Deepquantum achieves closed-loop integration of three quantum computing paradigms

Quantum machine learning stands to revolutionize fields from drug discovery to materials science, but the development and implementation of these algorithms remains a major challenge. Jun-Jie He, Ke-Ming Hu, and Yu-Ze Zhu, along with colleagues including Guan-Ju Yan and Shu-Yi Liang from Shanghai Jiao Tong University, announced DeepQuantum, a new software platform designed to fill this gap. This open-source framework is built on the popular PyTorch platform and allows researchers to efficiently design and run hybrid quantum-classical models on standard computing hardware, while also simulating photonic quantum computers. DeepQuantum uniquely integrates three distinct approaches to quantum computation: circuit-based, photonic circuit-based, and measurement-based, providing a versatile toolkit for both specialized and general-purpose quantum algorithm design, paving the way for more accessible and powerful quantum machine learning applications.

China's national quantum science funding source

This document details extensive research funding and support for projects focused on quantum science and technology. Main funding sources include the National Key Research and Development Program of China, the National Natural Science Foundation of China (NSFC), the Quantum Science and Technology Innovation Program, and the Shanghai Municipal Science and Technology Commission (STCSM). Additional support will be provided by SJTU's Young Faculty Startup Fund, Jiangsu Province Frontier Technology Research and Development Program, and Zhiyuan Future Scholar Program. Additional support for X is mentioned in the specific acknowledgments.

DeepQuantum brings together AI and quantum computing paradigms

Scientists have developed DeepQuantum, a new open-source software platform designed to bridge artificial intelligence and quantum computing. Built on the widely adopted PyTorch platform, this framework enables efficient design and execution of both hybrid quantum-classical models and variational algorithms on standard CPUs and GPUs. DeepQuantum uniquely integrates three major paradigms of computing: quantum circuits, photonic quantum circuits, and measurement-based quantum computing, providing robust support for specialized and general-purpose photonic algorithm designs. For the first time in any framework, the team achieved closed-loop integration of these paradigms to support large-scale simulations through tensor network technology and distributed parallel computing architectures.

DeepQuantum's architecture features three core classes: QubitCircuit, QumodeCircuit, and Pattern, which give users the flexibility to build and simulate quantum computations. The QumodeCircuit class includes Fock, Gaussian, and Bosonic backends to address diverse simulation needs in photonic quantum computing. The Pattern class, on the other hand, facilitates exploration of measurement-based quantum computations. The researchers demonstrated the framework's capabilities through comprehensive benchmarks, achieving close simulations of circuits with more than 100 qubits on a single laptop. DeepQuantum leverages PyTorch's native communication protocols to efficiently utilize multi-node, multi-GPU computational power to power large-scale quantum simulations. The framework supports algorithmic design and mapping of time-domain multiplexed photonic quantum circuits and incorporates a built-in optimizer that enables on-chip training of quantum machine learning models.

Hybrid quantum-classical algorithm using DeepQuantum

DeepQuantum represents a major advance in the field of quantum computing, providing an open-source software platform built on PyTorch that integrates artificial intelligence with both classical and quantum computing techniques. The platform supports a variety of quantum backends, including Fock, Gaussian, and Bosonic systems, uniquely enabling closed-loop integration of circuits, photonic circuits, and measurement-based computing paradigms. This capability facilitates the design and implementation of hybrid quantum-classical algorithms, which are critical to extracting practical value from today's noisy medium-scale quantum devices. Researchers demonstrate DeepQuantum's capabilities through large-scale simulations that leverage tensor network technology and distributed parallel computing architectures.

These simulations demonstrate the platform's ability to model complex quantum systems and perform demanding calculations such as quantum Fourier transforms. The research team acknowledges the continuing challenges in scaling quantum computers and highlights the growing role of artificial intelligence in overcoming hurdles related to quantum error correction and circuit compilation. Future efforts may focus on extending the platform's capabilities, applying it to a broader range of problems, and further bridging the gap between theoretical quantum benefits and concrete real-world applications.