Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

EPJ Quantum Technology

A quantum reservoir computing approach to quantum stock price forecasting in technology-markets

Wendy Otieno, Alexandre M. Zagoskin, Alexander G. Balanov, Juan Sebastian Totero Gongora, Sergey E. Savel'ev

Published Sep 10, 2026• 0 citations
Publisher

Abstract

We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading prices of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025 (exclusive), as well as minute-by-minute trading prices during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding 86%. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.

DOI
10.1140/epjqt/s40507-026-00563-2

Related papers

A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets

Wendy Otieno, Alexandre Zagoskin, Alexander G. Balanov +2 more

We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading volumes of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025, as well as minute-by-minute trading volumes during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding $86 \%$. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.

quant-pharXiv:2602.13094•Feb 16, 2026•0 citations

Quantum reservoir computing for realized volatility forecasting

Qingyu Li, Chiranjib Mukhopadhyay, Abolfazl Bayat +1 more

Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing (QRC) has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disciplines, quantitative finance and econometrics can hugely benefit from emerging quantum technologies. In this work, we investigate the application of QRC for realized volatility forecasting. Our model employs a fully connected transverse-field Ising Hamiltonian as the reservoir with distinct input and memory qubits to capture temporal dependencies. The QRC approach is benchmarked against several econometric models and standard machine learning algorithms. The models are evaluated using multiple error metrics and the model confidence set procedures. To enhance interpretability and mitigate current quantum hardware limitations, we utilize wrapper-based forward selection for feature selection, identifying optimal subsets and quantifying feature importance via Shapley values. Our results indicate that the proposed quantum reservoir approach consistently outperforms benchmark models across various metrics, highlighting its potential for financial forecasting despite existing quantum hardware constraints. This work serves as a proof of concept for the applicability of quantum computing in econometrics and financial analysis, paving the way for further research into quantum-enhanced predictive modeling as quantum hardware capabilities continue to advance.

arXiv:2505.13933•Physical Review Research•Mar 12, 2026•3 citations

Optimal quantum reservoir computing for market forecasting: An application to fight food price crises

Laia Domingo, M. Grande, Gabriel G. Carlo +2 more

The emerging technology of Quantum Reservoir Computing (QRC) stands out in the noisy-intermediate scale quantum era for its efficiency, adaptability, and compatibility with current quantum hardware. By harnessing quantum dynamics for feature extraction, QRC offers a promising alternative for complex forecasting tasks — such as predicting agri-commodity price fluctuations — central to addressing food price crises and supply chain sustainability. In this work, we systematically investigate the design of quantum reservoirs and identify circuit complexity, quantified via the majorization criterion, as a practical indicator of forecasting performance. Unlike alternative complexity measures that require full state tomography or time-reversed dynamics, majorization can be evaluated from measurement statistics alone, making it particularly suited for near-term quantum devices. We demonstrate that quantum reservoirs built from a universal gate family composed of Hadamard, controlled-NOT, and T gates achieve strong predictive accuracy while requiring significantly fewer gates than the widely used transverse-field Ising model. Our results show that quantum reservoirs outperform deep learning and statistical baselines, and achieve comparable, slightly superior, performance to classical reservoir computing—an improvement consistent with the compact model structure of QRC, which, with significantly fewer trainable parameters, is better suited to prevent overfitting in data-scarce regimes. We further explore robustness under realistic noise conditions, showing that certain types of quantum noise, such as amplitude damping, can even enhance performance at low error rates. These findings suggest that QRC is a promising tool for time series prediction in data-limited, high-impact domains.

arXiv:2401.03347•Engineering Applications of Artificial Intelligence•Aug 7, 2026•0 citations

Quantum-Based Stock Price Forecasting with Stop Loss and False Breakout Analysis

Durga Meena Rajendran, K. Maharajan

This research work introduces a novel quantum computing approach to stock price forecasting that leverages the power of quantum algorithms for enhanced predictive accuracy and risk management. This comprehensive framework that integrates Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and Quantum Amplitude Estimation (QAE) to create a robust prediction system capable of navigating complex market dynamics. This approach goes beyond traditional quantum machine learning by incorporating specialized components for stop loss optimization and false breakout detection-critical elements for practical trading systems that are often overlooked in theoretical models. Experimental results across multiple market sectors during the 2018-2022 period demonstrates that this quantum-based framework achieves a 23.6% improvement in directional accuracy and a 31.4% enhancement in risk-adjusted returns compared to classical machine learning benchmarks. The model exhibits particular strength during high volatility periods, where quantum superposition and entanglement properties efficiently capture complex market patterns that elude classical algorithms. This research bridges the gap between theoretical quantum computing advantages and practical financial applications, offering significant implications for the future of algorithmic trading as quantum hardware continues to advance.

