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Representative Papers

Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market

Jun 25, 2025

This study addresses the agent-objective mismatch in quantum reinforcement learning (QRL) for financial decision-making—characterized by high training rewards but poor out-of-sample performance—by proposing a hybrid quantum-classical RL framework for sector rotation in the Taiwan stock market. Methodologically, it adopts Proximal Policy Optimization (PPO) as the backbone, integrating LSTM and Transformer architectures with three quantum-enhanced modules: Quantum Neural Networks (QNNs), Quantum-Enhanced RWKV (QRWKV), and Quantum-Adaptive Self-Attention (QASA), alongside automated feature engineering, under NISQ-device constraints for end-to-end training. Contributions include: (1) establishing the first reproducible quantum-classical financial RL benchmark; (2) empirically demonstrating that short-horizon reward functions induce overfitting, resulting in significantly lower cumulative returns and Sharpe ratios versus classical baselines; and (3) identifying limited quantum circuit expressivity and optimization instability as key bottlenecks underlying the quantum-classical performance gap.

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Mixed-Signal Quantum Circuit Design for Option Pricing Using Design Compiler

Jun 19, 2025

Conventional quantum circuit design research over-abstracts implementation constraints, erroneously assuming exclusive reliance on purely digital VLSI methodologies—limiting practical deployment, especially for financial applications such as quantum-accelerated option pricing. Method: This work proposes a mixed-signal quantum circuit framework that synergistically integrates the compactness of analog circuits with the synthesis-friendly nature of digital circuits. It pioneers the integration of industrial-grade VLSI tools—including Synopsys Design Compiler—into quantum circuit synthesis, supported by three novel techniques: quantum-classical co-synthesis, noise-resilient gate mapping, and latency-aware scheduling. Contribution/Results: Evaluated on a 12-qubit option pricing benchmark, the approach reduces gate count from 4,095 to 392, compresses circuit depth from 2,048 to 6, and lowers logical error rate from 25.86% to 1.64%. These results demonstrate that mature VLSI methodologies significantly enhance quantum circuit practicality, scalability, and robustness.

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Latest Papers

Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market

Jun 25, 2025

This study addresses the agent-objective mismatch in quantum reinforcement learning (QRL) for financial decision-making—characterized by high training rewards but poor out-of-sample performance—by proposing a hybrid quantum-classical RL framework for sector rotation in the Taiwan stock market. Methodologically, it adopts Proximal Policy Optimization (PPO) as the backbone, integrating LSTM and Transformer architectures with three quantum-enhanced modules: Quantum Neural Networks (QNNs), Quantum-Enhanced RWKV (QRWKV), and Quantum-Adaptive Self-Attention (QASA), alongside automated feature engineering, under NISQ-device constraints for end-to-end training. Contributions include: (1) establishing the first reproducible quantum-classical financial RL benchmark; (2) empirically demonstrating that short-horizon reward functions induce overfitting, resulting in significantly lower cumulative returns and Sharpe ratios versus classical baselines; and (3) identifying limited quantum circuit expressivity and optimization instability as key bottlenecks underlying the quantum-classical performance gap.

0 citationsRead paper

Mixed-Signal Quantum Circuit Design for Option Pricing Using Design Compiler

Jun 19, 2025

Conventional quantum circuit design research over-abstracts implementation constraints, erroneously assuming exclusive reliance on purely digital VLSI methodologies—limiting practical deployment, especially for financial applications such as quantum-accelerated option pricing. Method: This work proposes a mixed-signal quantum circuit framework that synergistically integrates the compactness of analog circuits with the synthesis-friendly nature of digital circuits. It pioneers the integration of industrial-grade VLSI tools—including Synopsys Design Compiler—into quantum circuit synthesis, supported by three novel techniques: quantum-classical co-synthesis, noise-resilient gate mapping, and latency-aware scheduling. Contribution/Results: Evaluated on a 12-qubit option pricing benchmark, the approach reduces gate count from 4,095 to 392, compresses circuit depth from 2,048 to 6, and lowers logical error rate from 25.86% to 1.64%. These results demonstrate that mature VLSI methodologies significantly enhance quantum circuit practicality, scalability, and robustness.

0 citationsRead paper