Fault-tolerant quantum computation requires scaling quantum processors to millions of physical qubits, yet purely superconducting architectures face significant hardware and control overhead. Hybrid multimode piezomechanical systems offer a hardware-efficient alternative by encoding quantum information in dense bosonic acoustic modes. However, stabilizing entanglement in such networks is challenging because continuous measurement and environmental dissipation generate strongly nonlinear driven-dissipative dynamics that are difficult to optimize analytically. Here we present an autonomous artificial-intelligence framework for the design and control of multimode superconducting optomechanical circuits. Using a model-free Recurrent Proximal Policy Optimization agent, the system learns a dynamic control protocol that exploits weak continuous measurement backaction to emulate a squeezed-vacuum environment, stabilizing steady-state remote entanglement with logarithmic negativity EN = 0.506 ± 0.027. In parallel, a Physics-Informed Neural Network incorporating the Lindblad master equation identifies control trajectories near a Liouvillian exceptional point, enabling efficient navigation of the non-Hermitian phase space. This physics-guided optimization reduces the circuit depth of standard quantum parity-check routines by 43.2% relative to conventional compilation. These results demonstrate that integrating machine learning with quantum control can autonomously discover robust strategies for stabilizing entanglement and reducing circuit complexity, providing a scalable pathway toward hardware-efficient quantum networks and distributed quantum information processing.
Fault-tolerant quantum computation requires scaling quantum processors to millions of physical qubits, yet purely superconducting architectures face significant hardware and control overhead. Hybrid multimode piezomechanical systems offer a hardware-efficient alternative by encoding quantum information in dense bosonic acoustic modes. However, stabilizing entanglement in such networks is challenging because continuous measurement and environmental dissipation generate strongly nonlinear driven-dissipative dynamics that are difficult to optimize analytically. Here we present an autonomous artificial-intelligence framework for the design and control of multimode superconducting optomechanical circuits. Using a model-free Recurrent Proximal Policy Optimization agent, the system learns a dynamic control protocol that exploits weak continuous measurement backaction to emulate a squeezed-vacuum environment, stabilizing steady-state remote entanglement with logarithmic negativity EN = 0.506 ± 0.027. In parallel, a Physics-Informed Neural Network incorporating the Lindblad master equation identifies control trajectories near a Liouvillian exceptional point, enabling efficient navigation of the non-Hermitian phase space. This physics-guided optimization reduces the circuit depth of standard quantum parity-check routines by 43.2% relative to conventional compilation. These results demonstrate that integrating machine learning with quantum control can autonomously discover robust strategies for stabilizing entanglement and reducing circuit complexity, providing a scalable pathway toward hardware-efficient quantum networks and distributed quantum information processing.
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.
This study proposes an HCQA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The HCQA integrates computational intelligence techniques by leveraging a Deep Q-Network (DQN) for learning and policy optimization, enhanced by a quantum-based action selection mechanism based on the Q-values. A quantum circuit encodes the agent current state using Ry gates, and then creates a superposition of possible actions. Measurement of the circuit results in probabilistic action outcomes, allowing the agent to generate optimal QSCs by selecting sequences of gates that maximize the Quantum Fisher Information (QFI) while minimizing the number of gates. This computational intelligence-driven HCQA enables the automated generation of entangled quantum states, specifically the squeezed states, with high QFI sensitivity for quantum state estimation and control. Evaluation of the HCQA on a QSC that consists of two qubits and a sequence of Rx, Ry, and S gates demonstrates its efficiency in generating optimal QSCs with a QFI of 1. This work highlights the synergy between AI-driven learning and quantum computation, illustrating how intelligent agents can autonomously discover optimal quantum circuit designs for enhanced sensing and estimation tasks.
Scaling superconducting quantum processors is fundamentally limited by environmentally induced decoherence arising from nanoscale fabrication defects. Conventional heuristic microwave control protocols cannot dynamically adapt to device-specific material variations, while purely data-driven deep learning approaches often violate physical constraints. Here we introduce a compact quantum physics-informed neural network (QPINN) that learns device-aware microwave control pulses for superconducting qubits. Trained on empirical defect characteristics extracted from 162 InAs nanosheet Josephson junction measurements, the model embeds the Lindblad master equation directly into its loss function to enforce physical consistency. Open-system simulations demonstrate a 42% reduction in leakage to non-computational states. Hardware experiments executed on the 156-qubit IBM Quantum processor show an increase in Ramsey dephasing time from 83.3 ± 7.8 μs to 113.0 ± 1.1 μs while maintaining gate fidelity exceeding 99.99%.
