量子电路预期值监督学习中的挑战和机遇
Simone Cantori1, Sebastiano Pilati1
1INFN-Sezione di Perugia, University of Camerino, School of Science and Technology, Physics Division, I-62032 Camerino (MC), Italy and , 06123 Perugia, Italy.
Physical review. E
|August 19, 2025
概括
深度神经网络可以模拟随机量子电路. 监督学习为具有高角度差异的电路提供了量子优势,优于大型模拟的经典方法.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 计算物理学的计算物理.
背景情况:
- 深度神经网络 (DNN) 在预测量子电路输出方面表现有前途.
- 监督学习 (SL) 是培训DNN的一个关键技术.
- 变量量子算法 (VQAs) 使用特定的电路架构.
研究的目的:
- 研究DNN在量子电路模拟方面的潜力.
- 确定这种方法的局限性和成功应用.
- 将DNN仿真与传统的直接仿真方法进行比较.
主要方法:
- 在随机量子电路上使用监督学习训练的DNN.
- 在变量量子算法 (VQA) 中常见的测试电路.
- 分析了电路参数 (层间变量,角度变化) 的计算成本缩放.
主要成果:
- SL成本以指数级扩大,间层角度差异,表明潜在的量子优势.
- 只有互量子比特角度变化的电路被高效地模拟.
- 训练有素的DNN准确地预测了经典模拟器无法处理的更大,更深的电路的预期值.
结论:
- 基于DNN的仿真为特定电路类型提供了获得量子优势的途径.
- 这种方法可以在大型量子电路中优于经典模拟.
- 标准电路指标 (纠,可表达性,成本) 不能预测模拟难度.
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