aiqbench analysis
This paper is primarily about a quantum algorithm for continuous optimization, so it passes the topic gate. It extends Quantum Hamiltonian Descent to Riemannian manifolds, and its main claims are theoretical: quantum corrections fade at late times, so convergence near optima is governed by the classical potential. The numerical examples and query-complexity estimates are small-scale and early-stage, so readers should treat the practical advantage over classical optimizers as unproven.
Quantum Riemannian Hamiltonian Descent
arXiv:2603.28624v2 Announce Type: replace Abstract: We propose Quantum Riemannian Hamiltonian Descent (QRHD), a quantum algorithm for continuous optimization on Riemannian manifolds that extends Quantum Hamiltonian Descent (QHD) by incorporating geometric structure of the parameter space via a position-dependent metric in the kinetic term. We formulate QRHD in both operator and path integral formalisms and derive the corresponding quantum equations of motion, showing that quantum corrections appear in the action integral but they are suppressed at late times by the time-dependent dissipation factor. This implies that convergence near optimal points is controlled by the classical potential while quantum effects influence early-time dynamics. By analyzing the semiclassical equation, we estimate a lower bound on the convergence time and numerically demonstrate whether QRHD works as a quantum optimization algorithm in some examples. A quantum circuit implementation based on time-dependent Hamiltonian simulation is also discussed and the query complexity is estimated.
Abstract:We propose Quantum Riemannian Hamiltonian Descent (QRHD), a quantum algorithm for continuous optimization on Riemannian manifolds that extends Quantum Hamiltonian Descent (QHD) by incorporating geometric structure of the parameter space via a position-dependent metric in the kinetic term. We formulate QRHD in both operator and path integral formalisms and derive the corresponding quantum equations of motion, showing that quantum corrections appear in the action integral but they are suppressed at late times by the time-dependent dissipation factor. This implies that convergence near optimal points is controlled by the classical potential while quantum effects influence early-time dynamics. By analyzing the semiclassical equation, we estimate a lower bound on the convergence time and numerically demonstrate whether QRHD works as a quantum optimization algorithm in some examples. A quantum circuit implementation based on time-dependent Hamiltonian simulation is also discussed and the query complexity is estimated.
arXiv:2603.28624 [quant-ph]
(or arXiv:2603.28624v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2603.28624 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 231 words
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