Quantum Reservoir Computing for Realized Volatility Forecasting
arXiv:2505.13933 · doi:10.1103/rbj7-4wnq
Abstract
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 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 quantum reservoir computing 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 quantum reservoir computing 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.
24 pages, close to published version
References in corpus (37)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Variational Quantum Algorithms
- Probing many-body dynamics on a 51-atom quantum simulator
- Quantum Chemistry in the Age of Quantum Computing
- Strong quantum computational advantage using a superconducting quantum processor
- Observation of a Many-Body Dynamical Phase Transition with a 53-Qubit Quantum Simulator
- Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator
- Programmable Quantum Simulations of Spin Systems with Trapped Ions
- Observation of non-Hermitian bulk-boundary correspondence in quantum dynamics
- Quantum error correction below the surface code threshold
- Quantum computing for finance: overview and prospects
- All-optical Reservoir Computing
- Computational Mechanics: Pattern and Prediction, Structure and Simplicity
- Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
- A universal qudit quantum processor with trapped ions
- Harnessing disordered quantum dynamics for machine learning
- Photonic architecture for scalable quantum information processing in NV-diamond
- Quantum reservoir processing
- Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
- Towards simulating 2D effects in lattice gauge theories on a quantum computer
- Entangling logical qubits with lattice surgery
- Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks
- Occam's Quantum Razor: How Quantum Mechanics can reduce the complexity of classical models
- Quantum reservoir computing with a single nonlinear oscillator
- Experimental quantum adversarial learning with programmable superconducting qubits
- Time Series Quantum Reservoir Computing with Weak and Projective Measurements
- Quantum Computing for Molecular Biology
- A high-fidelity quantum matter-link between ion-trap microchip modules
- Scalable photonic platform for real-time quantum reservoir computing
- Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations
- Experimental property-reconstruction in a photonic quantum extreme learning machine
- On fundamental aspects of quantum extreme learning machines
- Quantum pricing-based column-generation framework for hard combinatorial problems
- Multi-Level Variational Spectroscopy using a Programmable Quantum Simulator
- LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks
- Fermionic Simulators for Enhanced Scalability of Variational Quantum Simulation