Introduces amplitude amplification and the amplitude-estimation scaling used in Monte Carlo applications.
arxiv.org/abs/quant-ph/0005055 ↗Quantum amplitude estimation
A finance- and Monte-Carlo-facing primitive that makes the oracle and error model visible.
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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
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 (for example a linear part , a quadratic part , a forcing term ), a preparation unitary for , the evolution time , and an error tolerance . Returns A normalized state -close to , a history state over , or an estimate of an observable of the solution.
- Coherent amplitude-estimation readout Method
Takes A preparation routine with , or repeated copies of ; a description of ; a target additive error and a confidence . Coherent, controlled access to and 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 hidden in a state prepared by a state-preparation operator , where is typically a success probability or expectation of interest. The original construction runs phase estimation on the Grover-style operator built from and two reflections; 's eigenphases encode , so measuring phase-register qubits yields an estimate with error scaling as queries to , compared with independent classical samples needed for the same error via a Chernoff/Hoeffding bound — a quadratic query reduction assuming coherent access to . 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
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 = qcThe circuit is a readable access-model sketch; benchmark data must state the estimator variant and oracle cost.
Quantum vs classical
| Metric | Classical | Quantum |
|---|---|---|
| Query complexity (error ε) | O(1/ε²) | O(1/ε) |
| Cost model | Monte Carlo samples | coherent oracle calls |
| Asymptotic speedup | baseline | quadratic |
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.