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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]]))
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