标题： 通过量子电路学习的量子场论的量子-经典模拟 显示英文标题

标题： Quantum-classical simulation of quantum field theory by quantum circuit learning

摘要： 我们采用量子电路学习来模拟量子场论（QFTs）。通常，在使用量子计算机模拟QFTs时，由于量子设备在利用泡利自旋矩阵实现哈密顿量时的技术限制，会遇到重大挑战。为了解决这一难题，我们利用量子电路学习，采用紧凑的量子比特配置和低深度量子电路来预测量子场论中的实时动力学。这种方法的关键优势在于，单个量子比特测量可以准确预测包括完全连接算符在内的各种物理参数。为了展示我们方法的有效性，我们用它来预测量子电动力学1+1维模型中的猝发动力学、手征动力学和喷注产生。我们发现，我们的预测与严格的经典计算结果高度吻合，表现出极高的准确性。这种混合量子-经典方法展示了在前沿量子设备上高效模拟大规模QFTs的可行性。 显示英文摘要

摘要： We employ quantum circuit learning to simulate quantum field theories (QFTs). Typically, when simulating QFTs with quantum computers, we encounter significant challenges due to the technical limitations of quantum devices when implementing the Hamiltonian using Pauli spin matrices. To address this challenge, we leverage quantum circuit learning, employing a compact configuration of qubits and low-depth quantum circuits to predict real-time dynamics in quantum field theories. The key advantage of this approach is that a single-qubit measurement can accurately forecast various physical parameters, including fully-connected operators. To demonstrate the effectiveness of our method, we use it to predict quench dynamics, chiral dynamics and jet production in a 1+1-dimensional model of quantum electrodynamics. We find that our predictions closely align with the results of rigorous classical calculations, exhibiting a high degree of accuracy. This hybrid quantum-classical approach illustrates the feasibility of efficiently simulating large-scale QFTs on cutting-edge quantum devices.