The introduction of a Central Bank Digital Currency (CBDC), such as the Digital Euro, presents a critical challenge to commercial bank liquidity management due to the potential for large-scale deposit conversion, with the ECB estimating deposit drains of up to EUR 700 billion under extreme scenarios. This paper proposes a novel Quantum-Enhanced Deep Reinforcement Learning (QDRL) framework to optimize commercial bank funding strategies under CBDC-induced liquidity stress. By integrating a Variational Quantum Circuit (VQC) into the Soft Actor-Critic (SAC) algorithm, the QSAC agent minimizes funding costs while maintaining regulatory liquidity buffers across a five-bank simulation network with interbank contagion dynamics. The paper rigorously addresses the barren plateau problem through theoretical analysis and empirical gradient variance measurements, and stress-tests the framework under NISQ noise models calibrated to IBM Quantum hardware using Zero Noise Extrapolation. Results show an 8.1% funding cost reduction over classical DRL baselines and zero LCR breaches across four calibrated stress scenarios, though the advantage narrows to 3.2% against industry-standard MPC benchmarks. A Quantum Explainable AI analysis further interprets the agent's decision-making, identifying LCR and CBDC flow as the most influential features. While results remain simulation-only pending fault-tolerant quantum hardware, this research positions QDRL as a promising tool for financial stability and regulatory readiness in the evolving era of central banking.
The introduction of a Central Bank Digital Currency (CBDC), such as the Digital Euro, presents a critical challenge to commercial bank liquidity management due to the potential for large-scale deposit conversion, with the ECB estimating deposit drains of up to EUR 700 billion under extreme scenarios. This paper proposes a novel Quantum-Enhanced Deep Reinforcement Learning (QDRL) framework to optimize commercial bank funding strategies under CBDC-induced liquidity stress. By integrating a Variational Quantum Circuit (VQC) into the Soft Actor-Critic (SAC) algorithm, the QSAC agent minimizes funding costs while maintaining regulatory liquidity buffers across a five-bank simulation network with interbank contagion dynamics. The paper rigorously addresses the barren plateau problem through theoretical analysis and empirical gradient variance measurements, and stress-tests the framework under NISQ noise models calibrated to IBM Quantum hardware using Zero Noise Extrapolation. Results show an 8.1% funding cost reduction over classical DRL baselines and zero LCR breaches across four calibrated stress scenarios, though the advantage narrows to 3.2% against industry-standard MPC benchmarks. A Quantum Explainable AI analysis further interprets the agent's decision-making, identifying LCR and CBDC flow as the most influential features. While results remain simulation-only pending fault-tolerant quantum hardware, this research positions QDRL as a promising tool for financial stability and regulatory readiness in the evolving era of central banking.
Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-classical hybrid algorithms to tackling real-world finan-cial challenges. In this respect, we corroborate the concept with rigorous backtesting and validate the framework's performance under realistic market conditions, by including fixed transaction cost per trade. This paper introduces a Quantum Attention Deep Q-Network (QADQN) approach to address these challenges through quantum-enhanced reinforcement learning. Our QADQN architecture uses a variational quantum circuit inside a traditional deep Q-learning framework to take advantage of possible quantum advantages in decision-making. We gauge the QADQN agent's performance on historical data from major market indices, including the S&P 500. We evaluate the agent's learning process by examining its reward accumulation and the effectiveness of its experience replay mechanism. Our empirical results demonstrate the QADQN's superior performance, achieving better risk-adjusted returns with Sortino ratios of 1.28 and 1.19 for non-overlapping and overlapping test periods respectively, indicating effective downside risk management.
Despite rapid evolution of digital currencies, the demand for secure, fast and scalable cross border payment solution has emerged. At the moment, the frameworks for the existing Central Bank Digital Currency (CBDC) are slow for transactions, lack security, and are not capable of adjusting to the dynamic changes with respect to the exchange rate. This paper therefore suggests a novel Quantum Ledger Based Multi Currency Smart Exchange System (QL-MSES) to counter challenges Quantum computers are capable of, quantum cryptography, quantum ledger technology and machine learning driven prediction models. The real time sound synchronization of global transactions as well as unbreakable encryption are performed in Quantum Key Distribution (QKD) and Quantum Entanglement Protocol (QEP systems in this system. AI-PEX proxies the rest of the world using deep reinforcement learning, and the remainder of the world input is passed through ACMA which dynamically selects the best exchange paths. In addition, the system includes the Homomorphic Encryption and Zero-Knowledge Proofs system, hence allowing the flow of information without jeopardizing the user’s privacy and without violating regulatory compliance. With this evaluation, this proposed QL-MSES is enabled to be faster with transactions, secured with data, and agile in exchanges when markets are volatile. The results of the performance evaluation show that QL-MSES offers higher efficiency and security than a conventional CBDC system. By employing this innovative solution, the applications of CBDC 3.0 can be utilised for cross border remittance, smart treasury management and decentralised finance, a revolutionary structure to adopt CBDC 3.0.
