DeepQuantum: A PyTorch-based Software Platform for Quantum Machine Learning and Photonic Quantum Computing

TL;DR

DeepQuantum is an open-source PyTorch platform that supports hybrid quantum-classical models, photonic quantum computing, and large-scale simulations, enabling advanced quantum algorithm development.

Contribution

It introduces the first framework integrating quantum circuits, photonic quantum circuits, and measurement-based quantum computing with support for large-scale simulations.

Findings

Supports efficient hybrid quantum-classical models on CPUs and GPUs.

Implements multiple photonic quantum computing backends.

Enables large-scale simulations with tensor networks and distributed computing.

Abstract

We introduce DeepQuantum, an open-source, PyTorch-based software platform for quantum machine learning and photonic quantum computing. This AI-enhanced framework enables efficient design and execution of hybrid quantum-classical models and variational quantum algorithms on both CPUs and GPUs. For photonic quantum computing, DeepQuantum implements Fock, Gaussian, and Bosonic backends, catering to different simulation needs. To our knowledge, it is the first framework to realize closed-loop integration of three paradigms of quantum computing, namely quantum circuits, photonic quantum circuits, and measurement-based quantum computing, thereby enabling robust support for both specialized and universal photonic quantum algorithm design. Furthermore, DeepQuantum supports large-scale simulations based on tensor network techniques and a distributed parallel computing architecture. We demonstrate these capabilities through comprehensive benchmarks and illustrative examples. With its unique features, DeepQuantum is intended to be a powerful platform for both AI for Quantum and Quantum for AI.