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| """A cache for storing small matrices in multiple formats.""" | |
| from sympy.core.numbers import (I, Rational, pi) | |
| from sympy.core.power import Pow | |
| from sympy.functions.elementary.exponential import exp | |
| from sympy.matrices.dense import Matrix | |
| from sympy.physics.quantum.matrixutils import ( | |
| to_sympy, to_numpy, to_scipy_sparse | |
| ) | |
| class MatrixCache: | |
| """A cache for small matrices in different formats. | |
| This class takes small matrices in the standard ``sympy.Matrix`` format, | |
| and then converts these to both ``numpy.matrix`` and | |
| ``scipy.sparse.csr_matrix`` matrices. These matrices are then stored for | |
| future recovery. | |
| """ | |
| def __init__(self, dtype='complex'): | |
| self._cache = {} | |
| self.dtype = dtype | |
| def cache_matrix(self, name, m): | |
| """Cache a matrix by its name. | |
| Parameters | |
| ---------- | |
| name : str | |
| A descriptive name for the matrix, like "identity2". | |
| m : list of lists | |
| The raw matrix data as a SymPy Matrix. | |
| """ | |
| try: | |
| self._sympy_matrix(name, m) | |
| except ImportError: | |
| pass | |
| try: | |
| self._numpy_matrix(name, m) | |
| except ImportError: | |
| pass | |
| try: | |
| self._scipy_sparse_matrix(name, m) | |
| except ImportError: | |
| pass | |
| def get_matrix(self, name, format): | |
| """Get a cached matrix by name and format. | |
| Parameters | |
| ---------- | |
| name : str | |
| A descriptive name for the matrix, like "identity2". | |
| format : str | |
| The format desired ('sympy', 'numpy', 'scipy.sparse') | |
| """ | |
| m = self._cache.get((name, format)) | |
| if m is not None: | |
| return m | |
| raise NotImplementedError( | |
| 'Matrix with name %s and format %s is not available.' % | |
| (name, format) | |
| ) | |
| def _store_matrix(self, name, format, m): | |
| self._cache[(name, format)] = m | |
| def _sympy_matrix(self, name, m): | |
| self._store_matrix(name, 'sympy', to_sympy(m)) | |
| def _numpy_matrix(self, name, m): | |
| m = to_numpy(m, dtype=self.dtype) | |
| self._store_matrix(name, 'numpy', m) | |
| def _scipy_sparse_matrix(self, name, m): | |
| # TODO: explore different sparse formats. But sparse.kron will use | |
| # coo in most cases, so we use that here. | |
| m = to_scipy_sparse(m, dtype=self.dtype) | |
| self._store_matrix(name, 'scipy.sparse', m) | |
| sqrt2_inv = Pow(2, Rational(-1, 2), evaluate=False) | |
| # Save the common matrices that we will need | |
| matrix_cache = MatrixCache() | |
| matrix_cache.cache_matrix('eye2', Matrix([[1, 0], [0, 1]])) | |
| matrix_cache.cache_matrix('op11', Matrix([[0, 0], [0, 1]])) # |1><1| | |
| matrix_cache.cache_matrix('op00', Matrix([[1, 0], [0, 0]])) # |0><0| | |
| matrix_cache.cache_matrix('op10', Matrix([[0, 0], [1, 0]])) # |1><0| | |
| matrix_cache.cache_matrix('op01', Matrix([[0, 1], [0, 0]])) # |0><1| | |
| matrix_cache.cache_matrix('X', Matrix([[0, 1], [1, 0]])) | |
| matrix_cache.cache_matrix('Y', Matrix([[0, -I], [I, 0]])) | |
| matrix_cache.cache_matrix('Z', Matrix([[1, 0], [0, -1]])) | |
| matrix_cache.cache_matrix('S', Matrix([[1, 0], [0, I]])) | |
| matrix_cache.cache_matrix('T', Matrix([[1, 0], [0, exp(I*pi/4)]])) | |
| matrix_cache.cache_matrix('H', sqrt2_inv*Matrix([[1, 1], [1, -1]])) | |
| matrix_cache.cache_matrix('Hsqrt2', Matrix([[1, 1], [1, -1]])) | |
| matrix_cache.cache_matrix( | |
| 'SWAP', Matrix([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])) | |
| matrix_cache.cache_matrix('ZX', sqrt2_inv*Matrix([[1, 1], [1, -1]])) | |
| matrix_cache.cache_matrix('ZY', Matrix([[I, 0], [0, -I]])) | |