Abstract: Autonomous cyber-physical networks increasingly combine distributed sensing, edge intelligence, networked control, and safety-critical actuation, yet their reliability is constrained by correlated cyber faults, communication loss, model drift, hardware degradation, and the fragility of emerging quantum resources. This paper develops a fault-tolerant distributed quantum-intelligence architecture that couples photonic circuit emulation, quantum error-aware computation, AI-augmented control, distributed consensus, and deterministic safety fallback. The purpose is to formalize how heterogeneous quantum, photonic, AI, and control subsystems can jointly preserve mission-level stability when individual components fail or become uncertain. The study adopts a model-based systems methodology, synthesizes the supplied literature on fault-tolerant photonic quantum computing, distributed autonomous control, cyber-physical resilience, AI-native edge systems, and quantum-assisted optimization, and formulates a multi-layer state-space and reliability model. A composite Fault-Tolerant Quantum Intelligence Resilience Index (FTQIRI) integrates quantum reliability, photonic emulation fidelity, AI robustness, network resilience, closed-loop stability, and cyber assurance. An illustrative 0–10 numerical model yields an integrated score of 8.49 for the proposed architecture versus 6.34 for a conventional hybrid baseline, indicating the expected value of cross-layer redundancy and adaptive orchestration rather than empirical superiority. The paper further specifies an invention-oriented architecture, independent system and method claim drafts, and dependent claim concepts around digital-twin emulation, syndrome-informed control, consensus-based failover, cryptographic verification, and safety envelopes. The principal contribution is a unified mathematical and engineering framework that treats quantum fault tolerance and cyber-physical fault tolerance as coupled control problems, thereby connecting photonic quantum hardware, distributed intelligence, and autonomous network safety within one verifiable decision loop. Keywords: fault-tolerant quantum computing; distributed quantum intelligence; photonic circuit emulation; autonomous cyber-physical systems; AI-augmented control; quantum error correction; resilient edge intelligence; distributed consensus; safe reinforcement learning; digital twin; quantum–classical co-design; cyber resilience; multi-agent control; reliability optimization; verifiable AI
Quantum circuit optimization is a foundational challenge in realizing practical quantum computation, requiring the reduction of gate counts and circuit depth while preserving logical equivalence across noisy intermediate-scale quantum (NISQ) devices. Conventional rule-based and heuristic compilation approaches demonstrate diminishing effectiveness as circuit complexity grows, motivating the search for adaptive, data-driven alternatives. This paper reformulates quantum circuit optimization as a Markov decision process (MDP), enabling deep reinforcement learning (RL) agents to autonomously discover efficient transformation strategies. A graph neural network (GNN)-based actor-critic architecture is developed to encode circuit topology and guide transformation selection. Drawing on empirical evidence from phase-structured quantum control tasks, neural feedback architectures, and convergence behavior in active learning agents, we demonstrate that the proposed framework achieves an average two-qubit gate count reduction of 31.4% and a circuit depth reduction of 27.8% across standard benchmark circuits, outperforming established compilation baselines. This work provides a principled theoretical foundation for the RL-as-optimizer paradigm and contributes an extensible experimental platform suited to NISQ-era constraints.
Entanglement is fundamental to quantum information science and technology, yet controlling and manipulating entanglement—so-called entanglement engineering—for arbitrary quantum systems remains a formidable challenge. There are two difficulties: the fragility of quantum entanglement and its experimental characterization. We develop a model-free deep reinforcement-learning (RL) approach to entanglement engineering, in which feedback control together with weak continuous measurement and partial state observation is exploited to generate and maintain desired entanglement. We employ quantum optomechanical systems with linear or nonlinear photon–phonon interactions to demonstrate the workings of our machine-learning-based entanglement engineering protocol. In particular, the RL agent sequentially interacts with one or multiple parallel quantum optomechanical environments, collects trajectories, and updates the policy to maximize the accumulated reward to create and stabilize quantum entanglement over an arbitrary amount of time. The machine-learning-based model-free control principle is applicable to the entanglement engineering of experimental quantum systems in general.