QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
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DOI：
10.1109/hpca53966.2022.00057
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Hanrui Wang;Yongshan Ding;Jiaqi Gu;Yujun Lin;D. Pan;F. Chong;Song Han
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文献类型：
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作者：
Hanrui Wang;Yongshan Ding;Jiaqi Gu;Yujun Lin;D. Pan;F. Chong;Song Han
量子噪声是嘈杂的中间量子量子（NISQ）计算机的关键挑战。降低噪声的先前工作主要集中在栅极级别或脉冲级噪声自适应汇编上。但是，有限的研究通过使量子电路本身具有弹性来探索更高水平的优化水平。在本文中，我们提出了Quontumnas，这是对变异电路和Qubit映射的噪声自动共同搜索的综合框架。变分量子电路是构建用于机器学习的量子神经网络和用于量子模拟的变异ansatzes的有前途的方法。但是，由于设计空间和参数训练成本较大，找到最佳的变分路及其最佳参数是具有挑战性的。我们建议通过引入新型的超电路将电路搜索从参数训练中解脱出来。 SuperCircuit由多层预定的参数化门（例如U3和Cu3）构建，并通过迭代采样并更新其的参数子集（子电路）。它提供了从头开始训练的子电路性能的准确估计。然后，我们对子电路及其Qubit映射进行进化共同搜索。子电路性能是用从超电路继承的参数估算的，并使用真实的设备噪声模型进行模拟。最后，我们执行迭代的门修剪和固定，以细粒度的方式去除冗余大门。通过12个量子机器学习（QML）（QML）和各种量子量化量化量化（VQE）基准，在14台量子计算机上进行了评估，人类，随机和现有的噪声自适应量子映射基线。对于QML任务，Quantumnas是第一个在实际量子计算机上证明超过95％2级，85％4级和32％10级分类精度的人。与UCCSD基准相比，它还可以实现H2，H2O，LIH，CH4，BEH2的VQE任务的最低特征值。我们还开放源代码，以便快速培训参数化量子电路，以促进未来的研究。
Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research has explored a higher level of optimization by making the quantum circuits themselves resilient to noise.In this paper, we propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing quantum neural networks for machine learning and variational ansatzes for quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search from parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates (e.g., U3 and CU3) and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates in a fine-grained manner.Extensively evaluated with 12 quantum machine learning (QML) and variational quantum eigensolver (VQE) benchmarks on 14 quantum computers, QuantumNAS significantly outperforms noise-unaware search, human, random, and existing noise-adaptive qubit mapping baselines. For QML tasks, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real quantum computers. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD baselines. We also open-source the TorchQuantum library for fast training of parameterized quantum circuits to facilitate future research.