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Faster quantum simulation by randomization

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arxiv 1805.08385 v2 pith:KOLZ5MCL submitted 2018-05-22 quant-ph

Faster quantum simulation by randomization

classification quant-ph
keywords boundsproductsummandsapproachbetterefficientformulashamiltonian
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Product formulas can be used to simulate Hamiltonian dynamics on a quantum computer by approximating the exponential of a sum of operators by a product of exponentials of the individual summands. This approach is both straightforward and surprisingly efficient. We show that by simply randomizing how the summands are ordered, one can prove stronger bounds on the quality of approximation for product formulas of any given order, and thereby give more efficient simulations. Indeed, we show that these bounds can be asymptotically better than previous bounds that exploit commutation between the summands, despite using much less information about the structure of the Hamiltonian. Numerical evidence suggests that the randomized approach has better empirical performance as well.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Optimal Lower Bounds for Hamiltonian Simulation

    quant-ph 2026-07 conditional novelty 7.0

    There exist simple weighted-local Hamiltonians for which quantum simulation requires Ω(min over K of (Kt + t²λ_K²/ε)) gates — exactly matching the composite qDRIFT algorithm's cost.

  2. Phase estimation with randomized Hamiltonians

    quant-ph 2019-07 unverdicted novelty 7.0

    Generalizes iterative phase estimation to randomized Hamiltonians per step plus importance sampling, yielding fewer terms and sometimes fewer qubits for gapped chemical Hamiltonians.

  3. Fast and Parallel High-Rate STAR Architecture for Megaquop Quantum Simulation

    quant-ph 2026-06 unverdicted novelty 6.0

    A symmetry-co-designed high-rate QEC architecture with parallel STAR injection on bivariate bicycle codes achieves ~5.5x space savings for TFIM and Fermi-Hubbard simulations versus surface-code STAR.