Fermionic Born Machines: Classical training of quantum generative models based on Fermion Sampling

Understand

Quantum generative learning is a promising application of quantum computers, but faces several trainability challenges, including the difficulty in experimental gradient estimations.

• For certain structured quantum generative models, however, expectation values of local observables can be efficiently computed on a classical computer, enabling fully classical training without quantum gradient evaluations.
• Although training is classically efficient, sampling from these circuits is still believed to be classically hard, so inference must be carried out on a quantum device, potentially yielding a computational advantage.
• In this work, we introduce Fermionic Born Machines as an example of such classically trainable quantum generative models.