This paper presents a Quantum Reinforcement Learning (QRL) solution to the dynamic portfolio optimization problem based on Variational Quantum Circuits. The implemented QRL approaches are quantum analogues of the classical neural-network-based Deep Deterministic Policy Gradient and Deep Q-Network algorithms. Through an empirical evaluation on real-world financial data, we show that our quantum agents achieve risk-adjusted performance comparable to, and in some cases exceeding, that of classical Deep RL models with several orders of magnitude more parameters. However, while quantum circuit execution is inherently fast at the hardware level, practical deployment on cloud-based quantum systems introduces substantial latency, making end-to-end runtime currently dominated by infrastructural overhead and limiting practical applicability. Taken together, our results suggest that QRL is theoretically competitive with state-of-the-art classical reinforcement learning and may become practically advantageous as deployment overheads diminish. This positions QRL as a promising paradigm for dynamic decision-making in complex, high-dimensional, and non-stationary environments such as financial markets. The complete codebase is released as open source at: https://github.com/VincentGurgul/qrl-dpo-public
In the era of Noisy Intermediate-Scale Quantum devices, Variational Quantum Circuits in Quantum Machine Learning are gaining attention, advancing towards practical quantum computing applications on NISQ devices. Reinforcement Learning (RL), known for its humanlike, trial-and-error learning, excels in adapting to changing financial environments. In particular, classical algorithms such as Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) show promise, while emerging quantum neural networks offer potential for improved function approximation, better generalization capabilities and reduced parameters. In light of these advancements, we introduce a quantum-enhanced version of the DDPG agent (QDDPG), aiming to leverage these quantum capabilities for more efficient financial decisionmaking processes. The quantum part of the model utilizes a parameterized quantum policy, which consists of a sequential reuploading variational circuit with trainable input scaling parameters. Our objective is to explore the practicality and potential benefits of QRL in finance, aiming to realize viable quantum computing applications on NISQ devices. The QDDPG model surpasses traditional analytic strategies in portfolio allocation, achieving higher returns with fewer parameters and a favourable Sharpe ratio. In a simple configuration using a RAW-parameterized quantum policy, the model achieves an average Sharpe ratio of 1.20 across 15 trained agents, compared to 0.96 for the minimum-variance strategy and 0.81 for the buy-and-hold baseline. Furthermore we investigate the impact of quantum noise, a significant challenge for NISQ devices. We analyze various types of noise, such as general depolarization, amplitude damping, phase damping, measurement noise, and shot noise, which uniquely affect quantum computations. Our study quantifies how these noise types influence the performance, reliability, and robustness of the QRL model. We study new techniques to harness quantum noise as hyperparameter to control the exploration-exploitation balance of the agent, using noise as regularization method and to stabilize the training of the agents. This comprehensive analysis aims to not only highlight the challenges posed by quantum noise but also to explore innovative methods to mitigate these effects and enhance the applicability of quantum algorithms in real-world applications.
Classical reinforcement learning (RL) methods often struggle in complex, high-dimensional environments because of their extensive parameter requirements and challenges posed by stochastic, non-deterministic settings. This study introduces quantum deep reinforcement learning (QDRL) to train humanoid agents efficiently. While previous quantum RL models focused on smaller environments, such as wheeled robots and robotic arms, our work pioneers the application of QDRL to humanoid robotics, specifically in environments with substantial observation and action spaces, such as MuJoCo's Humanoid-v4 and Walker2d-v4. Using parameterized quantum circuits, we explored a hybrid quantum-classical setup to directly navigate high-dimensional state spaces, bypassing traditional mapping and planning. By integrating quantum computing with deep RL, we aim to develop models that can efficiently learn complex navigation tasks in humanoid robots. We evaluated the performance of the Soft Actor-Critic (SAC) in classical RL against its quantum implementation. The results show that the quantum SAC achieves an 8% higher average return (246.40) than the classical SAC (228.36) after 92% fewer steps, highlighting the accelerated learning potential of quantum computing in RL tasks.