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
this https URL

在这篇论文中，我们提出了 QuantumSEA 方法，通过动态探索电路的稀疏连接并在训练过程中保持少量的量子门，以实现训练时的电路容量，并在真实的量子设备上实现可行的执行；同时通过在真实设备噪声模型下联合优化量子电路的拓扑和参数，提高了噪声鲁棒性。在 7 个量子机器学习和变分量子特征解算器基准测试中进行了广泛实验，QuantumSEA 方法始终明显优于噪声感知搜索、人工设计和随机生成的量子电路基线，即使在最具挑战性的芯片训练模式下，我们的方法仅使用一半的量子门，节省了约两倍的电路执行时间。