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Strong empiricalAlgorithmsAmplitude estimation

Quantum amplitude estimation

A finance- and Monte-Carlo-facing primitive that makes the oracle and error model visible.

financemonte carloriskprobability estimation

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Amplitude estimation estimates the success probability embedded in a quantum state. It is attractive for risk, option pricing, and integration models, but only after state preparation and oracle costs are included.

Circuit & simulation
84%
What this takes and returns
TakesNothingWhat joins here

No input port at this edge: the record publishes no gate sequence and no register, so there is nothing here to read one off — and unlike a declared hole, nothing has been recorded about what belongs here.

Nothing in the Atlas meets this end.

ReturnsNothingWhat joins here

No output port at this edge: the record publishes no gate sequence and no register, so there is nothing here to read one off — and unlike a declared hole, nothing has been recorded about what belongs here.

Nothing in the Atlas meets this end.

This record publishes no gate sequence and no register, so there is nothing here to read an interface off. Absent rather than empty. See all 152 →

Where this sits

This record is named by the layer graph at:

  • Quantum simulation of the KvN representation Method

    Takes Access oracles for the components of FF (for example a linear part F1F_1, a quadratic part F2F_2, a forcing term F0F_0), a preparation unitary for yiny_{\mathrm{in}}, the evolution time TT, and an error tolerance ε\varepsilon. Returns A normalized state ε\varepsilon-close to y(T)/y(T)y(T)/\lVert y(T)\rVert, a history state over [0,T][0,T], or an estimate of an observable of the solution.

  • Coherent amplitude-estimation readout Method

    Takes A preparation routine AA with A0=ψA\lvert 0\rangle = \lvert \psi\rangle, or repeated copies of ρ\rho; a description of OO; a target additive error ε\varepsilon and a confidence 1δ1-\delta. Coherent, controlled access to AA and AA^\dagger is required by some methods here and by none of the sampling-based ones. Returns A scalar estimate with a stated additive-error guarantee, plus the shot or query budget and the maximum circuit depth actually consumed.

How it works

Amplitude estimation extracts the amplitude aa hidden in a state A0=1a0ϕ0+a1ϕ1\mathcal A|0\rangle=\sqrt{1-a}\,|0\rangle|\phi_0\rangle+\sqrt a\,|1\rangle|\phi_1\rangle prepared by a state-preparation operator A\mathcal A, where aa is typically a success probability or expectation of interest. The original construction runs phase estimation on the Grover-style operator Q=AS0ASχQ=-\mathcal A S_0 \mathcal A^\dagger S_\chi built from A\mathcal A and two reflections; QQ's eigenphases encode a=sin2θa=\sin^2\theta, so measuring mm phase-register qubits yields an estimate a^\hat a with error scaling as O(2m)=O(1/ε)O(2^{-m})=O(1/\varepsilon) queries to A\mathcal A, compared with O(1/ε2)O(1/\varepsilon^2) independent classical samples needed for the same error via a Chernoff/Hoeffding bound — a quadratic query reduction assuming coherent access to A\mathcal A. This record checks a small Bernoulli-amplitude toy circuit against repeated classical sampling; the query-count advantage, as the entry's caveat states, depends entirely on state-preparation and oracle-access costs a toy circuit cannot certify.

Implementation
Native
amplitude_estimation.py
from qiskit import QuantumCircuit

qc = QuantumCircuit(3)
# A prepares a Bernoulli amplitude.
qc.ry(0.7, 0)
# Grover iterate and phase-estimation controls follow.
qc.barrier()

FINAL_CIRCUIT = qc

The circuit is a readable access-model sketch; benchmark data must state the estimator variant and oracle cost.

Quantum vs classical
MetricClassicalQuantum
Query complexity (error ε)O(1/ε²)O(1/ε)
Cost modelMonte Carlo samplescoherent oracle calls
Asymptotic speedupbaselinequadratic

Classical baseline

Classical Monte Carlo with independent samples and a confidence interval for the same scalar quantity.

Quantum claim

Quadratic reduction in query/sample complexity under coherent state-preparation and oracle access.

How to compare

State the confidence interval, estimator variant, data-loading circuit, and total repetitions; a lower query count alone is not a business speedup.

Declared gaps

Nobody has reviewed this record for gaps yet.

Literature & references
Quantum Amplitude Amplification and Estimation2000 · Gilles Brassard, Peter Hoyer, Michele Mosca, Alain Tapp

Introduces amplitude amplification and the amplitude-estimation scaling used in Monte Carlo applications.

arxiv.org/abs/quant-ph/0005055