  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.
