Benchmarking Photonic Quantum Machine Learning Simulators

Abstract

In the past few years, quantum computing has gotten more attention, and the need for efficient simulations is getting increasingly important as well. A significant branch of quantum computing is photonic quantum computing. For simulating photonic quantum circuits, Strawberry Fields is the most popular framework. In this paper, we compared it with another framework currently under development called Piquasso regarding gradient calculation time, which is an essential part of continuous-variable quantum neural networks. We present the apparent scalability of Piquasso over Strawberry Fields by storing fewer data, but leading to possible accuracy differences as a trade-off, which could motivate future work.