qiskit.ignis.verification.TomographyFitter¶

class
TomographyFitter
(result, circuits, meas_basis='Pauli', prep_basis='Pauli')[소스]¶ Base maximumlikelihood estimate tomography fitter class
Initialize tomography fitter with experimental data.
 매개변수
result (
Union
[Result
,List
[Result
]]) – a Qiskit Result object obtained from executing tomography circuits.circuits (
Union
[List
[QuantumCircuit
],List
[str
]]) – a list of circuits or circuit names to extract count information from the result object.meas_basis (
Union
[TomographyBasis
,str
]) – (default: ‘Pauli’) A function to return measurement operators corresponding to measurement outcomes. See Additional Information.prep_basis (
Union
[TomographyBasis
,str
]) – (default: ‘Pauli’) A function to return preparation operators. See Additional Information

__init__
(result, circuits, meas_basis='Pauli', prep_basis='Pauli')[소스]¶ Initialize tomography fitter with experimental data.
 매개변수
result (
Union
[Result
,List
[Result
]]) – a Qiskit Result object obtained from executing tomography circuits.circuits (
Union
[List
[QuantumCircuit
],List
[str
]]) – a list of circuits or circuit names to extract count information from the result object.meas_basis (
Union
[TomographyBasis
,str
]) – (default: ‘Pauli’) A function to return measurement operators corresponding to measurement outcomes. See Additional Information.prep_basis (
Union
[TomographyBasis
,str
]) – (default: ‘Pauli’) A function to return preparation operators. See Additional Information
Methods
__init__
(result, circuits[, meas_basis, …])Initialize tomography fitter with experimental data.
add_data
(results, circuits)Add tomography data from a Qiskit Result object.
fit
([method, standard_weights, beta, psd, …])Reconstruct a quantum state using CVXPY convex optimization.
set_measure_basis
(basis)Set the measurement basis
set_preparation_basis
(basis)Set the preparation basis function
Attributes
Return tomography data
Return the tomography measurement basis.
Return the tomography preparation basis.

add_data
(results, circuits)[소스]¶ Add tomography data from a Qiskit Result object.
 매개변수
results (
List
[Result
]) – The results obtained from executing tomography circuits.circuits (
List
[Union
[QuantumCircuit
,str
]]) – circuits or circuit names to extract count information from the result object.
 예외
QiskitError – In case some of the tomography data is not found in the results

property
data
¶ Return tomography data

fit
(method='auto', standard_weights=True, beta=0.5, psd=True, trace=None, trace_preserving=False, **kwargs)[소스]¶ Reconstruct a quantum state using CVXPY convex optimization.
Fitter method
The
'cvx'
fitter method uses the CVXPY convex optimization package with a SDP solver. The'lstsq'
method uses leastsquares fitting. The'auto'
method will use'cvx'
if the both the CVXPY and a suitable SDP solver packages are found on the system, otherwise it will default to'lstsq'
.Objective function
This fitter solves the constrained leastsquares minimization: minimize: \(a \cdot x  b _2\)
subject to:
\(x \succeq 0\)
\(\text{trace}(x) = 1\)
where:
a is the matrix of measurement operators \(a[i] = \text{vec}(M_i).H\)
b is the vector of expectation value data for each projector \(b[i] \sim \text{Tr}[M_i.H \cdot x] = (a \cdot x)[i]\)
x is the vectorized density matrix to be fitted
PSD constraint
The PSD keyword constrains the fitted matrix to be postivesemidefinite. For the
lstsq
fitter method the fitted matrix is rescaled using the method proposed in Reference [1]. For thecvx
fitter method the convex constraint makes the optimization problem a SDP. If PSD=False the fitted matrix will still be constrained to be Hermitian, but not PSD. In this case the optimization problem becomes a SOCP.Trace constraint
The trace keyword constrains the trace of the fitted matrix. If trace=None there will be no trace constraint on the fitted matrix. This constraint should not be used for process tomography and the trace preserving constraint should be used instead.
CVXPY Solvers:
Various solvers can be called in CVXPY using the solver keyword argument. If
psd=True
an SDP solver is required other an SOCP solver is required. See the CVXPY documentation for more information on solvers. Note that the default SDP solver (‘SCS’) distributed with CVXPY will not be used for the'auto'
method due its reduced accuracy compared to other solvers. When using the'cvx'
method we strongly recommend installing one of the other supported SDP solvers.References:
 [1] J Smolin, JM Gambetta, G Smith, Phys. Rev. Lett. 108, 070502
(2012). Open access: arXiv:1106.5458 [quantph].
 매개변수
method (
str
) – The fitter method ‘auto’, ‘cvx’ or ‘lstsq’.standard_weights (
bool
) – (default: True) Apply weights to tomography data based on count probabilitybeta (
float
) – hedging parameter for converting counts to probabilitiespsd (
bool
) – Enforced the fitted matrix to be positive semidefinite.trace (
Optional
[int
]) – trace constraint for the fitted matrix.trace_preserving (
bool
) – Enforce the fitted matrix to be trace preserving when fitting a Choimatrix in quantum process tomography. Note this method does not apply for ‘lstsq’ fitter method.**kwargs – kwargs for fitter method.
 예외
QiskitError – In case the fitting method is unrecognized.
 반환 형식
array
 반환값
The fitted matrix rho that minimizes \(\text{basis_matrix} * \text{vec(rho)}  \text{data}_2\).

property
measure_basis
¶ Return the tomography measurement basis.

property
preparation_basis
¶ Return the tomography preparation basis.