qiskit.aqua.algorithms.IterativeAmplitudeEstimation
class IterativeAmplitudeEstimation(epsilon, alpha, confint_method='beta', min_ratio=2, state_preparation=None, grover_operator=None, objective_qubits=None, post_processing=None, a_factory=None, q_factory=None, i_objective=None, initial_state=None, quantum_instance=None)
The Iterative Amplitude Estimation algorithm.
This class implements the Iterative Quantum Amplitude Estimation (IQAE) algorithm, proposed in [1]. The output of the algorithm is an estimate that, with at least probability , differs by epsilon to the target value, where both alpha and epsilon can be specified.
It differs from the original QAE algorithm proposed by Brassard [2] in that it does not rely on Quantum Phase Estimation, but is only based on Grover’s algorithm. IQAE iteratively applies carefully selected Grover iterations to find an estimate for the target amplitude.
References
[1]: Grinko, D., Gacon, J., Zoufal, C., & Woerner, S. (2019).
Iterative Quantum Amplitude Estimation. arXiv:1912.05559.
[2]: Brassard, G., Hoyer, P., Mosca, M., & Tapp, A. (2000).
Quantum Amplitude Amplification and Estimation. arXiv:quant-ph/0005055.
The output of the algorithm is an estimate for the amplitude a, that with at least probability 1 - alpha has an error of epsilon. The number of A operator calls scales linearly in 1/epsilon (up to a logarithmic factor).
Parameters
- epsilon (
float
) – Target precision for estimation target a, has values between 0 and 0.5 - alpha (
float
) – Confidence level, the target probability is 1 - alpha, has values between 0 and 1 - confint_method (
str
) – Statistical method used to estimate the confidence intervals in each iteration, can be ‘chernoff’ for the Chernoff intervals or ‘beta’ for the Clopper-Pearson intervals (default) - min_ratio (
float
) – Minimal q-ratio () for FindNextK - state_preparation (
Union
[QuantumCircuit
,CircuitFactory
,None
]) – A circuit preparing the input state, referred to as . - grover_operator (
Union
[QuantumCircuit
,CircuitFactory
,None
]) – The Grover operator used as unitary in the phase estimation circuit. - objective_qubits (
Optional
[List
[int
]]) – A list of qubit indices. A measurement outcome is classified as ‘good’ state if all objective qubits are in state , otherwise it is classified as ‘bad’. - post_processing (
Optional
[Callable
[[float
],float
]]) – A mapping applied to the estimate of , usually used to map the estimate to a target interval. - a_factory (
Optional
[CircuitFactory
]) – The A operator, specifying the QAE problem - q_factory (
Optional
[CircuitFactory
]) – The Q operator (Grover operator), constructed from the A operator - i_objective (
Optional
[int
]) – Index of the objective qubit, that marks the ‘good/bad’ states - initial_state (
Optional
[QuantumCircuit
]) – A state to prepend to the constructed circuits. - quantum_instance (
Union
[QuantumInstance
,Backend
,BaseBackend
,None
]) – Quantum Instance or Backend
Raises
AquaError – if the method to compute the confidence intervals is not supported
__init__
__init__(epsilon, alpha, confint_method='beta', min_ratio=2, state_preparation=None, grover_operator=None, objective_qubits=None, post_processing=None, a_factory=None, q_factory=None, i_objective=None, initial_state=None, quantum_instance=None)
The output of the algorithm is an estimate for the amplitude a, that with at least probability 1 - alpha has an error of epsilon. The number of A operator calls scales linearly in 1/epsilon (up to a logarithmic factor).
Parameters
- epsilon (
float
) – Target precision for estimation target a, has values between 0 and 0.5 - alpha (
float
) – Confidence level, the target probability is 1 - alpha, has values between 0 and 1 - confint_method (
str
) – Statistical method used to estimate the confidence intervals in each iteration, can be ‘chernoff’ for the Chernoff intervals or ‘beta’ for the Clopper-Pearson intervals (default) - min_ratio (
float
) – Minimal q-ratio () for FindNextK - state_preparation (
Union
[QuantumCircuit
,CircuitFactory
,None
]) – A circuit preparing the input state, referred to as . - grover_operator (
Union
[QuantumCircuit
,CircuitFactory
,None
]) – The Grover operator used as unitary in the phase estimation circuit. - objective_qubits (
Optional
[List
[int
]]) – A list of qubit indices. A measurement outcome is classified as ‘good’ state if all objective qubits are in state , otherwise it is classified as ‘bad’. - post_processing (
Optional
[Callable
[[float
],float
]]) – A mapping applied to the estimate of , usually used to map the estimate to a target interval. - a_factory (
Optional
[CircuitFactory
]) – The A operator, specifying the QAE problem - q_factory (
Optional
[CircuitFactory
]) – The Q operator (Grover operator), constructed from the A operator - i_objective (
Optional
[int
]) – Index of the objective qubit, that marks the ‘good/bad’ states - initial_state (
Optional
[QuantumCircuit
]) – A state to prepend to the constructed circuits. - quantum_instance (
Union
[QuantumInstance
,Backend
,BaseBackend
,None
]) – Quantum Instance or Backend
Raises
AquaError – if the method to compute the confidence intervals is not supported
Methods
__init__ (epsilon, alpha[, confint_method, …]) | The output of the algorithm is an estimate for the amplitude a, that with at least probability 1 - alpha has an error of epsilon. |
construct_circuit (k[, measurement]) | Construct the circuit Q^k A |0>. |
is_good_state (measurement) | Determine whether a given state is a good state. |
post_processing (value) | Post processing of the raw amplitude estimation output . |
run ([quantum_instance]) | Execute the algorithm with selected backend. |
set_backend (backend, **kwargs) | Sets backend with configuration. |
Attributes
a_factory | Get the A operator encoding the amplitude a that’s approximated, i.e. |
backend | Returns backend. |
grover_operator | Get the operator, or Grover operator. |
i_objective | Get the index of the objective qubit. |
objective_qubits | Get the criterion for a measurement outcome to be in a ‘good’ state. |
precision | Returns the target precision epsilon of the algorithm. |
q_factory | Get the Q operator, or Grover-operator for the Amplitude Estimation algorithm, i.e. |
quantum_instance | Returns quantum instance. |
random | Return a numpy random. |
state_preparation | Get the operator encoding the amplitude . |
a_factory
Get the A operator encoding the amplitude a that’s approximated, i.e.
