This paper investigates the integration of quantum computing into offline reinforcement learning and the deployment of the resulting quantum policy in a real-time control hardware realization of the cart-pole system. Variational Quantum Circuits (VQCs) are used to represent the policy. Classical model-based offline policy search was applied, in which a pure VQC with trainable input-output weights is used as a policy network instead of a classical multilayer perceptron. The goal is to evaluate the potential of deploying quantum architectures in real-world industrial control problems. The experimental results show that the investigated model-based offline policy search is able to generate quantum policies that can balance the hardware cart-pole. A latency analysis reveals that while local simulated execution meets real-time requirements, cloud-based quantum processing remains too slow for closed-loop control.
This paper investigates the integration of quantum computing into offline reinforcement learning and the deployment of the resulting quantum policy in a real-time control hardware realization of the cart-pole system. Variational Quantum Circuits (VQCs) are used to represent the policy. Classical model-based offline policy search was applied, in which a pure VQC with trainable input-output weights is used as a policy network instead of a classical multilayer perceptron. The goal is to evaluate the potential of deploying quantum architectures in real-world industrial control problems. The experimental results show that the investigated model-based offline policy search is able to generate quantum policies that can balance the hardware cart-pole. A latency analysis reveals that while local simulated execution meets real-time requirements, cloud-based quantum processing remains too slow for closed-loop control.
We explore the use of pure quantum policies, implemented via variational quantum circuits (VQCs), for offline reinforcement learning (RL). In contrast to hybrid models, our policy architecture contains no classical neural layers. Built on the MOOSE framework, we replace the classical policy with a VQC enhanced by trainable input and output weights. The policy is trained entirely offline using synthetic rollouts from a learned surrogate model of a physical cart-pole system. Evaluation in this simulated environment shows that the quantum policy performs on par with the classical baseline in terms of stability, smoothness, and reward accumulation. These results demonstrate that purely quantum models can effectively learn control strategies in model-based offline RL, offering a promising step toward real-world quantum-enhanced decision-making.
This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuits. The model is trained by gradient descent to fit a pre-recorded data set. The policy is optimized with a gradient-free optimization scheme using the return estimate given by the model as the fitness function. This model-based approach allows, in principle, full realization on a quantum computer during the optimization phase and gives hope that a quantum advantage can be achieved as soon as sufficiently powerful quantum computers are available.
This paper presents an engineering design framework for integrating Variational Quantum Circuits (VQCs) into industrial control systems via real-time quantum emulation on embedded hardware. In this work, we present a novel framework for fully embedded real-time quantum machine learning (QML), in which a four-qubit, four-layer VQC is both emulated and trained in situ on an FPGA-based embedded platform (dSPACE MicroLabBox 1202). The system achieves deterministic microsecond-scale response at a closed-loop frequency of 100 kHz, enabling its application in latency-critical control tasks. We demonstrate the feasibility of online VQC training within this architecture by approximating nonlinear functions in real time, thereby validating the potential of embedded QML for advanced signal processing and control applications. This approach provides a scalable and practical path toward real-time Quantum Reinforcement Learning (QRL) and other quantum-enhanced embedded controllers. The results validate the feasibility of real-time quantum emulation and establish a structured engineering design methodology for implementing trainable quantum machine learning (QML) models on embedded platforms, thereby enabling the development of deployable quantum-enhanced controllers.
