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arXiv 2303.08154quant-ph
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利用随机门激活训练变分量子算法

Training variational quantum algorithms with random gate activation

Shuo Liu, Shi-Xin Zhang, Shao-Kai Jian, Hong Yao

AI总结:

针对变分量子算法的贫瘠高原与局部极小值问题,提出随机量子门激活训练方法,以更少参数保持表达能力、逃离局部极小值并增强抗噪性。

摘要:

变分量子算法(VQA)在近期应用中具有巨大潜力,并有望在实际任务上实现量子优势。然而,VQA面临严重的贫瘠高原问题,并且有很大概率陷入局部极小值。在这篇Letter中,我们提出了一种用于VQA的、采用随机量子门激活的新型训练算法,以高效解决这两个问题。与传统的普通优化策略相比,这一新算法实际处理的训练参数少得多,从而在保持相同表达能力的同时有效缓解贫瘠高原。此外,通过向电路ansatz中随机添加双量子比特门,优化轨迹能够凭借更多随机性来源逃离局部极小值,并更频繁地到达全局最小值。在真实量子实验中,该新训练算法还可以减少所需的量子计算资源,并对量子噪声具有更强的韧性。我们将该训练算法应用于求解基态的变分量子模拟问题,并给出了有说服力的结果,展示了这一新策略的优势:通过缓解贫瘠高原、逃离局部极小值以及降低量子噪声影响的共同作用,实现了更好的性能。我们进一步提出,纠缠相变可能是我们的RA训练如此有效的一个潜在原因。

英文摘要:

Variational quantum algorithms (VQAs) hold great potentials for near-term applications and are promising to achieve quantum advantage on practical tasks. However, VQAs suffer from severe barren plateau problem as well as have a large probability of being trapped in local minima. In this Letter, we propose a novel training algorithm with random quantum gate activation for VQAs to efficiently address these two issues. This new algorithm processes effectively much fewer training parameters than the conventional plain optimization strategy, which efficiently mitigates barren plateaus with the same expressive capability. Additionally, by randomly adding two-qubit gates to the circuit ansatz, the optimization trajectories can escape from local minima and reach the global minimum more frequently due to more sources of randomness. In real quantum experiments, the new training algorithm can also reduce the quantum computational resources required and be more quantum noise resilient. We apply our training algorithm to solve variational quantum simulation problems for ground states and present convincing results that showcase the advantages of our novel strategy where better performance is achieved by the combination of mitigating barren plateaus, escaping from local minima, and reducing the effect of quantum noises. We further propose that the entanglement phase transition could be one underlying reason why our RA training is so effective.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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