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This study rigorously evaluates randomized quantum linear systems solvers by deriving explicit error bounds for hybrid Fourier sampling and Hamiltonian simulation approaches. The findings reveal that even minimal problem instances demand an impractical number of non-Clifford gates, effectively challenging the viability of these schemes in the near-term fault-tolerant era while confirming Random Taylor Expansion as slightly more efficient than product formulas despite overall limitations.
The Practicality of Randomized Quantum Linear Systems Solvers
arXiv:2510.13766v2 Announce Type: replace Abstract: Randomized quantum algorithms have been proposed for quantum linear algebra with the goal of constructing shallower circuits than methods based on block encodings, and have been speculated to offer benefits in the early fault-tolerant era. In this work, we derive explicit, non-asymptotic error bounds on every algorithmic parameter of a randomized quantum linear systems solver that combines sampling from a Fourier series with Hamiltonian simulation, and confirm these bounds numerically. We show that even a $4 \times 4$ instance with condition number $\kappa = 100$ requires on the order of $10^{15}$ non-Clifford gates to converge, calling into question the practicality of such randomized schemes. Comparing the two Hamiltonian-simulation kernels, product formulas (PFs) and the random Taylor expansion (RTE), both our analytical bounds and experiments show RTE reaches a given target error with roughly an order of magnitude smaller total gate budget, although neither is practical. Our analysis bridges asymptotic proposals and hardware implementation.
Abstract:Randomized quantum algorithms have been proposed for quantum linear algebra with the goal of constructing shallower circuits than methods based on block encodings, and have been speculated to offer benefits in the early fault-tolerant era. In this work, we derive explicit, non-asymptotic error bounds on every algorithmic parameter of a randomized quantum linear systems solver that combines sampling from a Fourier series with Hamiltonian simulation, and confirm these bounds numerically. We show that even a $4 \times 4$ instance with condition number $\kappa = 100$ requires on the order of $10^{15}$ non-Clifford gates to converge, calling into question the practicality of such randomized schemes. Comparing the two Hamiltonian-simulation kernels, product formulas (PFs) and the random Taylor expansion (RTE), both our analytical bounds and experiments show RTE reaches a given target error with roughly an order of magnitude smaller total gate budget, although neither is practical. Our analysis bridges asymptotic proposals and hardware implementation.
arXiv:2510.13766 [quant-ph]
(or arXiv:2510.13766v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2510.13766 Focus to learn more arXiv-issued DOI via DataCite
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