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 efforts have explored a higher level of optimization by making the quantum circuits themselves resilient to noise. 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 QML and 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 and parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates 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. Extensively evaluated with 12 QML and VQE benchmarks on 10 quantum comput, QuantumNAS significantly outperforms baselines. For QML, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real QC. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD. We also open-source torchquantum (https://github.com/mit-han-lab/pytorch-quantum) for fast training of parameterized quantum circuits to facilitate future research.
翻译:Qantum 噪音是Noisy 中级量子流(NISQ)计算机的关键挑战。先前的缓解噪音工作主要侧重于门级或脉级噪声适应汇编。然而,有限的研究努力探索了更高的优化水平,使量子电路本身能够适应噪音。我们提议建立量子电路(QantumNAS),这是一个用于对变异电路和qubt绘图进行噪声调调调调调调的综合框架。挥发量量子电路是建造QML和量子模拟的一个很有希望的方法。然而,由于设计空间和参数培训成本巨大,找到最佳的变频电路及其最佳参数具有挑战性。我们提议通过引入新的超级环球体来取消电路搜索和参数培训。我们用多层预设的参数化门和训练来对变频电路流电路流和量绘图。我们从抓头到直径直径直径、直径直径直径直径直径直线路路路路路段的测试和直径直径直径直径直径直径直径直径直径直径直路路路路路路路路路路路段,然后进行进的对QQQQQ和直径直径直径直路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路段的测。我们路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路段路路路路路路路路路路路路路段路路路路路路段,我们最后通过S。我们10路路路路路路路路路路路路路路路路路路路路路路路路路路路段路段路路路路路路路路路路段路段路段路段路段和直路路路段和直路段路段和直路段路段路段路段和直路段和直至深路段路段路段和深路段路段路段路段路段路段路段路段路段路段路段路段路段路段路