Hybrid Quantum-Classical Ridgelet Neural Networks for Portfolio Optimization
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914514789728256 |
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| author | Yadav, Bahadur Mohanty, Sanjay Kumar |
| author_facet | Yadav, Bahadur Mohanty, Sanjay Kumar |
| contents | In this study, we introduce a quantum computing method that incorporates Ridglet transforms into quantum processing pipelines for financial time-series forecasting with Quantum Approximate Optimization Algorithm (QAOA)-based portfolio optimization. We propose a Quantum Ridgelet Neural Network (QRNN) model for forecasting time-series data that integrates Parametrized Quantum Circuits (PQCs) with ridgelet-based feature transformations and QAOA-based portfolio optimization for asset selection. By breaking down financial time-series data into multi-resolution components, the ridgelet transform enables the identification of both local and global trends. Ridgelet-based features improve the scalability and accuracy of quantum computing by significantly reducing the number of qubits needed. However, the predicted results are turned into a QUBO-based mean-variance optimization problem and solved with QAOA to select the best stocks. Our study begins with a theoretical formulation of the single-qubit system for our proposed model. This formulation is further extended to a multi-qubit system, and we show that it captures a significant fraction of the predictive signal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03654 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hybrid Quantum-Classical Ridgelet Neural Networks for Portfolio Optimization Yadav, Bahadur Mohanty, Sanjay Kumar Machine Learning Optimization and Control Quantum Algebra In this study, we introduce a quantum computing method that incorporates Ridglet transforms into quantum processing pipelines for financial time-series forecasting with Quantum Approximate Optimization Algorithm (QAOA)-based portfolio optimization. We propose a Quantum Ridgelet Neural Network (QRNN) model for forecasting time-series data that integrates Parametrized Quantum Circuits (PQCs) with ridgelet-based feature transformations and QAOA-based portfolio optimization for asset selection. By breaking down financial time-series data into multi-resolution components, the ridgelet transform enables the identification of both local and global trends. Ridgelet-based features improve the scalability and accuracy of quantum computing by significantly reducing the number of qubits needed. However, the predicted results are turned into a QUBO-based mean-variance optimization problem and solved with QAOA to select the best stocks. Our study begins with a theoretical formulation of the single-qubit system for our proposed model. This formulation is further extended to a multi-qubit system, and we show that it captures a significant fraction of the predictive signal. |
| title | Hybrid Quantum-Classical Ridgelet Neural Networks for Portfolio Optimization |
| topic | Machine Learning Optimization and Control Quantum Algebra |
| url | https://arxiv.org/abs/2601.03654 |