The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environment. The study employed a multilayer perceptron (MLP) agent as a classical baseline and a parameterized variational quantum circuit (VQC) as a quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results demonstrated that the classical MLP achieved near-optimal policy convergence with a mean return of 498.7 +/- 3.2, maintaining stable equilibrium throughout training. In contrast, the VQC exhibited limited learning capability, with an average return of 14.6 +/- 4.8, primarily constrained by circuit depth and qubit connectivity. Noise robustness analysis further revealed that the MLP policy deteriorated gracefully under Gaussian perturbations, while the VQC displayed higher sensitivity at equivalent noise levels. Despite the lower asymptotic performance, the VQC exhibited significantly lower parameter count and marginally increased training time, highlighting its potential scalability for low-resource quantum processors. The results suggest that while classical neural policies remain dominant in current control benchmarks, quantum-enhanced architectures could offer promising efficiency advantages once hardware noise and expressivity limitations are mitigated.
The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environment. The study employed a multilayer perceptron (MLP) agent as a classical baseline and a parameterized variational quantum circuit (VQC) as a quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results demonstrated that the classical MLP achieved near-optimal policy convergence with a mean return of 498.7 +/- 3.2, maintaining stable equilibrium throughout training. In contrast, the VQC exhibited limited learning capability, with an average return of 14.6 +/- 4.8, primarily constrained by circuit depth and qubit connectivity. Noise robustness analysis further revealed that the MLP policy deteriorated gracefully under Gaussian perturbations, while the VQC displayed higher sensitivity at equivalent noise levels. Despite the lower asymptotic performance, the VQC exhibited significantly lower parameter count and marginally increased training time, highlighting its potential scalability for low-resource quantum processors. The results suggest that while classical neural policies remain dominant in current control benchmarks, quantum-enhanced architectures could offer promising efficiency advantages once hardware noise and expressivity limitations are mitigated.
Abstract Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning, yet their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise. This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles. By innovatively employing quantum circuits to dynamically generate parameters for classical Multi-Layer Perceptrons (MLPs) via amplitude encoding and parameterized quantum operations, VQC-MLPNet substantially expands representation capabilities and augments training stability. We provide rigorous theoretical guarantees via statistical learning techniques and Neural Tangent Kernel analysis, explicitly deriving upper bounds on approximation, uniform deviation, and optimization errors. These theoretical insights demonstrate exponential improvements in representation capacity relative to quantum circuit depth and the number of qubits, providing clear computational advantages over standalone quantum circuits and existing hybrid quantum architectures. Our theoretical claims are empirically corroborated through extensive experiments, including the classification of semiconductor quantum-dot charge states and the prediction of genomic transcription factor binding sites, demonstrating resilient performance even under realistic IBM quantum noise simulations. This research establishes a theoretically sound and practically robust framework, advancing the frontiers of quantum-enhanced learning for unconventional computing paradigms in the Noisy Intermediate-Scale Quantum era and beyond.
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.
This thesis investigates the integration of quantum computing into machine learning, specifically focusing on reinforcement learning. The study focuses on the building of a code library to enable the exploration of the use of variational quantum circuits as function approximators within reinforcement learning algorithms, specifically policy gradient methods. A comprehensive framework is developed to analyze the performance and trainability of variational quantum circuit-based reinforcement learning models, supported by this library, to understand how different quantum circuit designs and data re-uploading techniques might affect the performance and trainability. To demonstrate the library’s capabilities, experiments were conducted in classic control environments like CartPole-v1 and Acrobot-v1, evaluating the performance (measured as average return) and trainability of three quantum models. These experiments examined the effects of data re-uploading, classical postprocessing, and inverse temperature parameter scheduling on both metrics. The empirical results show that variational quantum circuits are effective function approximators, with data re-uploading significantly enhancing model performance. Additionally, the globality of the measured observables plays a critical role, with higher globality values leading to better performance. Despite increasing circuit expressiveness, the gradient magnitudes and variances remain substantial throughout training. Furthermore, the tested inverse temperature schedules, used to adjust the policy’s level of exploration, did not show any significant advantage, indicating the challenge in finding optimal settings for these schedules. The study also introduces the universal quantum classifier adapted for reinforcement learning. Contrary to the barren plateau phenomenon, it has been empirically demonstrated that although increasing the number of qubits leads to performance drops in the Acrobot environment, it does not lead to vanishing gradients during the agents’ training process. Furthermore, under specific sets of hyperparameters, all three quantum models demonstrate strong and similar performance levels in both environments.
One of the multiple facets of quantum reinforcement learning (QRL) is enhancing reinforcement learning (RL) algorithms with quantum submodules, namely with variational quantum circuits (VQC) as function approx-imators. QRL solutions are empirically proven to require fewer training iterations or adjustable parameters than their classical counterparts, but are usually restricted to applications that have a discrete action space and thus limited industrial relevance. We propose a hybrid quantum-classical (HQC) deep deterministic policy gradient (DDPG) approach for a robot to navigate through a maze using continuous states, continuous actions and using local observations from the robot’s LiDAR sensors. We show that this HQC method can lead to models of comparable test results to the neural network (NN)-based DDPG algorithm, that need around 200 times fewer weights. We also study the scalability of our solution with respect to the number of VQC layers and qubits, and find that in general results improve as the layer and qubit counts increase. The best rewards among all similarly sized HQC and classical DDPG methods correspond to a VQC of 8 qubits and 5 layers with no other NN. This work is another step towards continuous QRL, where literature has been sparse.
Quantum Reinforcement Learning (QRL) emerged as a branch of reinforcement learning (RL) that uses quantum submodules in the architecture of the algorithm. One branch of QRL focuses on the replacement of neural networks (NN) by variational quantum circuits (VQC) as function approximators. Initial works have shown promising results on classical environments with discrete action spaces, but many of the proposed architectural design choices of the VQC lack a detailed investigation. Hence, in this work we investigate the impact of VQC design choices such as angle embedding, encoding block architecture and postprocessesing on the training capabilities of QRL agents. We show that VQC design greatly influences training performance and heuristically derive enhancements for the analyzed components. Additionally, we show how to design a QRL agent in order to solve classical environments with continuous action spaces and benchmark our agents against classical feed-forward NNs.
Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At this stage, the evidence supporting the performance and advantages of quantum machine learning (QML) models relative to classical models is insufficient. To address this gap, this paper presents an empirical study on the performance of QML models and their classical counterparts. We compare seven model pairs spanning supervised learning and reinforcement learning. Our results indicate that the evaluated quantum machine learning models do not yet surpass the classical baselines in overall prediction performance, policy stability, or training time. Nevertheless, QML remains a promising approach for filtering noise and controlling false positives. Our research findings summarize the challenges facing quantum machine learning across hardware environments, training efficiency, and convergence stability, providing a foundation for research into the robustness and parameter optimization of QML. This work is publicly available at https://github.com/Z-537-437/QML.