3,808 research outputs found
Hamiltonian Quantum Generative Adversarial Networks
We propose Hamiltonian Quantum Generative Adversarial Networks (HQuGANs), to
learn to generate unknown input quantum states using two competing quantum
optimal controls. The game-theoretic framework of the algorithm is inspired by
the success of classical generative adversarial networks in learning
high-dimensional distributions. The quantum optimal control approach not only
makes the algorithm naturally adaptable to the experimental constraints of
near-term hardware, but also has the potential to provide a better convergence
due to overparameterization compared to the circuit model implementations. We
numerically demonstrate the capabilities of the proposed framework to learn
various highly entangled many-body quantum states, using simple two-body
Hamiltonians and under experimentally relevant constraints such as
low-bandwidth controls. We analyze the computational cost of implementing
HQuGANs on quantum computers and show how the framework can be extended to
learn quantum dynamics
- …