Asian Conference on Innovation in Technology (ASIANCON)•Aug 22, 2025•0 citations

Digital Quantum Reservoir Computing for ATM Time Series Prediction

Chiara Vercellino, Giacomo Vitali, Valeria Zaffaroni +5 more

We investigate a digital quantum reservoir computing (QRC) framework for multi-step forecasting of automated teller machine (ATM) cash demand time series on near-term quantum devices. The proposed approach uses parametrized four-qubit reservoirs with a fixed structure exploiting partial measurement and reset, where temporal data is encoded in rotation angles. Training is restricted to a classical Ridge-regression readout. We systematically analyze the impact of the circuit ansatzë, reservoir memory, measurement-derived observables, and the execution backend on the forecasting performance. Experiments are performed with noiseless simulation, noise-aware emulation, and a real IQM Spark quantum processor. Although the QRC models do not outperform the classical Prophet benchmark in terms of Mean Absolute Error and Normalized Mean Squared Error metrics, they achieve more competitive results in Dynamic Time Warping metric, indicating a partial ability to capture temporal structure. These findings provide an empirical assessment of digital QRC for realistic financial forecasting and highlight both its current limitations and its potential on near-term quantum hardware.

quant-pharXiv:2606.04686•Jun 4, 2026•0 citations

Quantum Computing–Driven Framework For Cryptocurrency Market Analysis And Price Forecasting

M. Venkata, Jagilinki Hemanjali, Datla Siva Rama Raju +3 more

Cryptocurrency markets are known for their high volatility and complex price dynamics, which make accurate prediction and analysis extremely challenging. Traditional financial forecasting models and classical machine learning algorithms often struggle to capture the nonlinear and rapidly changing patterns present in cryptocurrency datasets. In recent years, advancements in artificial intelligence and quantum computing have opened new possibilities for analyzing complex financial data and improving prediction accuracy.This study proposes a quantum computing–based framework for cryptocurrency market prediction by integrating quantum machine learning techniques with financial time-series analysis. The proposed model utilizes quantum computing concepts such as quantum feature mapping, variational quantum circuits, and quantum recurrent neural networks to analyze cryptocurrency market data. Historical datasets containing information about cryptocurrency prices, trading volume, and market capitalization are used to train and evaluate the model.The proposed system aims to identify hidden patterns in cryptocurrency market trends and generate accurate predictions for future price movements and market volatility. The performance of the quantum-based model is compared with classical deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Experimental results indicate that the quantum machine learning approach achieves improved prediction accuracy and lower forecasting error compared to traditional deep learning models.By leveraging the computational advantages of quantum computing, the proposed framework provides a powerful approach for analyzing highly complex financial datasets. The results demonstrate that quantum machine learning techniques have the potential to significantly enhance cryptocurrency market analysis, enabling more accurate forecasting and better decision-making for investors and financial analysts.

