aiqbench analysis
This research presents resource-efficient quantum algorithms for estimating matrix functions of Hermitian matrices, achieving shallower circuit depths and requiring only a single ancillary qubit through classically postprocessed product formulae. The approach offers practical advantages for systems with k-local interactions by tightening gate complexity bounds while extending protocols beyond standard observable estimation to include measurement distributions of time-evolved states. These advancements directly address critical hardware constraints in near-term quantum computing, making complex simulations like Green's function evaluation more feasible on existing devices.
Resource-efficient quantum eigenvalue transform with commutator scaling
arXiv:2608.13862v1 Announce Type: new Abstract: We develop quantum algorithms for estimating properties of general matrix functions of Hermitian matrices, with applications to phase estimation, Green's function evaluation, and estimating measurement distributions of time-evolved states. The resulting methods exhibit commutator scaling in matrix parameters similar to that usually found for product formulae, lower circuit depth in other parameters, and require only a single ancillary qubit. Our central primitive consists of classically postprocessing randomly chosen product formulae circuits, which mathematically corresponds to an approximation of a Richardson extrapolation. Within our framework, we introduce a protocol for approximating the measurement distributions of quantum states, extending beyond standard observable estimation. We also provide tightened gate complexity bounds for practically relevant systems, including those with k-local interactions, long-tailed matrix ensembles, and conserved quantities. Finally, numerical experiments confirm that our method can achieve significantly shallower circuit depths than standard product formulae in certain parameter regimes, and highlight the potential of their heuristic application.
Abstract:We develop quantum algorithms for estimating properties of general matrix functions of Hermitian matrices, with applications to phase estimation, Green's function evaluation, and estimating measurement distributions of time-evolved states. The resulting methods exhibit commutator scaling in matrix parameters similar to that usually found for product formulae, lower circuit depth in other parameters, and require only a single ancillary qubit. Our central primitive consists of classically postprocessing randomly chosen product formulae circuits, which mathematically corresponds to an approximation of a Richardson extrapolation. Within our framework, we introduce a protocol for approximating the measurement distributions of quantum states, extending beyond standard observable estimation. We also provide tightened gate complexity bounds for practically relevant systems, including those with k-local interactions, long-tailed matrix ensembles, and conserved quantities. Finally, numerical experiments confirm that our method can achieve significantly shallower circuit depths than standard product formulae in certain parameter regimes, and highlight the potential of their heuristic application.
arXiv:2608.13862 [quant-ph]
(or arXiv:2608.13862v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.13862 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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