基于约束元量子电路(MQCC)的NISQ应用设计自动化

Haowei Deng, Yuxiang Peng, M. Hicks, Xiaodi Wu
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引用次数: 0

摘要

近期的中等规模量子(NISQ)计算机可能具有非常有限的硬件资源,其中精确可控的量子位昂贵,容易出错且稀缺。因此,这种计算机的程序员必须在特定于目标应用程序和量子硬件的大量(可能是异构的)因素之间进行权衡。为了帮助他们,我们提出了约束元量子电路(MQCC),这是一个量子程序的元编程框架。程序员将他们的应用程序表达为普通量子电路的简洁集合,由一组(手动或自动)添加的元级选择变量拼接在一起,这些变量的值根据一组可编程的定量优化标准进行约束。MQCC的编译器生成适当的约束,并通过SMT求解器解决这些约束,从而生成优化的、可运行的程序。我们展示了MQCC的一些通用应用,包括自动生成有效的错误综合征提取方案,用于使用异构量子位进行容错量子纠错,以及编写近似量子傅立叶变换和量子相位估计的方法,该方法可以顺利地权衡精度和资源使用。我们还说明MQCC可以轻松地编码先前的一次性NISQ应用程序设计——多编程(MP)、串扰缓解(CM)——以及它们的优化目标组合(即组合的MP-CM)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)
Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC), a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs-–multi-programming (MP), crosstalk mitigation (CM)—as well as a combination of their optimization goals (i.e., a combined MP-CM).
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