•Jan 1, 2026•0 citations
We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading prices of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025 (exclusive), as well as minute-by-minute trading prices during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding 86%. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.
We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading volumes of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025, as well as minute-by-minute trading volumes during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding 86%86 \%86%. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.
Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing (QRC) has emerged as a particularly powerful approach, combining quantum computation with machine learning for modeling nonlinear temporal dependencies in high-dimensional time series. As with many data-driven disciplines, quantitative finance and econometrics can hugely benefit from emerging quantum technologies. In this work, we investigate the application of QRC for realized volatility forecasting. Our model employs a fully connected transverse-field Ising Hamiltonian as the reservoir with distinct input and memory qubits to capture temporal dependencies. The QRC approach is benchmarked against several econometric models and standard machine learning algorithms. The models are evaluated using multiple error metrics and the model confidence set procedures. To enhance interpretability and mitigate current quantum hardware limitations, we utilize wrapper-based forward selection for feature selection, identifying optimal subsets and quantifying feature importance via Shapley values. Our results indicate that the proposed quantum reservoir approach consistently outperforms benchmark models across various metrics, highlighting its potential for financial forecasting despite existing quantum hardware constraints. This work serves as a proof of concept for the applicability of quantum computing in econometrics and financial analysis, paving the way for further research into quantum-enhanced predictive modeling as quantum hardware capabilities continue to advance.
The emerging technology of Quantum Reservoir Computing (QRC) stands out in the noisy-intermediate scale quantum era for its efficiency, adaptability, and compatibility with current quantum hardware. By harnessing quantum dynamics for feature extraction, QRC offers a promising alternative for complex forecasting tasks — such as predicting agri-commodity price fluctuations — central to addressing food price crises and supply chain sustainability. In this work, we systematically investigate the design of quantum reservoirs and identify circuit complexity, quantified via the majorization criterion, as a practical indicator of forecasting performance. Unlike alternative complexity measures that require full state tomography or time-reversed dynamics, majorization can be evaluated from measurement statistics alone, making it particularly suited for near-term quantum devices. We demonstrate that quantum reservoirs built from a universal gate family composed of Hadamard, controlled-NOT, and T gates achieve strong predictive accuracy while requiring significantly fewer gates than the widely used transverse-field Ising model. Our results show that quantum reservoirs outperform deep learning and statistical baselines, and achieve comparable, slightly superior, performance to classical reservoir computing—an improvement consistent with the compact model structure of QRC, which, with significantly fewer trainable parameters, is better suited to prevent overfitting in data-scarce regimes. We further explore robustness under realistic noise conditions, showing that certain types of quantum noise, such as amplitude damping, can even enhance performance at low error rates. These findings suggest that QRC is a promising tool for time series prediction in data-limited, high-impact domains.
This research work introduces a novel quantum computing approach to stock price forecasting that leverages the power of quantum algorithms for enhanced predictive accuracy and risk management. This comprehensive framework that integrates Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and Quantum Amplitude Estimation (QAE) to create a robust prediction system capable of navigating complex market dynamics. This approach goes beyond traditional quantum machine learning by incorporating specialized components for stop loss optimization and false breakout detection-critical elements for practical trading systems that are often overlooked in theoretical models. Experimental results across multiple market sectors during the 2018-2022 period demonstrates that this quantum-based framework achieves a 23.6% improvement in directional accuracy and a 31.4% enhancement in risk-adjusted returns compared to classical machine learning benchmarks. The model exhibits particular strength during high volatility periods, where quantum superposition and entanglement properties efficiently capture complex market patterns that elude classical algorithms. This research bridges the gap between theoretical quantum computing advantages and practical financial applications, offering significant implications for the future of algorithmic trading as quantum hardware continues to advance.
We investigate a digital quantum reservoir computing (QRC) framework for multi-step forecasting of automated teller machine (ATM) cash demand time series on near-term quantum devices. The proposed approach uses parametrized four-qubit reservoirs with a fixed structure exploiting partial measurement and reset, where temporal data is encoded in rotation angles. Training is restricted to a classical Ridge-regression readout. We systematically analyze the impact of the circuit ansatzë, reservoir memory, measurement-derived observables, and the execution backend on the forecasting performance. Experiments are performed with noiseless simulation, noise-aware emulation, and a real IQM Spark quantum processor. Although the QRC models do not outperform the classical Prophet benchmark in terms of Mean Absolute Error and Normalized Mean Squared Error metrics, they achieve more competitive results in Dynamic Time Warping metric, indicating a partial ability to capture temporal structure. These findings provide an empirical assessment of digital QRC for realistic financial forecasting and highlight both its current limitations and its potential on near-term quantum hardware.
Cryptocurrency markets are known for their high volatility and complex price dynamics, which make accurate prediction and analysis extremely challenging. Traditional financial forecasting models and classical machine learning algorithms often struggle to capture the nonlinear and rapidly changing patterns present in cryptocurrency datasets. In recent years, advancements in artificial intelligence and quantum computing have opened new possibilities for analyzing complex financial data and improving prediction accuracy.This study proposes a quantum computing–based framework for cryptocurrency market prediction by integrating quantum machine learning techniques with financial time-series analysis. The proposed model utilizes quantum computing concepts such as quantum feature mapping, variational quantum circuits, and quantum recurrent neural networks to analyze cryptocurrency market data. Historical datasets containing information about cryptocurrency prices, trading volume, and market capitalization are used to train and evaluate the model.The proposed system aims to identify hidden patterns in cryptocurrency market trends and generate accurate predictions for future price movements and market volatility. The performance of the quantum-based model is compared with classical deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. Experimental results indicate that the quantum machine learning approach achieves improved prediction accuracy and lower forecasting error compared to traditional deep learning models.By leveraging the computational advantages of quantum computing, the proposed framework provides a powerful approach for analyzing highly complex financial datasets. The results demonstrate that quantum machine learning techniques have the potential to significantly enhance cryptocurrency market analysis, enabling more accurate forecasting and better decision-making for investors and financial analysts.