Quantum computing has entered the Noisy Intermediate-Scale Quantum (NISQ) era. Currently, the quantum processors we have are sensitive to environmental variables like radiation and temperature, thus producing noisy outputs. Although many proposed algorithms and applications exist for NISQ processors, we still face uncertainties when interpreting their noisy results. Specifically, how much confidence do we have in the quantum states we are picking as the output? This confidence is important since a NISQ computer will output a probability distribution of its qubit measurements, and it is sometimes hard to distinguish whether the distribution represents meaningful computation or just random noise. This paper presents a novel approach to attack this problem by framing quantum circuit fidelity prediction as a Time Series Forecasting problem, therefore making it possible to utilize the power of Long Short-Term Memory (LSTM) neural networks. A complete workflow to build the training circuit dataset and LSTM architecture is introduced, including an intuitive method of calculating the quantum circuit fidelity. The trained LSTM system, Q-fid, can predict the output fidelity of a quantum circuit running on a specific processor, without the need for any separate input of hardware calibration data or gate error rates. Evaluated on the QASMbench NISQ benchmark suite, Q-fid's prediction achieves an average RMSE of 0.0515, up to 24.7x more accurate than the default Qiskit transpile tool mapomatic. When used to find the high-fidelity circuit layouts from the available circuit transpilations, Q-fid predicts the fidelity for the top 10% layouts with an average RMSE of 0.0252, up to 32.8x more accurate than mapomatic.

翻译：量子计算已进入含噪声中等规模量子（NISQ）时代。目前，我们拥有的量子处理器对环境变量（如辐射和温度）敏感，从而产生噪声输出。尽管针对NISQ处理器已提出许多算法和应用，但在解读其噪声结果时仍面临不确定性。具体而言，我们对作为输出选取的量子态有多大置信度？这一置信度至关重要，因为NISQ计算机将输出其量子比特测量的概率分布，有时难以区分该分布代表有意义的计算还是随机噪声。本文提出一种新颖方法，通过将量子电路保真度预测构建为时间序列预测问题，从而利用长短期记忆（LSTM）神经网络的强大能力。文中介绍了构建训练电路数据集和LSTM架构的完整工作流，包括一种直观的量子电路保真度计算方法。训练后的LSTM系统Q-fid可预测在特定处理器上运行的量子电路的输出保真度，无需单独输入硬件校准数据或门错误率。在QASMbench NISQ基准套件上评估，Q-fid的预测平均均方根误差（RMSE）为0.0515，比默认的Qiskit转译工具mapomatic精确高达24.7倍。当用于从可用电路转译中找到高保真度电路布局时，Q-fid对前10%布局的保真度预测平均RMSE为0.0252，比mapomatic精确高达32.8倍。