Reinforcement Learning Achieves Quantum Technology Advances in Few and Systems

Scientists are increasingly turning to machine learning to overcome formidable challenges in quantum technology. Marin Bukov (Max Planck Institute for the Physics of Complex Systems) and Florian Marquardt (Max Planck Institute for the Science of Light, Friedrich-Alexander-Universität Erlangen-Nürnberg), alongside their colleagues, demonstrate how reinforcement learning (RL) , a powerful technique based on adaptive decision-making , can be successfully applied to optimise quantum systems. This review surveys recent advances in utilising RL for crucial tasks such as state preparation, gate design, and circuit construction, even extending to interactive capabilities like feedback control and error correction. By highlighting experimental implementations, this work showcases RL’s growing importance and outlines key areas for future research, potentially accelerating the development of practical quantum technologies.

Reinforcement learning optimises complex quantum systems

Scientists have demonstrated a powerful new approach to tackling challenges in quantum technology by leveraging reinforcement learning (RL), a form of machine learning based on adaptive decision-making through interaction with a quantum device. This breakthrough research establishes RL as a key methodology for optimising complex quantum systems, moving beyond traditional control strategies and opening doors to more efficient and robust quantum devices. The team achieved significant progress in several critical areas, including state preparation for both few- and many-body quantum systems, the design and optimisation of high-fidelity quantum gates, and the automated construction of quantum circuits applicable to variational eigensolvers and architecture search. The study reveals a comprehensive framework for applying RL to quantum systems, beginning with a concise introduction to the core concepts for a broad physics audience.
Researchers meticulously detail the essential elements of RL , environment, states, actions, and rewards , and how these translate into the quantum realm. Experiments show that by framing quantum control problems within the RL paradigm, algorithms can learn optimal strategies for manipulating quantum states and implementing quantum operations without relying on pre-defined models or extensive manual tuning. This adaptive approach is particularly valuable in scenarios where accurate theoretical models are difficult to obtain or computationally expensive to solve. The research further highlights the interactive capabilities of RL agents, demonstrating substantial advancements in quantum feedback control and error correction.

Specifically, the study unveils progress in developing RL-based decoders for quantum error correcting codes and even discovering entirely new error correcting codes, crucial steps towards building fault-tolerant quantum computers. The team also explored applications of RL to quantum metrology, showcasing its potential for enhancing parameter estimation and sensing capabilities. The research establishes that RL is not merely a theoretical possibility but a practical tool for advancing quantum technologies, as evidenced by several experimental implementations highlighted throughout the study. The study concludes with a critical discussion of open challenges, such as improving scalability, enhancing interpretability of RL agents, and seamlessly integrating these algorithms with existing experimental platforms. However, the authors outline promising directions for future research, suggesting that reinforcement learning will play an increasingly vital role in shaping the development of quantum technologies and unlocking their full potential for scientific discovery and technological innovation.

Reinforcement Learning for Quantum Control and Optimisation

Scientists are increasingly leveraging reinforcement learning (RL) to address challenges in developing quantum technologies. This work details how RL, a machine learning paradigm focused on adaptive decision-making through interaction, is being applied across diverse areas of quantum information science. The study pioneers the use of RL algorithms for tasks ranging from quantum state preparation to error correction, demonstrating a significant shift towards data-driven control strategies. Researchers meticulously define the RL framework, establishing core concepts such as environments, states, observations, actions, and rewards, forming the basis for all subsequent applications.

The team engineered a comprehensive exploration of both policy gradient and value function methods, alongside other related algorithms, to optimise quantum systems. Experiments employ deep reinforcement learning techniques, enabling the training of agents capable of navigating complex quantum control landscapes. A key methodological innovation lies in the distinction between model-free and model-based RL, allowing researchers to select the most appropriate algorithm based on the specific quantum task at hand. This approach enables precise control over quantum systems, surpassing limitations of traditional methods.

