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arxiv: 2408.11105 · v1 · pith:3DFSEDZ6 · submitted 2024-08-20 · quant-ph

Benchmarking bosonic and fermionic dynamics

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keywords benchmarkingquantumanalograndomizedbosonicdevicesdynamicsfermionic
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Analog quantum simulation allows for assessing static and dynamical properties of strongly correlated quantum systems to high precision. To perform simulations outside the reach of classical computers, accurate and reliable implementations of the anticipated Hamiltonians are required. To achieve those, characterization and benchmarking tools are a necessity. For digital quantum devices, randomized benchmarking can provide a benchmark on the average quality of the implementation of a gate set. In this work, we introduce a versatile framework for randomized analog benchmarking of bosonic and fermionic quantum devices implementing particle number preserving dynamics. The scheme makes use of the restricted operations which are native to analog simulators and other continuous variable systems. Importantly, like randomized benchmarking, it is robust against state preparation and measurement errors. We discuss the scheme's efficiency, derive theoretical performance guarantees and showcase the protocol with numerical examples.

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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. Kostant relation in filtered randomized benchmarking for passive bosonic devices

    quant-ph 2025-11 unverdicted novelty 6.0

    New immanant and character-based filters reduce computational cost in bosonic randomized benchmarking while providing simple variance expressions and constant low variance for the character filter.

  2. Fermionic Averaged Circuit Eigenvalue Sampling

    quant-ph 2025-04 unverdicted novelty 6.0

    FACES is a new protocol for simultaneous self-consistent learning of averaged error rates across many FLO gates with rigorously shown efficient sampling complexity via Kravchuk transformations.

  3. Randomized Benchmarking with Synthetic Quantum Circuits

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    A framework using synthetic circuits improves randomized benchmarking sample efficiency for reducible representations, showing over 100x advantage for SU(2)-symmetric high-spin systems versus character RB.