Quantum Computers Enhance Fill Probability Estimates in Algorithmic Bond Trading, Improving Model Accuracy with Transformed Data

Estimating the likelihood of completing trade orders accurately is crucial for successful algorithmic trading, yet financial markets present complex and unpredictable dynamics. Axel Ciceri, Austin Cottrell, and Joshua Freeland, alongside colleagues from HSBC Holdings Plc and IBM Quantum, investigate how quantum computing can improve these estimations in the corporate bond market. The team developed a method to enhance fill probability predictions by incorporating data transformed using IBM’s Heron processors, and then compared the results to those from traditional simulations. This approach yields a significant improvement in predictive accuracy, demonstrating up to a 34% gain in out-of-sample test scores, and suggests that the inherent noise within current quantum hardware may surprisingly contribute to better performance. This research highlights the potential of quantum computing as a valuable exploratory tool for quantitative finance, paving the way for practical applications in institutional trading strategies.

Quantum Finance and Order Book Dynamics

This research explores the potential of quantum computing to improve financial modeling, specifically in understanding how orders are placed and executed in financial markets. Scientists investigate whether quantum algorithms can offer advantages over traditional methods in predicting market behavior, acknowledging the limitations of current quantum hardware. The focus remains on practical applications, seeking ways to overcome the challenges posed by near-term quantum devices. A key aspect of the work centers on order book dynamics and trade execution, a complex process crucial for efficient trading.

Researchers explore the concept of time reversal invariance in finance, considering whether quantum mechanics might offer new insights into market dynamics. The study provides a theoretical background in financial modeling and quantum computing, explaining relevant concepts and algorithms. The methodology involves selecting specific quantum algorithms, such as the Variational Quantum Eigensolver and Quantum Support Vector Machines, and employing techniques like zero-noise extrapolation and probabilistic error cancellation to reduce errors. Classical benchmarking is used to compare the performance of quantum algorithms to classical machine learning models. Results from quantum simulations are presented, along with analyses of error impacts.

Quantum Trading with Real Market Data

Scientists engineered a novel methodology to assess the impact of quantum computing on algorithmic trading strategies, focusing on estimating the probability of trade orders being filled in the corporate bond market. The study employed a quantum algorithm executed on IBM Heron processors to transform real-world, production-scale intraday trade data, creating a unique dataset for analysis. This quantum-transformed data was then compared to data processed by noiseless quantum simulators and the original trading data, allowing for a rigorous performance comparison. The core innovation lies in a decoupled framework, embedding the quantum-generated data transforms as a selectively queryable component within low-latency institutional trade optimization settings.

Researchers implemented a trade execution backtesting method to evaluate the fill prediction performance of machine learning models, systematically comparing results across the three data sources. The results demonstrate a relative gain of up to 34% in out-of-sample test scores for models utilizing the hardware-transformed data, compared to those relying on either the original data or the noiseless quantum simulations. This significant improvement suggests that the inherent noise present in current quantum hardware contributes to a beneficial effect on fill probability estimation. Scientists hypothesize that this noise introduces a form of regularization, potentially enhancing the model’s ability to generalize from limited data and improve prediction accuracy.

Quantum Computing Improves Bond Trade Execution

Scientists have demonstrated a significant advancement in algorithmic trading through the application of quantum computing to estimate fill probabilities for trade orders in the corporate bond market. The research focused on evaluating the impact of hardware-transformed data on the accuracy of fill probability estimations, a critical component of automated trading strategies. A novel framework was introduced to integrate data transformed by a quantum processor as a decoupled component, allowing selective querying by models designed for low-latency trading environments. Experiments employed a trade execution backtesting method to rigorously assess model performance with varying data inputs.

Results demonstrate a relative gain of up to 34% in out-of-sample test scores for models utilizing data transformed by the quantum hardware, compared to those relying on original trading data or transformations generated by noiseless simulations. This substantial improvement highlights the potential of quantum computing to enhance the accuracy of fill probability estimations, directly impacting trading strategy performance. The study suggests that inherent noise within the current quantum hardware contributes to this observed effect, prompting further investigation into the role of quantum noise in financial modeling. Researchers isolated the quantum processing component from classical systems to ensure a focused evaluation of its impact, providing a clear benchmark for future advancements. This work establishes a foundation for exploring quantum computing as a complementary tool in quantitative finance, encouraging industry research towards practical applications in algorithmic trading and market making.

Quantum Features Enhance Bond Trading Predictions

This work investigates the estimation of trade fill probabilities, a crucial component in algorithmic trading strategies, focusing on the European corporate bond market. Researchers developed a framework that separates fill probability estimation into online and offline components, allowing for the exploration of advanced data transformations independent of real-time trading. This approach uses quantum-generated features, created through a specific quantum circuit executed on quantum hardware and a noiseless simulator, as inputs to machine learning models. Results demonstrate significant improvements in out-of-sample test scores for models utilizing data transformed by noisy quantum hardware, compared to those using classical data or data from noiseless quantum simulations.

These findings suggest that the inherent noise within current quantum hardware may contribute to enhanced performance in predicting trade fill probabilities. Future research directions include investigating how quantum hardware noise specifically affects the quantum circuit used and exploring whether noise-encoded feature vectors can improve the analysis of noisy financial data. This work highlights the potential of quantum computing as a complementary tool in quantitative finance and encourages further industry research into practical applications within trading strategies.