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IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

Hybrid Quantum – A Framework for High Frequency Stock Trading Using Reinforcement Learning

Anuj Mishra, Aviral Garg, Aditya Ghosh, Abhishek Kumar Singh, Tripti Pandey

Published Jan 31, 2026• 0 citations
Publisher

Abstract

This Paper shows information about the High Speed Stock Trading, In todays era the normal computers are struggling to identify and find the complex market patterns and strategies. So in order to solve this issue we built a new system that is a combination of quantum computing and classical computing in which Reinforcement Learning (a way for computers to learn with trial and error) is used with Quantum Machine Learning to make smarter trading decisions and strategies. Specially, we have used a quantum model (called a Variational Quantum classifier) as a part of Deep Q Network agent to help the system learning better trading strategies. Our goal is to use the superior representational power of quantum feature spaces for financial decision making. The hybrid agent was trained in a high-fidelity simulation environment that includes realistic market frictions and a risk-adjusted reward function. Backtesting results on unseen data indicate that the hybrid agent consistently performs better than its traditional counterpart. It achieves a total return of $21.2 \%$, which is higher than the $\mathbf{1 8 . 5 \%}$ return from the classical model. Additionally, risk-adjusted performance has clearly improved. The hybrid agent’s Sortino Ratio is $32.6 \%$ higher and its Sharpe Ratio is $26.4 \%$ higher. Its maximum drawdown is also lower. These findings demonstrate that quantum models are able to detect minute market relationships that conventional approaches might miss. As a result, trading tactics become more reliable and successful. This research marks a substantial breakthrough in the application of quantum technologies to challenging computational finance problems.

