Circuit depth grows in stages so each newly introduced layer can be initialized and optimized locally.
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Circuit depth grows in stages so each newly introduced layer can be initialized and optimized locally. This record separates the reusable method idea from any one molecule, Hamiltonian, optimizer, or device.
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.
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 →
This record is named by the layer graph at:
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.
Circuit depth grows in stages so each newly introduced layer can be initialized and optimized locally. 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.
METHOD: Layerwise VQE training
SCOPE: Circuit depth grows in stages so each newly introduced layer can be initialized and optimized locally.
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.
Compare Variational quantum eigensolver with the strongest classical method for the same instance, input budget, and output metric.
This reference exposes a quantum circuit pattern; it does not imply an application-level speedup without a matched benchmark.
Report input loading, circuit depth, repetitions, classical preprocessing, post-processing, and wall-clock time together.
Nobody has reviewed this record for gaps yet.
Primary or survey context for Layerwise VQE training; consult the paper for assumptions and implementation details.
arxiv.org/abs/2006.14904 ↗