  We delve into the use of photonic quantum computing to simulate quantum
mechanics and extend its application towards quantum field theory. We develop
and prove a method that leverages this form of Continuous-Variable Quantum
Computing (CVQC) to reproduce the time evolution of quantum-mechanical states
under arbitrary Hamiltonians, and we demonstrate the method's remarkable
efficacy with various potentials. Our method centres on constructing an
evolver-state, a specially prepared quantum state that induces the desired
time-evolution on the target state. This is achieved by introducing a
non-Gaussian operation using a measurement-based quantum computing approach,
enhanced by machine learning. Furthermore, we propose a framework in which
these methods can be extended to encode field theories in CVQC without
discretising the field values, thus preserving the continuous nature of the
fields. This opens new avenues for quantum computing applications in quantum
field theory.
