Near-deterministic production of universal quantum photonic gates enhanced by machine learning

We introduce architectures for near-deterministic implementation of fully tunable weak cubic phase gates requisite 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 from a quantum gadget that uses displaced squeezed states, interferometers and photon-number resolving detectors. The parameters of this gadget are trained using machine learning algorithms for variational circuits. Stacking these gadgets in parallel we build quantum resource farms in a scalable manner depending on the error tolerance. Using conventional teleportation techniques we can implement weak cubic phase gates, in principle, at a rate $\sim {\rm 100 kHz}$ dictated by the photon number resolving detectors. Our proposal is realizable with current photonic technologies without the need for quantum memories. The methods for non-Gaussian state preparation is of independent interest to the resource theory of non-Gaussianity.