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Error mitigation for variational quantum algorithms through mid-circuit measurements

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arxiv 2108.10927 v2 pith:5FNLWGTL submitted 2021-08-24 quant-ph

classification quant-ph
keywords quantumstatessubspacequbitsalgorithmserrorevolutionfull
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Noisy Intermediate-Scale Quantum (NISQ) algorithms require novel paradigms of error mitigation. To obtain noise-robust quantum computers, each logical qubit is equipped with hundreds or thousands of physical qubits. However, it is not possible to use memory-consuming techniques for current quantum devices having at most hundreds or at best thousands of physical qubits on their own. For specific problems, valid quantum states have a unique structure as in the case of Fock states and W-states where the Hamming weight is fixed, and the evolution takes place in a smaller subspace of the full Hilbert space. With this pre-knowledge, some errors can be detected in the course of the evolution of the circuit, by filtering the states not obeying the pattern through post-selection. In this paper, we present mid-circuit post-selection schemes for frequently used encodings such as one-hot, binary, gray, and domain-wall encoding. For the particular subspace of one-hot states, we propose a method that works by compressing the full Hilbert space to a smaller subspace, allowing projecting to the desired subspace without using any ancilla qubits. We demonstrate the effectiveness of the approach for the Quantum Alternating Operator Ansatz algorithm. Our method is particularly suitable for the currently available hardware, where measuring and resetting is possible, but classical control conditional operators are not.

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Cited by 1 Pith paper

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

  1. Perspectives on Utilization of Measurements in Quantum Algorithms

    quant-ph 2025-07 conditional novelty 3.0 of 10

    A survey that categorizes quantum measurement uses into static circuits, dynamic circuits, and challenge-solving techniques, and argues measurements deserve more attention in algorithm design.

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