Quantum Natural Gradient2019 · James Stokes, Josh Izaac, Nathan Killoran, Giuseppe Carleo
Primary or survey context for Quantum natural-gradient VQE; consult the paper for assumptions and implementation details.
arxiv.org/abs/1909.02108 ↗Attested & literatureAlgorithmsVariational quantum eigensolver
Quantum natural-gradient VQE
The Fubini–Study metric preconditions parameter updates according to circuit-state geometry.
Atlas stars stay in the public catalog. Saving this entry to your workspace starts an unstarred private copy.
The Fubini–Study metric preconditions parameter updates according to circuit-state geometry. This record separates the reusable method idea from any one molecule, Hamiltonian, optimizer, or device.
Circuit & simulation
hybrid objective
measure Hupdate θ
quantum circuit
preparemeasure H
classical update
update θ
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:
- Follow the steepest descent in the state's own geometry Method
Takes A parameterised circuit family; an objective function of its parameters, evaluated only through estimates bought with a finite shot budget; a starting point; and a stopping rule — a tolerance, an iteration cap, or an exhausted budget. Returns A preparation routine for the state at the parameters the search stopped at, and the total number of objective evaluations it consumed. The routine is returned whether or not the search found a minimum; that it stopped is not evidence that it converged.
How it works
The Fubini–Study metric preconditions parameter updates according to circuit-state geometry. In a complete experiment, the method must be paired with a defined qubit Hamiltonian, reference state, parameterized circuit, measurement grouping, classical optimizer, stopping rule, and error analysis. The catalog therefore treats it as a literature-backed algorithm record rather than pretending that one generic snippet is the paper's implementation. Use the cited source to recover assumptions and compare energy error, variance, circuit resources, measurement cost, optimizer evaluations, and robustness under the same instance and budget.
Implementation
Unsupported
vqe-natural-gradient.txt
METHOD: Quantum natural-gradient VQE
SCOPE: The Fubini–Study metric preconditions parameter updates according to circuit-state geometry.
This is a literature method record, not a fixed circuit.
Supply: Hamiltonian, reference state, ansatz, optimizer, measurement plan, and stopping rule.A reference record, not runnable source. Leona cannot execute it, so it cannot be saved to your Library as a circuit.
Quantum vs classical
Classical baseline
Compare Variational quantum eigensolver with the strongest classical method for the same instance, input budget, and output metric.
Quantum claim
This reference exposes a quantum circuit pattern; it does not imply an application-level speedup without a matched benchmark.
How to compare
Report input loading, circuit depth, repetitions, classical preprocessing, post-processing, and wall-clock time together.
Declared gaps
Nobody has reviewed this record for gaps yet.