Parameterized Quantum Circuits (PQC) have obtained increasing popularity thanks to their great potential for near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Achieving quantum advantages usually requires a large number of qubits and quantum circuits with enough capacity. However, limited coherence time and massive quantum noises severely constrain the size of quantum circuits that can be executed reliably on real machines. To address these two pain points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive quantum circuits, aiming to achieve two key objectives: (1) implicit circuits capacity during training - by dynamically exploring the circuit's sparse connectivity and sticking a fixed small number of quantum gates throughout the training which satisfies the coherence time and enjoy light noises, enabling feasible executions on real quantum devices; (2) noise robustness - by jointly optimizing the topology and parameters of quantum circuits under real device noise models. In each update step of sparsity, we leverage the moving average of historical gradients to grow necessary gates and utilize salience-based pruning to eliminate insignificant gates. Extensive experiments are conducted with 7 Quantum Machine Learning (QML) and Variational Quantum Eigensolver (VQE) benchmarks on 6 simulated or real quantum computers, where QuantumSEA consistently surpasses noise-aware search, human-designed, and randomly generated quantum circuit baselines by a clear performance margin. For example, even in the most challenging on-chip training regime, our method establishes state-of-the-art results with only half the number of quantum gates and ~2x time saving of circuit executions. Codes are available at https://github.com/VITA-Group/QuantumSEA.

翻译：参数化量子电路（PQC）因其在近期含噪中等规模量子（NISQ）计算机上展现的巨大潜力而日益受到关注。实现量子优势通常需要足够数量的量子比特和容量充足的量子电路。然而，有限的相干时间和大量量子噪声严重制约了可在真实机器上可靠执行的量子电路规模。为解决这两个痛点，我们提出QuantumSEA，一种面向噪声自适应量子电路的即时稀疏探索方法，旨在实现两个关键目标：（1）训练过程中的隐式电路容量——通过动态探索电路的稀疏连接性，在整个训练过程中保持固定小数量的量子门，以满足相干时间要求并降低噪声影响，从而实现真实量子设备上的可行执行；（2）噪声鲁棒性——在真实设备噪声模型下联合优化量子电路的拓扑结构与参数。在每次稀疏性更新步骤中，我们利用历史梯度的移动平均值来生长必要的量子门，并采用基于显著性的剪枝方法消除非重要门。我们在6台模拟或真实量子计算机上，针对7项量子机器学习（QML）和变分量子本征求解器（VQE）基准进行了广泛实验。结果显示，QuantumSEA在性能上始终显著超越噪声感知搜索、人工设计及随机生成的量子电路基线方法。例如，即便在最具挑战性的片上训练场景中，我们的方法仅使用一半数量的量子门并节省约2倍的电路执行时间，即取得了最先进的结果。代码见 https://github.com/VITA-Group/QuantumSEA。