用生成式量子电路解决组合优化问题，10比特内几乎完美
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
- 用Transformer生成条件量子电路，可灵活适配不同问题
- 在10比特组合优化问题上表现接近完美，泛化能力强
- 适合研究量子算法可扩展性与混合计算框架的学者
量子计算正进入新阶段，逻辑量子处理器有望解决超越经典能力的复杂问题。尽管进展显著，将量子算法应用于现实问题仍具挑战。混合量子-经典方法虽被探索，但常受限于表达能力、训练难度或可扩展性。本文提出条件生成式量子本征值求解器（conditional-GQE），一种基于编码器-解码器Transformer的上下文感知量子电路生成器。聚焦组合优化，我们在最多10个量子比特的问题上进行训练，对新问题表现出近乎完美的性能。通过利用经典生成模型的高表达力与灵活性，结合高效的偏好训练机制，conditional-GQE提供了一个可泛化且可扩展的量子电路生成框架。该方法推动了混合量子-经典计算的发展，有助于加速向容错量子计算的过渡。
原文摘要 · Abstract (English)
Quantum computing is entering a transformative phase with the emergence of logical quantum processors, which hold the potential to tackle complex problems beyond classical capabilities. While significant progress has been made, applying quantum algorithms to real-world problems remains challenging. Hybrid quantum-classical techniques have been explored to bridge this gap, but they often face limitations in expressiveness, trainability, or scalability. In this work, we introduce conditional Generative Quantum Eigensolver (conditional-GQE), a context-aware quantum circuit generator powered by an encoder-decoder Transformer. Focusing on combinatorial optimization, we train our generator for solving problems with up to 10 qubits, exhibiting nearly perfect performance on new problems. By leveraging the high expressiveness and flexibility of classical generative models, along with an efficient preference-based training scheme, conditional-GQE provides a generalizable and scalable framework for quantum circuit generation. Our approach advances hybrid quantum-classical computing and contributes to accelerate the transition toward fault-tolerant quantum computing.
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