. Unsurprisingly, the less generation done by the model, the more likely the vanishing distribution in Section 3 occurs: the model forgets how to generate previous tasks, as the ratio of previous tasks in the total dataset decreases exponentially over time. Models using task-specific tokens mitigate this somewhat, as demonstrated in the first subgraph where the performance of LAMOL 0.03 TASK is much better than that of LAMOL 0.03 In addition, the more samples the model generates, the better the overall performance of the model. However, this performance gain disappears when the sampling ratio γ is around 0.1 to 0.3. We propose LAMOL, a simple yet effective method for LLL based on language modeling. A single LM achieves LLL without additional model components and without keeping old examples. Moreover, any pre-trained LM can be used to leverage a large amount of unlabeled text to improve LLL. Finally, more tasks can be added whenever needed. Five tasks and their corresponding datasets from decaNLP : • Question Answering -Stanford Question Answering Dataset (SQuAD) : This dataset consists of context, questions, and answers. The context is paragraphs from English Wikipedia, and the answers are spans from its corresponding question paragraphs. For evaluation, we use the normalized F1 score (nF1), which strips out articles and punctuation as in. Test datasets in this task are hidden from the host so that users must upload models to their platform to generate the test ; due to this inconvenience and our many models, we elected to use the development set to test the metric. Note that we do not use the development set in the training process. The size of the training set is 87,599 while that of the development set is 10,570. • Semantic Parsing -WikiSQL : In this task, normal sentences are translated into SQL-structured SQL queries. WikiSQL provides logical forms along with natural language utterances. The exact match of the logical forms (lfEM) is used to evaluate the performance. The model outputs are required to be matched the SQL format. Otherwise, its won't get any score. The size of the training set is 56,355; that of the test set is 15,878. • Sentiment Analysis -Stanford Sentiment Treebank (SST, binary version) : This dataset consists of movie reviews with its answers, including positive and negative binary options. The exact match score is used as the metric. The size of the training set is 6,920; that of the test set is 1,821. • Semantic Role Labeling -QA-SRL: QA-SRL is a question answering form of the SRL task. The normalized F1 (nF1) score is used. The size of the training set is 6,414; that of the test set is 2,201. • Goal-Oriented Dialogue -English Wizard of Oz (WOZ) : WOZ is a restaurant reservation task that provides a predefined ontology of a series of information for helping an agent to make reservations for customers. To keep track of the dialogue state, turn-based dialogue state EM (dsEM), which requires the model outputs exactly follow the characters' conversation order, is used for judgment. The size of the training set is 2,536; that of the test set is 1,646. Four text classification tasks and five datasets from MBPA++ (dAutume et al. 2019): • News Classification -AGNews: News articles to be classified into 4 classes. • Sentiment Analysis -Yelp and Amazon: Customer reviews and ratings on Yelp and Amazon. Both datasets include 5 classes. • Wikipedia Article Classification -DBPedia: Articles and their corresponding categories on Wikipedia, including 14 classes. • Questions and Answers Categorization -Yahoo: Questions and answers on the Yahoo! platform, including 10 classes. The dataset collected by Table 6: Summary of averaged score on reversed five tasks. The scores are reported as the averaged score over all tasks of the models after training on every task. The rightmost three columns -LAMOL with γ = 0.05 and γ = 0.2 of real samples from previous tasks. Best performance in boldface. Normal Examples squad1 The United States has been accused of a wide ranging war in Afghanistan since 9 / 11. During the campaign, US forces in Afghanistan were involved in an extensive air campaign. At least 1,600 American servicemen and women were killed, while more than 1,600 civilians were injured. After the US-led invasion of Afghanistan on 12 / 11 / 2001, an estimated 10,000 American soldiers were killed in combat. What were the targets included in the conflict? ans Afghanistan squad1 In 1849, the French army was forced to withdraw, and the French were finally expelled, although it was not until late November that the French recaptured most of their territories. French troops then reached Egypt. On 21 January 1852 (the year after he left), in Cairo, they captured Tripoli, Benghazi, Benghazi, and the eastern part of Libya. After Gaddafi's return to office, he established the Gaddafi regime. On 13 February 1856, the Gaddafi family relocated to Egypt. On 13 May 1857, the army was forced to withdraw from Libya, and the army returned to Benghazi. On whom did Gaddafi's army return to Benghazi? ans Gaddafi's family wikisql the table has columns rank, athlete, sport, nation, gold, silver and key words max, min, count, sum, avg, =, >, <, op, select, where, and, col, sst it's a movie that's not worth a look.is this review negative or positive? ans negative sst it's an ugly movie about an old man who's been given power by a new god to bring about a new era.is this review negative or positive? ans negative srl in a statement on his official page, juan valderrama said that the president's nominees would be appointed by the president and confirmed by the national senate.who might be appointed? ans president srl the first two films, released on august 21, 1999, are the first in a line of films that were made by two different people, each of whom worked in tandem on the film.what was released? ans the first two films Examples of Chaos squad1 the table has columns rank (nation), gold, silver, bronze, total and key words max, min, count, sum, avg, =, >, <, op, select, where, and, col, table, caption, page, section, op, cond, question, agg, aggops, condops --which total has a gold smaller than 2, and has the lowest rank of bronze of 3?what is the translation from english to sql? ans select min total from table where gold < 2 and bronze = 3 squad1 In addition to the role played by the main characters, the main cast members are played by the same cast members who were in season one of the original series.is this review negative or positive? ans positive wikisql it's a shame that the story doesn't end with a good ending, but it certainly means the world to me.is this review negative or positive? ans positive wikisql on the same day, june 19, 2012, former heavyweight champion arthur lang defended his title against alexander green in the final of the world heavyweight championship.what did someone defend? ans his title sst at this point, he was only twenty -five years old at the time of his first appearance on the mcf tv series.who was something twenty -five years old? ans he sst on paper, the movie's two most fascinating characters are two peas in the same class, both of whom have made an appearance in this film.who did something feature? ans two most fascinating characters srl a popular seasonal film in austrian films and the most influential in the history of cinema.is this review negative or positive? ans positive srl it's not a bad film, it's just not as good as you've seen it before.is this review negative or positive? ans negative Table 7: Examples generated by LAMOL with task-specific tokens. Annotations squad1, wikisql, sst, srl correspond to each task-specific token of SQuAD, WikiSQL, SST, and QA-SRL, respectively. ans is the ANS token that separates the question from the answer. The upper frame shows the normal situation whereas the lower frame shows generated contents that are inconsistent with their task-specific token.

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Language modeling for lifelong language learning.

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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 present a new approach, SNIP, that is simple, versatile and interpretable; it prunes irrelevant connections for a given task at single-shot prior to training and is applicable to a variety of neural network models without modifications.