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This study demonstrates the robust scaling behavior of variational quantum algorithms on combinatorial optimization problems, maintaining high accuracy even at the scale of 5,000 qubits with shallow circuit depths. The findings suggest that classically simulable ansätze can effectively approximate optimal solutions within a tight error margin, offering valuable insights for the practical deployment of near-term quantum hardware.
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Scaling law of variational quantum algorithms

arXiv1 min read

arXiv:2609.39949v1 Announce Type: new Abstract: We study scaling law of variational quantum algorithms by large-scale numerical experiments on combinatorial optimizations. On experiments up to n = 5000 qubits with O( log(n)) circuit depth with a classically simulable ansatz, the converged energies consistently differ by no more than 0.5% from the optimal or reference energies reported in the benchmarks.

Abstract:We study scaling law of variational quantum algorithms by large-scale numerical experiments on combinatorial optimizations. On experiments up to n = 5000 qubits with O( log(n)) circuit depth with a classically simulable ansatz, the converged energies consistently differ by no more than 0.5% from the optimal or reference energies reported in the benchmarks.

arXiv:2609.39949 [quant-ph]

(or arXiv:2609.39949v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.39949 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 144 words

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