Optimizing Optical Quantum Logic Gates using Genetic Algorithms

TL;DR

This paper presents a genetic algorithm-based method to optimize optical quantum logic gates, achieving high fidelity and success probability, demonstrated on NS and CZ gates, with potential applications in quantum optics and sensors.

Contribution

Introduces an annealing genetic algorithm for optimizing quantum logic gates, achieving theoretical maximum success probabilities with high fidelity.

Findings

Successfully optimized NS gate to maximum success probability of 1/4.

Achieved high fidelity (0.9997) for CZ gate with success probability of 2/27.

Method applicable to other quantum transformations and areas.

Abstract

We introduce the method of using an annealing genetic algorithm to the numerically complex problem of looking for quantum logic gates which simultaneously have highest fidelity and highest success probability. We first use the linear optical quantum nonlinear sign (NS) gate as an example to illustrate the efficiency of this method. We show that by appropriately choosing the annealing parameters, we can reach the theoretical maximum success probability (1/4 for NS) for each attempt. We then examine the controlled-z (CZ) gate as the first new problem to be solved. We show results that agree with the highest known maximum success probability for a CZ gate (2/27) while maintaining a fidelity of 0.9997. Since the purpose of our algorithm is to optimize a unitary matrix for quantum transformations, it could easily be applied to other areas of interest such as quantum optics and quantum sensors.