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.

我们利用量子电路学习来模拟量子场论，通过使用紧凑的量子比特配置和低深度的量子电路来预测量子场论中的实时动态，从而准确地预测各种物理参数，包括全连接算符。通过在一个 1+1 维的量子电动力学模型中预测淬火动力学、手性动力学和喷注产生，我们发现我们的预测与严格的经典计算结果十分吻合，显示了利用现代量子设备高效模拟大规模量子场论的可行性。