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