Distributed quantum computation is often proposed to increase the scalability of quantum hardware, as it reduces cooperative noise and requisite connectivity by sharing quantum information between distant quantum devices. However, such exchange of quantum information itself poses unique engineering challenges, requiring high gate fidelity and costly non-local operations. To mitigate this, we propose near-term distributed quantum computing, focusing on approximate approaches that involve limited information transfer and conservative entanglement production. We first devise an approximate distributed computing scheme for the time evolution of quantum systems split across any combination of classical and quantum devices. Our procedure harnesses mean-field corrections and auxiliary qubits to link two or more devices classically, optimally encoding the auxiliary qubits to both minimize short-time evolution error and extend the approximate scheme’s performance to longer evolution times. We then expand the scheme to include limited quantum information transfer through selective qubit shuffling or teleportation, broadening our method’s applicability and boosting its performance. Finally, we build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms. To characterize our technique, we introduce a non-linear perturbation theory that discerns the critical role of our mean-field corrections in optimization and may be suitable for analyzing other non-linear quantum techniques. This fragmented pre-training is remarkably successful, reducing algorithmic error by orders of magnitude while requiring fewer iterations.

中文翻译：

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使用平均场校正和辅助量子位的近期分布式量子计算

分布式量子计算通常被提出来提高量子硬件的可扩展性，因为它通过在远程量子设备之间共享量子信息来减少协作噪声和必要的连接性。然而，这种量子信息交换本身就带来了独特的工程挑战，需要高门保真度和昂贵的非本地操作。为了缓解这一问题，我们提出近期分布式量子计算，重点关注涉及有限信息传输和保守纠缠产生的近似方法。我们首先设计了一种近似分布式计算方案，用于跨经典和量子设备的任意组合分割的量子系统的时间演化。我们的程序利用平均场校正和辅助量子位来传统地连接两个或多个设备，对辅助量子位进行最佳编码，以最大限度地减少短时演化误差并将近似方案的性能扩展到更长的演化时间。然后，我们扩展该方案以包括通过选择性量子位洗牌或隐形传态进行有限的量子信息传输，从而扩大我们的方法的适用性并提高其性能。最后，我们以这些概念为基础，为变分量子算法的分段预训练提供了一种近似电路切割技术。为了表征我们的技术，我们引入了一种非线性微扰理论，该理论可以识别平均场校正在优化中的关键作用，并且可能适合分析其他非线性量子技术。这种分段预训练非常成功，将算法错误减少了几个数量级，同时需要更少的迭代。