Production of photonic universal quantum gates enhanced by machine learning

TL;DR

This paper presents a machine learning-enhanced method for producing high-fidelity non-Gaussian resource states in photonic quantum computing, enabling more efficient implementation of universal quantum gates.

Contribution

It introduces a novel photonic architecture trained with machine learning to generate resource states with higher success probabilities for quantum computation.

Findings

Achieved a 10^4-fold increase in resource state production probability.

Developed a scalable resource farm concept for improved success rates.

Demonstrated the implementation of tunable cubic phase gates using these states.

Abstract

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. We discuss in detail various aspects of the non-Gaussian state preparation resulting from the numerical experiments. We then propose a notion of resource farms where these gadgets are stacked in parallel, to increase the success probability further. We find a trade-off between the success probability of the farm, the error tolerance, and the number of gadgets. Using the resource states in conventional gate teleportation techniques we can then implement weak tuneable cubic phase gates. The numerical tools that have been developed could potentially be useful for other applications in photonics as well.