z iSrSSKJr SSKJr SSKrSSKJr SSK J r SSK J r J r JrJr SS KJr SS KJr SS KJr "S S \ 5rg)z Statevector Estimator V2 class ) annotations)IterableN) SparsePauliOp)BaseEstimatorV2)DataBinEstimatorPubLikePrimitiveResult PubResult) EstimatorPub) PrimitiveJob)_statevector_from_circuitc\rSrSrSrSSS.SSjjr\SSj5r\SSj5rSS .SS jjr SS jr SS jr S r g)StatevectorEstimatora Simple implementation of :class:`BaseEstimatorV2` with full state vector simulation. This class is implemented via :class:`~.Statevector` which turns provided circuits into pure state vectors. These states are subsequently acted on by :class:`~.SparsePauliOp`, which implies that, at present, this implementation is only compatible with Pauli-based observables. Each tuple of ``(circuit, observables, parameter values, precision)``, called an estimator primitive unified bloc (PUB), produces its own array-based result. The :meth:`~.EstimatorV2.run` method can be given a sequence of pubs to run in one call. .. note:: The result of this class is exact if the circuit contains only unitary operations. On the other hand, the result could be stochastic if the circuit contains a non-unitary operation such as a reset for a some subsystems. The stochastic result can be made reproducible by setting ``seed``, e.g., ``StatevectorEstimator(seed=123)``. .. plot:: :alt: Output from the previous code. :include-source: from qiskit.circuit import Parameter, QuantumCircuit from qiskit.primitives import StatevectorEstimator from qiskit.quantum_info import Pauli, SparsePauliOp import matplotlib.pyplot as plt import numpy as np # Define a circuit with two parameters. circuit = QuantumCircuit(2) circuit.h(0) circuit.cx(0, 1) circuit.ry(Parameter("a"), 0) circuit.rz(Parameter("b"), 0) circuit.cx(0, 1) circuit.h(0) # Define a sweep over parameter values, where the second axis is over # the two parameters in the circuit. params = np.vstack([ np.linspace(-np.pi, np.pi, 100), np.linspace(-4 * np.pi, 4 * np.pi, 100) ]).T # Define three observables. Many formats are supported here including # classes such as qiskit.quantum_info.SparsePauliOp. The inner length-1 # lists cause this array of observables to have shape (3, 1), rather # than shape (3,) if they were omitted. observables = [ [SparsePauliOp(["XX", "IY"], [0.5, 0.5])], [Pauli("XX")], [Pauli("IY")] ] # Instantiate a new statevector simulation based estimator object. estimator = StatevectorEstimator() # Estimate the expectation value for all 300 combinations of # observables and parameter values, where the pub result will have # shape (3, 100). This shape is due to our array of parameter # bindings having shape (100,), combined with our array of observables # having shape (3, 1) pub = (circuit, observables, params) job = estimator.run([pub]) # Extract the result for the 0th pub (this example only has one pub). result = job.result()[0] # Error-bar information is also available, but the error is 0 # for this StatevectorEstimator. result.data.stds # Pull out the array-based expectation value estimate data from the # result and plot a trace for each observable. for idx, pauli in enumerate(observables): plt.plot(result.data.evs[idx], label=pauli) plt.legend() gN)default_precisionseedcXlX lg)z Args: default_precision: The default precision for the estimator if not specified during run. seed: The seed or Generator object for random number generation. If None, a random seeded default RNG will be used. 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