Gulania Sahil, Alexeev Yuri, Gray Stephen K, Peng Bo, Govind Niranjan
Mathematics and Computer Science, Argonne National Laboratory, Lemont, Illinois 60439, United States.
Computational Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
J Chem Theory Comput. 2025 Jul 8;21(13):6280-6291. doi: 10.1021/acs.jctc.5c00353. Epub 2025 Jun 13.
Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANNs) and the Yang-Baxter equation (YBE). Unlike traditional error mitigation methods, which are computationally intensive, we investigate artificial error mitigation. We developed a novel method that combines ANNs for noise mitigation combined with the YBE to generate noisy data. This approach effectively reduces noise in quantum simulations, enhancing the accuracy of the results. The YBE rigorously preserves quantum correlations and symmetries in spin chain simulations in certain classes of integrable lattice models, enabling effective compression of quantum circuits while retaining linear scalability with the number of qubits. This compression facilitates both full and partial implementations, allowing the generation of noisy quantum data on hardware alongside noiseless simulations using classical platforms. By introducing controlled noise through the YBE, we enhance the data set for error mitigation. We train an ANN model on partial data from quantum simulations, demonstrating its effectiveness in mitigating errors in time-evolving quantum states, providing a scalable framework to enhance quantum computation fidelity, particularly in noisy intermediate-scale quantum (NISQ) systems. We demonstrate the efficacy of this approach by performing quantum time dynamics simulations using the Heisenberg XY Hamiltonian on real quantum devices.
量子计算显示出巨大潜力，但错误构成了重大挑战。本研究探索了使用人工神经网络（ANNs）和杨-巴克斯特方程（YBE）来减轻量子错误的新策略。与传统的计算量大的错误减轻方法不同，我们研究人工错误减轻。我们开发了一种新颖的方法，将用于减轻噪声的人工神经网络与杨-巴克斯特方程相结合以生成噪声数据。这种方法有效地降低了量子模拟中的噪声，提高了结果的准确性。在某些可积晶格模型的自旋链模拟中，杨-巴克斯特方程严格保持量子相关性和对称性，能够在保持与量子比特数量呈线性可扩展性的同时有效压缩量子电路。这种压缩有利于完整和部分实现，允许在硬件上生成噪声量子数据，同时使用经典平台进行无噪声模拟。通过杨-巴克斯特方程引入受控噪声，我们增强了用于减轻错误的数据集。我们在量子模拟的部分数据上训练人工神经网络模型，证明了其在减轻随时间演化的量子态中的错误方面的有效性，提供了一个可扩展的框架来提高量子计算保真度，特别是在有噪声的中等规模量子（NISQ）系统中。我们通过在真实量子设备上使用海森堡XY哈密顿量进行量子时间动力学模拟来证明这种方法的有效性。
