DeepQuantum Releases Open-Source Quantum Machine-Learning Platform
Researchers led by Jun-Jie He on Dec. 22, 2025 introduced DeepQuantum, an open-source PyTorch-based platform for quantum machine learning and photonic quantum computing. The framework integrates quantum circuits, photonic circuits, and measurement-based computing with Fock, Gaussian, and Bosonic backends, tensor-network techniques, and distributed parallel simulation. It supports hybrid quantum-classical variational algorithms on CPUs and GPUs, facilitating AI-for-quantum and quantum-for-AI development.
Key Points
- 1Integrates quantum-circuit, photonic-circuit, and measurement-based paradigms in a single open-source PyTorch framework.
- 2Implements Fock, Gaussian, Bosonic backends plus tensor networks and distributed parallel simulation for scalability.
- 3Enables practitioners to build and benchmark hybrid variational quantum-classical models on CPUs and GPUs.
Scoring Rationale
High novelty and practical tooling for quantum ML, but limited by specialized scope and preprint, single-source validation.
Sources
Public references used for this report.
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