DOI
10.1109/sceecs68810.2026.11429785

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This Paper shows information about the High Speed Stock Trading, In todays era the normal computers are struggling to identify and find the complex market patterns and strategies. So in order to solve this issue we built a new system that is a combination of quantum computing and classical computing in which Reinforcement Learning (a way for computers to learn with trial and error) is used with Quantum Machine Learning to make smarter trading decisions and strategies. Specially, we have used a quantum model (called a Variational Quantum classifier) as a part of Deep Q Network agent to help the system learning better trading strategies. Our goal is to use the superior representational power of quantum feature spaces for financial decision making. The hybrid agent was trained in a high-fidelity simulation environment that includes realistic market frictions and a risk-adjusted reward function. Backtesting results on unseen data indicate that the hybrid agent consistently performs better than its traditional counterpart. It achieves a total return of 21.2%21.2 \%21.2%, which is higher than the 18.5%\mathbf{1 8 . 5 \%}18.5% return from the classical model. Additionally, risk-adjusted performance has clearly improved. The hybrid agent’s Sortino Ratio is 32.6%32.6 \%32.6% higher and its Sharpe Ratio is 26.4%26.4 \%26.4% higher. Its maximum drawdown is also lower. These findings demonstrate that quantum models are able to detect minute market relationships that conventional approaches might miss. As a result, trading tactics become more reliable and successful. This research marks a substantial breakthrough in the application of quantum technologies to challenging computational finance problems.
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.
Quantum Machine Learning (QML) prediction of stock values is on the verge of changing the financial market and particularly enhancing HFT techniques. This is an attempt at quantum computing-machine learning hybrid aimed at enhancing the accuracy and efficiency of trading decisions. The historical machine learning-based stock price prediction models are incapable of processing large volumes of data, executing trades faster, and modeling complex market dynamics in real-time. The challenges result in ineffective trading decisions and trade lags at high frequencies. These problems are solved by HFT Optimization and Quantum Machine Learning. Because quantum computing capability is processing large amounts of data simultaneously, the proposed architecture enhances prediction accuracy and speed while reducing latency of decision-making. Quantum Neural Networks (QNNs) and quantum-optimized algorithms are useful to boost the modeling of market behavior. This technology will enhance stock price forecasting and optimization of trading strategies, which increases profit and minimize risk. Initial evidence indicates that quantum-based HFT systems are faster in execution speed and market flexibility than conventional techniques, which is essential to the future of automated trading.
The main goal of the proposed Quantum Reinforcement Learning (QRL) model that operates through Cirq framework serves to deliver competitive results in highfrequency trading environments in dynamic financial markets. Quantum parallel computing techniques provide to classical reinforcement learning methods a solution for their computational complexity and adaptability problems. The designed quantum-classical trading algorithm shows better market decisions along with quicker market reactions especially during periods of market volatility. The study investigates quantum-enhanced trading implementation methods and establishes three major research goals that include quantum scalability approaches, protecting quantum performance and building hybrid systems integrated with quantum and classical components.
Recognizing market trends in HFT gets simpler and faster thanks to quantum machine learning (QML) which also has the power to exceed classical model boundaries. Using quantum kernel methods and variational quantum circuits, QML can analyze high-dimensional financial information such as tick-by-tick data and order book samples, in the superposition state simultaneously. Comparative work proves that quantum-enhanced models have better accuracy, can detect patterns better and perform faster decisions on near-term quantum devices than classical ones. To be useful in banking-sector HFT, QML needs to easily connect with all existing internal information flows and trades. Essentially, users need to address hardware noise, guard against decoherence on NISQ machines, balance circuit complexity with tight time goals and guarantee scalable training for a quantum-classical mix. Latest progress in handling errors, such as zero-noise extrapolation, and adapting features has shown that models become stronger under realistic kinds of noise, although greater development is necessary to reach a production-ready level of reliability. Here, we bring together the most current work in QML for HFT, including different approaches, both software and hardware and ways to add quantum components to classical optimization loops. Outcomes from benchmarking are discussed, as well as the differing designs in kernel and variational approaches and significant parts of regulations related to quantum-assisted trading. In conclusion, a plan for more detailed studies is given, underlining how important consistent performance measures, detachable frameworks that can be used together and cooperation between technical fields are to get QML from research trials to compliant solutions in high-speed stock markets.
The ultra-low-latency decision-making process and sophisticated predictive technology are the key to staying competitive in the rapidly evolving global markets with the continuous fading of timeframes in affected markets. Classical algorithms are efficient but have limitations in scaling, speed, and non-linear market applications. This study introduces a quantum inspired model that integrates an encoding of quantum data, quantum approximate optimization, and quantum neural networks to generate real-time trading strategies. With quantum parallelism, the system processes volumes of financial data quickly and optimizes portfolio allocations to enhance predictive accuracy during times of volatility. A hybrid quantum-classical execution pipeline reduces latency during the linkage of algorithmic computations to global trading infrastructures. The framework offers more adaptability, higher risk-adjusted returns, and increased throughput compared to current statistical, machine learning, and quantum-inspired models. Experimental results demonstrate significant performance gains, with execution latency of 5.3 ms, prediction accuracy of 94.6 %, F1‑score of 93.8 %, portfolio efficiency of 0.91, throughput of 3,200 trades per second, and a Sharpe ratio of 1.56.
We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both classical architectures (LSTM, Transformer) and quantum-enhanced models (QNN, QRWKV, QASA) as policy and value networks. An automated feature engineering pipeline extracts financial indicators from capital share data to ensure consistent model input across all configurations. Empirical backtesting reveals a key finding: although quantum-enhanced models consistently achieve higher training rewards, they underperform classical models in real-world investment metrics such as cumulative return and Sharpe ratio. This discrepancy highlights a core challenge in applying reinforcement learning to financial domains-namely, the mismatch between proxy reward signals and true investment objectives. Our analysis suggests that current reward designs may incentivize overfitting to short-term volatility rather than optimizing risk-adjusted returns. This issue is compounded by the inherent expressiveness and optimization instability of quantum circuits under Noisy Intermediate-Scale Quantum (NISQ) constraints. We discuss the implications of this reward-performance gap and propose directions for future improvement, including reward shaping, model regularization, and validation-based early stopping. Our work offers a reproducible benchmark and critical insights into the practical challenges of deploying quantum reinforcement learning in real-world finance.