A |0>_n |0> = sqrt{1 - a} |psi_0>_n |0> + sqrt{a} |psi_1>_n |1>
see the original Brassard paper (https://arxiv.org/abs/quant-ph/0005055) for more detail.
Returns
the A operator as CircuitFactory
Return type
backend
Returns backend.
Return type
Union
[Backend
, BaseBackend
]
construct_circuit
construct_circuit(k, measurement=False)
Construct the circuit Q^k A |0>.
The A operator is the unitary specifying the QAE problem and Q the associated Grover operator.
Parameters
- k (
int
) – The power of the Q operator. - measurement (
bool
) – Boolean flag to indicate if measurements should be included in the circuits.
Return type
QuantumCircuit
Returns
The circuit Q^k A |0>.
grover_operator
Get the operator, or Grover operator.
If the Grover operator is not set, we try to build it from the operator and objective_qubits. This only works if objective_qubits is a list of integers.
Return type
Optional
[QuantumCircuit
]
Returns
The Grover operator, or None if neither the Grover operator nor the operator is set.
i_objective
Get the index of the objective qubit. The objective qubit marks the |psi_0> state (called ‘bad states’ in https://arxiv.org/abs/quant-ph/0005055) with |0> and |psi_1> (‘good’ states) with |1>. If the A operator performs the mapping
A |0>_n |0> = sqrt{1 - a} |psi_0>_n |0> + sqrt{a} |psi_1>_n |1>
then, the objective qubit is the last one (which is either |0> or |1>).
If the objective qubit (i_objective) is not set, we check if the Q operator (q_factory) is set and return the index specified there. If the q_factory is not defined, the index equals the number of qubits of the A operator (a_factory) minus one. If also the a_factory is not set, return None.
Returns
the index of the objective qubit
Return type
int
is_good_state
is_good_state(measurement)
Determine whether a given state is a good state.
Parameters
measurement (str
) – A measurement as bitstring, e.g. ‘01100’.
Return type
bool
Returns
True if the measurement corresponds to a good state, False otherwise.
Raises
ValueError – If self.objective_qubits
is not set.
objective_qubits
Get the criterion for a measurement outcome to be in a ‘good’ state.
Return type
Optional
[List
[int
]]
Returns
The criterion as list of qubit indices.
post_processing
post_processing(value)
Post processing of the raw amplitude estimation output .
Parameters
value (float
) – The estimation value .
Return type
float
Returns
The value after post processing, usually mapping the interval to the target interval.
precision
Returns the target precision epsilon of the algorithm.
Return type
float
Returns
The target precision (which is half the width of the confidence interval).
q_factory
Get the Q operator, or Grover-operator for the Amplitude Estimation algorithm, i.e.
where reflects about the |0>_n state and S_psi0 reflects about . See https://arxiv.org/abs/quant-ph/0005055 for more detail.
If the Q operator is not set, we try to build it from the A operator. If neither the A operator is set, None is returned.
Returns
returns the current Q factory of the algorithm
Return type
QFactory
quantum_instance
Returns quantum instance.
Return type
Optional
[QuantumInstance
]
random
Return a numpy random.
run
run(quantum_instance=None, **kwargs)
Execute the algorithm with selected backend.
Parameters
- quantum_instance (
Union
[QuantumInstance
,Backend
,BaseBackend
,None
]) – the experimental setting. - kwargs (dict) – kwargs
Returns
results of an algorithm.
Return type
dict
Raises
AquaError – If a quantum instance or backend has not been provided
set_backend
set_backend(backend, **kwargs)
Sets backend with configuration.
Return type
None
state_preparation
Get the operator encoding the amplitude .
Return type
QuantumCircuit
Returns
The operator as QuantumCircuit.