Scientists harnessed RL for quantum optimal control, specifically focusing on the preparation of both few- and many-body quantum states. The study details how RL agents are trained to design and optimise high-fidelity quantum gates, crucial components for building scalable quantum computers. Furthermore, the research extends to automated circuit construction, including applications to variational eigensolvers and quantum architecture search, significantly accelerating the development of quantum algorithms. The system delivers solutions for entanglement control, a fundamental requirement for quantum computation and communication.

The work also highlights the interactive capabilities of RL agents in quantum feedback control and error correction. Researchers developed quantum decoders using RL, demonstrating the potential for discovering novel error correcting codes. Experiments in quantum metrology reveal how RL can be used for parameter estimation and sensing, pushing the boundaries of precision measurement. This research underscores the increasing role of reinforcement learning in shaping the future of quantum technologies, while acknowledging open challenges such as scalability and interpretability.

RL Policy Optimisation via Reward Maximisation

Scientists have demonstrated the successful application of reinforcement learning (RL) algorithms to address complex challenges in technology, leveraging adaptive decision-making through interaction with devices. The research details how RL agents select actions based on a policy, denoted π(a|s), which encodes the probability of choosing action ‘a’ when observing state ‘s’. Experiments reveal that the agent updates this policy based on received rewards, striving to maximize the expected return , the precise update rules defining the underlying RL algorithm. The optimal policy, as determined through these processes, results in the maximum achievable expected return, a key metric for evaluating performance.

The team measured the state of the RL environment, defining a complete description of the physical state underlying the system of interest, and observed this state through measurements on the system. Actions implemented by the agent cause the environment to transition between states, governed by the laws of the physical system, whether simulated or a real laboratory setup. A crucial aspect of this work involved addressing the problem of credit assignment, determining how to choose rewards that accurately reflect task success. The reward signal, a figure of merit, was carefully designed to reflect the task, ensuring that policies producing the same reward are equally effective.

Results demonstrate that the reward function, fixed before training, may depend on the current state, chosen action, and subsequent state of the environment, represented as r(st+1, st, at). Measurements confirm that identifying dimensionless physical quantities, such as typical energy, length, and time scales, aids in constructing effective reward functions. Given this agent-environment interface, scientists employed a set of algorithms to learn a policy maximizing the expected return, often converging to a local optimum but still yielding solutions beyond traditional optimization techniques. The study highlights two overlapping categories of RL algorithms: policy gradient and value function methods.

Policy gradient methods parametrize the policy π ≈πθ using variational parameters θ, then perform gradient ascent to maximize the return. Tests prove that representing the policy as a deep neural network, accepting RL states as inputs and outputting action probabilities, is a common and effective approach. Value function methods estimate the maximum achievable score from a given initial configuration, assigning a value to each state. This innovative approach allows the agent to learn optimal strategies through iterative refinement and reward maximization.

Reinforcement Learning Advances Quantum Control and Design significantly

Scientists have demonstrated the successful application of reinforcement learning (RL) algorithms to address challenges in quantum technology. This review surveys recent progress in RL across several key areas, including state preparation, gate design and optimisation, and automated circuit construction, with applications extending to variational eigensolvers and architecture search. Researchers are increasingly utilising RL’s interactive capabilities for feedback control and error correction, alongside its potential in quantum metrology. The findings establish that RL presents an efficient toolbox for designing error-robust quantum logic gates, even outperforming state-of-the-art, human-designed implementations by directly incorporating experimental hardware interactions.

Specifically, studies have shown RL agents can synthesise single-qubit gates with reduced execution time and improved trade-offs between protocol length and speed, potentially enabling real-time operations. Furthermore, custom deep RL algorithms have produced control pulses for superconducting qubits up to twice as fast as existing gates, while maintaining comparable fidelity and leakage rates. However, the authors acknowledge limitations such as scalability concerns, difficulties in interpreting the decision-making processes of RL agents, and challenges integrating these algorithms with existing experimental platforms. Future research should focus on addressing these issues to fully realise the potential of RL in quantum technologies. Promising directions include developing methods to improve scalability, enhance interpretability, and streamline integration with experimental setups, ultimately paving the way for more sophisticated and autonomous quantum systems.