A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

Ryan L'Abbate;Anthony D'Onofrio;Samuel Stein;Samuel Yen-Chi Chen;Ang Li;Pin-Yu Chen;Juntao Chen;Ying Mao
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Abstract

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this article, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. In addition, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
基于量子态保真度的量子-经典协同训练架构
最近的进展凸显了当前量子系统的局限性,特别是近期量子设备上的量子比特数量有限。这一限制极大地制约了量子计算机的应用范围。此外,随着可用量子比特的增加,计算复杂性也呈指数级增长,带来了更多挑战。因此,我们迫切需要高效地使用量子比特,并减少目前的局限性和未来的复杂性。为了解决这个问题,现有的量子应用尝试在混合框架中整合经典和量子系统。在本文中,我们专注于量子深度学习,并介绍了一种名为 co-TenQu 的经典-量子协作架构。经典部分采用张量网络进行压缩和特征提取,使高维数据能被编码到有限量子比特的逻辑量子电路上。在量子方面,我们提出了一种基于量子态保真度的评估函数,通过双方之间的反馈回路迭代训练网络。与最先进的方法相比,co-TenQu 在公平环境下可将经典深度神经网络的性能提高 41.72%。此外,它的性能比其他基于量子的方法高出 1.9 倍,并在使用量子比特减少 70.59% 的情况下实现了类似的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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