Expectations¶

ExpectationAnalysis consumes the existing BackendResult.expectations {label: value} contract — the labeled observable values a backend associates with a run.

Inputs¶

A BackendResult, an ExecutionRecord, an ExperimentResult, a {label: float} mapping, or a serialized form carrying expectations.

Usage¶

from microquantum import ExpectationAnalysis, MockBackend, Parameter, QuantumCircuit

theta = Parameter("theta")
ansatz = QuantumCircuit(1).ry(theta, 0)

# Simulate with MockBackend and supply Z-expectation values directly.
backend = MockBackend()
r1 = backend.run(ansatz.bind_parameters({theta: 0.0}), shots=1024, seed=0)
r1.expectations = {"Z": 1.0}
r1.parameter_bindings = {"theta": 0.0}

r2 = backend.run(ansatz.bind_parameters({theta: 0.5}), shots=1024, seed=0)
r2.expectations = {"Z": 0.8776}
r2.parameter_bindings = {"theta": 0.5}

analysis = ExpectationAnalysis([r1, r2])
print(analysis.keys)                 # ('Z',) — the labels present
print(analysis.result_count)         # 2
print(analysis.mean("Z"))            # mean over the two executions
print(analysis.variance("Z"))
print(analysis.standard_deviation("Z"))
print(analysis.standard_error("Z"))
print(analysis.parameter_points("theta", "Z"))      # ordered points
print(analysis.parameter_to_expectation("theta", "Z"))
print(analysis.to_dict())

Notes¶

The analysis is consensus-based: it reads the labels backends already publish, so no new result format is introduced.
Per-label mean/variance/std/standard-error follow the microquantum.analysis.statistics conventions (population variance by default, ddof adjustable).