In recent years, reinforcement learning (RL) has been proven to effectively provide solution to complex problems in various engineering problems such as self-driving cars, industry automation in production lines. These applications also extend to other fields including providing a platform for financial trading and healthcare. Reinforcement learning comes with the trade-offs between exploitation and exploration regardless of applied techniques and algorithms. In more complex engineering system, which often consists of large action and state-space. This space can increase exponentially, and most RL techniques fail to efficiently compute optimal policies to these problems. The inefficiency in RL models leads to the extensive requirement of computational process, which does not often come cheap and affordable. To tackle this challenge, quantum computational models were studied and achieved different levels of success. Since its first theory in the 1940s, quantum algorithm has been improved and advanced to provide exponential speed up compared to classical solutions. Quantum computation offers great potential improvements to traditional RL models due to its ability to create superpositions and entanglement. In past research, quantum variational circuits (QVC) were created as alternatives to neural networks commonly used in RL and experimented on several RL benchmarks in OpenAI Gym environments: CartPole, Acrobat and Lunar Landing. One of such QVCs were designed and tested to achieve greater efficiency in learning speed while offer reduced number of trainable parameter than classical RL. Our research aims to investigate the QVC to balance the CartPole problem running on both local PC simulator and quantum computer. The research signifies the first time a quantum RL could learn to obtain the optimal policy to acquire the maximum expected reward to a control problem and effectively apply the trainable parameter to balance the pole. The system is first tailored into a RL environment with state space and action space. For each time step, the input data from the environment (state space) is encoded into quantum states. Through the QVC, the algorithm learns to optimize the RL policy to calculate the probability of future action to obtain the optimal reward for each state-action pair. The trainable parameters from the QVC are again optimized using gradient descent. The QVC design is vital to the success of the RL model; therefore, we vary the QVC gates and investigate the model performance. It is expected that the Quantum RL model would outperform the classical RL in learning the optimal policy in term of speed and computational resources. The successful control of the pole on a quantum simulation would be proof that quantum models could offer real solution to future computation-intensive problems where classical solutions are unaffordable.
Abstract The application of quantum reinforcement learning to real-time control systems faces significant challenges regarding hardware latency, noise susceptibility, and learning convergence. This work presents an end-to-end investigation of a minimal hybrid quantum–classical agent applied to the CartPole benchmark, addressing the gap between idealized simulation and execution on a physical superconducting quantum processing unit. We demonstrate that a single-qubit agent acts as an effective learning model, and its performance is functionally comparable to a classical actor-critic network even when the training of the hybrid agent is restricted to use parameter-shift for its quantum circuit component. To connect learning to deployment constraints, we map the inference-time trade-off between control-loop rate and measurement shot budget to provide guidance for an eventual real-time control demonstration. The resulting performance matrices show that both inference control frequency and shot count strongly affect balancing stability: higher inference frequencies consistently improve performance, and increasing the shot budget lowers the minimum inference frequency required to achieve near-maximal balancing. These results highlight the importance of finding an optimal medium between shot count and control frequency and developing circuits that are e.g. initial-state invariant. Lastly, we address the critical bottleneck of control latency on noisy intermediate-scale quantum hardware. By bypassing the standard high-level software stack and programming the Zurich Instruments readout electronics directly via command tables, we achieve more than an order-of-magnitude improvement in execution speed on the VTT Q5 processor. These results quantify some of the current boundaries of quantum-assisted control and provide a start for achieving the tens-of-hertz throughput required for real-time closed-loop control feedback.
The application of quantum reinforcement learning (QRL) to real-time control systems faces significant challenges regarding hardware latency, noise susceptibility, and learning convergence. This work presents an end-to-end investigation of a minimal hybrid quantum-classical agent applied to the CartPole benchmark, addressing the gap between idealized simulation and execution on a physical superconducting quantum processing unit (QPU). We demonstrate that a single-qubit agent acts as an effective learning model, solving the environment in substantially fewer episodes than a comparable classical actor-critic network even when the training of the hybrid agent is restricted to use parameter-shift for its quantum circuit component. To connect learning to deployment constraints, we map the inference-time trade-off between control-loop rate and measurement shot budget to provide guidance for an eventual real-time control demonstration. The resulting performance matrices show that both inference control frequency and shot count strongly affect balancing stability: higher inference frequencies consistently improve performance, and increasing the shot budget lowers the minimum inference frequency required to achieve near-maximal balancing. These results highlight the importance of finding an optimal medium between shot count and control frequency and developing circuits that are e.g. initial-state invariant. Lastly, we address the critical bottleneck of control latency on NISQ hardware by bypassing the standard high-level software stack and programming the Zurich Instruments readout electronics directly via command tables. These results quantify some of the current boundaries of quantum-assisted control and provide a start for achieving the tens-of-hertz throughput required for real-time closed-loop control feedback.