在量子比特受限硬件上使用噪声注入部署大型IQP电路玻恩机

中文摘要 AI 辅助

基于瞬时量子多项式（IQP）电路的量子电路玻恩机（QCBM）具有可被经典高效估计的损失函数，但在推理阶段需要量子计算机。因此，经典计算机可以训练超过现有量子硬件量子比特数的大型IQP QCBM。这促使我们研究如何利用量子比特受限的量子计算机从更大的IQP QCBM中采样。借鉴对含噪IQP电路的经典模拟的见解，我们提出了一种IQP QCBM的部署协议，该协议在逻辑层面注入噪声，以概率方式将原始电路分解为更小的簇，这些簇可以在量子比特受限的量子处理器上采样。我们还对已知的噪声量子比特应用轻量级重采样，以减轻引入误差的影响。我们将该协议应用于浅层IQP QCBM的数值模拟，以及在IBM Heron上进行的实验——将316量子比特的IQP电路部署到156量子比特的硬件上，结果表明，缓解措施提高了经验分布与目标分布的相似性。所提出的部署协议还比具有精确单量子比特边缘分布的经典基线更准确地保留了相关结构。

英文摘要

Quantum circuit Born machines (QCBMs) built with instantaneous quantum polynomial (IQP) circuits have loss functions that can be efficiently estimated classically, but require quantum computers during inference. Thus, classical computers can train large IQP QCBMs that exceed the qubit numbers of available quantum hardware. This motivates research into how qubit-limited quantum computers can be used to sample from larger IQP QCBMs. Leveraging insights from classical simulation of noisy IQP circuits, we propose a deployment protocol for IQP QCBMs that injects noise at the logical level to probabilistically decompose the original circuit into smaller clusters that can be sampled on qubit-limited quantum processors. We also apply lightweight resampling of known noisy qubits to mitigate the impact of the introduced error. Numerical simulations of our protocol applied to shallow IQP QCBMs and IBM Heron experiments deploying a 316-qubit IQP circuit onto 156-qubit hardware show that mitigation improves the similarity of the empirical distribution to the target distribution. The proposed deployment protocol also crucially preserves correlation structures more accurately than a classical baseline that has accurate single-qubit marginals.

发表机构

• Korea Institute of Science and Technology Information（韩国科学技术信息研究院）

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