A quantum artificial intelligence roadmap for autonomous photonic quantum technologies

Abstract

The growing scale and complexity of photonic quantum systems demand a transition from manual, heuristic control toward intelligent, autonomous operational frameworks. Quantum Artificial Intelligence (QAI) integrates machine learning, quantum-enhanced optimization, and real-time feedback to address key challenges in the calibration, stabilization, and scaling of photonic quantum technologies. Unlike conventional machine-learning-assisted quantum control and self-driving laboratory approaches that rely primarily on classical intelligence, the proposed QAI paradigm establishes a tighter integration between artificial intelligence and quantum hardware. Parameterized quantum circuits executed directly on the photonic processor participate in the agent’s decision-making process, while the same framework supports both real-time control tasks. QAI combines variational quantum neural networks, meta-reinforcement learning, multi-agent control architectures, and quantum optimization methods, such as the Quantum Approximate Optimization Algorithm, to enable adaptive calibration, error mitigation, and high-dimensional optimization in programmable photonic platforms. While this perspective focuses on real-time control and calibration, we also outline a conceptual pathway toward autonomous generative inverse design. To guide future development, we propose a three-stage roadmap that progresses from open-source hybrid quantum–classical control tools to fully autonomous laboratories capable of closed-loop self-optimization and experiment design. Experimental platforms include programmable silicon photonic circuits and lithium-niobate interferometers, where thermal drift, fabrication variability, and crosstalk require intelligent adaptation. Beyond quantum optics, the QAI framework has broader implications for quantum sensing, secure communications, and photonic computing. By introducing a unified QAI framework and a structured classification of Quantum Autonomy Levels (QALs), this work provides a roadmap toward autonomous quantum-optics laboratories and photonic quantum technologies.