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Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers

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arxiv 2506.22408 v1 pith:BB7E2PIV submitted 2025-06-27 quant-ph physics.chem-ph

Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers

classification quant-ph physics.chem-ph
keywords quantummatchgateqc-afqmcreactionqubitsseveralalgorithmauxiliary
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We demonstrate an end-to-end workflow to model chemical reaction barriers with the quantum-classical auxiliary field quantum Monte Carlo (QC-AFQMC) algorithm with quantum tomography using matchgate shadows. The workflow operates within an accelerated quantum supercomputing environment with the IonQ Forte quantum computer and NVIDIA GPUs on Amazon Web Services. We present several algorithmic innovations and an efficient GPU-accelerated execution, which achieves a several orders of magnitude speedup over the state-of-the-art implementation of QC-AFQMC. We apply the algorithm to simulate the oxidative addition step of the nickel-catalyzed Suzuki-Miyaura reaction using 24 qubits of IonQ Forte with 16 qubits used to represent the trial state, plus 8 additional ancilla qubits for error mitigation, resulting in the largest QC-AFQMC with matchgate shadow experiments ever performed on quantum hardware. We achieve a $9\times$ speedup in collecting matchgate circuit measurements, and our distributed-parallel post-processing implementation attains a $656\times$ time-to-solution improvement over the prior state-of-the-art. Chemical reaction barriers for the model reaction evaluated with active-space QC-AFQMC are within the uncertainty interval of $\pm4$ kcal/mol from the reference CCSD(T) result when matchgates are sampled on the ideal simulator and within 10 kcal/mol from reference when measured on QPU. This work marks a step towards practical quantum chemistry simulations on quantum devices while identifying several opportunities for further development.

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Cited by 6 Pith papers

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

  1. Quantum-Classical Auxiliary-Field Quantum Monte Carlo at the Edge of Practicability

    quant-ph 2026-06 unverdicted novelty 6.0

    QC-AFQMC per-step scaling reduced from O(N^5.5) to O(N^4.5) via Aitken's block transformation for singular Pfaffians and algorithmic differentiation for force bias, with demonstrations on H8 from real quantum data and Li2O4.

  2. Selecting optimal unrestricted Hartree-Fock trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo: Accuracy and limitations in modeling three iron-sulfur clusters

    physics.chem-ph 2026-05 conditional novelty 5.0

    Chemical properties and symmetries, not variational energy, should guide UHF trial selection for ph-AFQMC on iron-sulfur clusters, yielding accurate energies despite suboptimal sampling and bias compensation.

  3. Quantum-HPC Software Stacks and the openQSE Reference Architecture: A Survey

    quant-ph 2026-04 unverdicted novelty 5.0

    A survey of nine QHPC stacks identifies common patterns and proposes the openQSE reference architecture to unify interfaces for interoperability in quantum-HPC environments.

  4. Benchmarking quantum trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo

    quant-ph 2026-05 unverdicted novelty 4.0

    Adaptive quantum ansatze outperform fixed UCCSD in ph-AFQMC projected energies for stretched H chains while using more compact circuits.

  5. Quantum-HPC Software Stacks and the openQSE Reference Architecture: A Survey

    quant-ph 2026-04 unverdicted novelty 4.0

    A survey of nine QHPC stacks identifies common design patterns and proposes the openQSE reference architecture to unify interfaces across runtime, resource management, and orchestration layers.

  6. Crosstalk In Contemporary Quantum Devices

    quant-ph 2026-05 unverdicted novelty 1.0

    Review synthesizing crosstalk mechanisms, mitigation strategies, and security vulnerabilities across major quantum computing platforms from existing literature.