Quantum consistent neural/tensor networks for photonic circuits with strongly/weakly entangled states

TL;DR

This paper introduces neural and tensor network methods to simulate quantum optical systems with entangled states efficiently, overcoming traditional computational limitations and aiding quantum device design.

Contribution

It presents a novel quantum consistent neural/tensor network approach for simulating entangled photonic systems, scalable to larger Hilbert spaces.

Findings

Efficient approximation of unitary evolution in entangled systems.

Enables scalable quantum simulations with fewer training examples.

Improves precision in quantum system design and optimization.

Abstract

Modern quantum optical systems such as photonic quantum computers and quantum imaging devices require great precision in their designs and implementations in the hope to realistically exploit entanglement and reach a real quantum advantage. The theoretical and experimental explorations and validations of these systems are greatly dependent on the precision of our classical simulations. However, as Hilbert spaces increases, traditional computational methods used to design and optimize these systems encounter hard limitations due to the quantum curse of dimensionally. To address this challenge, we propose an approach based on neural and tensor networks to approximate the exact unitary evolution of closed entangled systems in a precise, efficient and quantum consistent manner. By training the networks with a reasonably small number of examples of quantum dynamics, we enable efficient parameter estimation in larger Hilbert spaces, offering an interesting solution for a great deal of quantum metrology problems.