pygsti.modelmembers.operations.denseop
The DenseOperator class and supporting functionality.
Module Contents
Classes
Adds a numpy-array-mimicing interface onto an operation object. |
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TODO: update docstring |
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TODO: update docstring |
Functions
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Computes a finite-difference Jacobian for a LinearOperator object. |
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Checks the deriv_wrt_params method of a LinearOperator object. |
- pygsti.modelmembers.operations.denseop.finite_difference_deriv_wrt_params(operation, wrt_filter, eps=1e-07)
Computes a finite-difference Jacobian for a LinearOperator object.
The returned value is a matrix whose columns are the vectorized derivatives of the flattened operation matrix with respect to a single operation parameter, matching the format expected from the operation’s deriv_wrt_params method.
Parameters
- operationLinearOperator
The operation object to compute a Jacobian for.
- wrt_filterlist or numpy.ndarray
List of parameter indices to filter the result by (as though derivative is only taken with respect to these parameters).
- epsfloat, optional
The finite difference step to use.
Returns
- numpy.ndarray
An M by N matrix where M is the number of operation elements and N is the number of operation parameters.
- pygsti.modelmembers.operations.denseop.check_deriv_wrt_params(operation, deriv_to_check=None, wrt_filter=None, eps=1e-07)
Checks the deriv_wrt_params method of a LinearOperator object.
This routine is meant to be used as an aid in testing and debugging operation classes by comparing the finite-difference Jacobian that should be returned by operation.deriv_wrt_params with the one that actually is. A ValueError is raised if the two do not match.
Parameters
- operationLinearOperator
The operation object to test.
- deriv_to_checknumpy.ndarray or None, optional
If not None, the Jacobian to compare against the finite difference result. If None, operation.deriv_wrt_parms() is used. Setting this argument can be useful when the function is called within a LinearOperator class’s deriv_wrt_params() method itself as a part of testing.
- wrt_filterlist or numpy.ndarray
List of parameter indices to filter the result by (as though derivative is only taken with respect to these parameters).
- epsfloat, optional
The finite difference step to use.
Returns
None
- class pygsti.modelmembers.operations.denseop.DenseOperatorInterface
Bases:
objectAdds a numpy-array-mimicing interface onto an operation object.
- to_array()
Return the array used to identify this operation within its range of possible values.
For instance, if the operation is a unitary operation, this returns a unitary matrix regardless of the evolution type. The related
to_dense()method, in contrast, returns the dense representation of the operation, which varies by evolution type.Note: for efficiency, this doesn’t copy the underlying data, so the caller should copy this data before modifying it.
Returns
numpy.ndarray
- to_sparse(on_space='minimal')
Return the operation as a sparse matrix.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
Returns
scipy.sparse.csr_matrix
- class pygsti.modelmembers.operations.denseop.DenseOperator(mx, basis, evotype, state_space=None)
Bases:
DenseOperatorInterface,pygsti.modelmembers.operations.krausop.KrausOperatorInterface,pygsti.modelmembers.operations.linearop.LinearOperatorTODO: update docstring An operator that behaves like a dense super-operator matrix.
This class is the common base class for more specific dense operators.
Parameters
- mxnumpy.ndarray
The operation as a dense process matrix.
- basisBasis or {‘pp’,’gm’,’std’} or None
The basis used to construct the Hilbert-Schmidt space representation of this state as a super-operator. If None, certain functionality, such as access to Kraus operators, will be unavailable.
- evotypeEvotype or str
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Attributes
- basenumpy.ndarray
Direct access to the underlying process matrix data.
Initialize a new LinearOperator
- property kraus_operators
A list of this operation’s Kraus operators as numpy arrays.
- classmethod from_kraus_operators(kraus_operators, basis='pp', evotype='default', state_space=None)
Create an operation by specifying its Kraus operators.
Parameters
- kraus_operatorslist
A list of numpy arrays, each of which specifyies a Kraus operator.
- basisstr or Basis, optional
The basis in which the created operator’s superoperator representation is in.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
- to_dense(on_space='minimal')
Return the dense array used to represent this operation within its evolution type.
Note: for efficiency, this doesn’t copy the underlying data, so the caller should copy this data before modifying it.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
Returns
numpy.ndarray
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.
- class pygsti.modelmembers.operations.denseop.DenseUnitaryOperator(mx, basis, evotype, state_space)
Bases:
DenseOperatorInterface,pygsti.modelmembers.operations.krausop.KrausOperatorInterface,pygsti.modelmembers.operations.linearop.LinearOperatorTODO: update docstring An operator that behaves like a dense (unitary) operator matrix.
This class is the common base class for more specific dense operators.
Parameters
- mxnumpy.ndarray
The operation as a dense process matrix.
- basisBasis or {‘pp’,’gm’,’std’}, optional
The basis used to construct the Hilbert-Schmidt space representation of this state as a super-operator.
- evotypeEvotype or str
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Attributes
- basenumpy.ndarray
Direct access to the underlying process matrix data.
Initialize a new LinearOperator
- property kraus_operators
A list of this operation’s Kraus operators as numpy arrays.
- classmethod from_kraus_operators(kraus_operators, basis='pp', evotype='default', state_space=None)
Create an operation by specifying its Kraus operators.
Parameters
- kraus_operatorslist
A list of numpy arrays, each of which specifyies a Kraus operator.
- basisstr or Basis, optional
The basis in which the created operator’s superoperator representation is in.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
- classmethod quick_init(unitary_mx, superop_mx, basis, evotype, state_space)
- to_dense(on_space='minimal')
Return the dense array used to represent this operation within its evolution type.
Note: for efficiency, this doesn’t copy the underlying data, so the caller should copy this data before modifying it.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
Returns
numpy.ndarray
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.