Production of photonic universal quantum gates enhanced by machine learning

Understand

We introduce photonic architectures for universal quantum computation.

• The first step is to produce a resource state which is a superposition of the first four Fock states with a probability $\geq 10^{-2}$, an increase by a factor of $10^4$ over standard sequential photon-subtraction techniques.
• The resource state is produced with near-perfect fidelity from a quantum gadget that uses displaced squeezed vacuum states, interferometers and photon-number resolving detectors.
• The parameters of this gadget are trained using machine learning algorithms for variational circuits.