A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics

Understand

We propose a neural-network variational quantum algorithm to simulate the time evolution of quantum many-body systems.

• Based on a modified restricted Boltzmann machine (RBM) wavefunction ansatz, the proposed algorithm can be efficiently implemented in near-term quantum computers with low measurement cost.
• Using a qubit recycling strategy, only one ancilla qubit is required to represent all the hidden spins in an RBM architecture.
• The variational algorithm is extended to open quantum systems by employing a stochastic Schrodinger equation approach.