Hybrid Quantum-Classical Portfolio Optimization Leveraging Counterdiabatic Annealing and Quantum Risk Factors
Year: 2025
Type: preprint
Abstract: We present a rigorously derived hybrid quantumclassical portfolio optimization framework integrating counterdiabatic quantum annealing acceleration and quantum risk factors informed by entanglement surrogates. Our novel Quadratic Unconstrained Binary Optimization (QUBO) formulation incorporates classical mean-variance objectives complemented by a quantum risk matrix derived from von Neumann entropies of parametrized two-qubit states simulated using Qiskit. We prove penalty parameter bounds ensuring feasibility, and theorems guaranteeing convergence accelerated by counterdiabatic driving. The hybrid algorithm combines industry-grade D-Wave Advantage quantum annealing with gate-model quantum simulation for risk modeling, validated on real market data with extensive parameter calibration, robustness, and ablation studies. Comprehensive complexity analysis and quantum circuit representations complement experiment results evidencing superior portfolio performance and convergence guarantees. All counterdiabatic schedule implementations and their impacts presented herein are derived from high-fidelity simulations and emulations on classical hardware, while baseline results were obtained using physical runs on the D-Wave Advantage system1.1 device with standard annealing schedules. This distinction is maintained throughout the manuscript to facilitate accurate interpretation of CD effects. This work pioneers rigorous, practical quantum-enhanced portfolio management integrating theory with cutting-edge hardware.
Author: Yalla Jnan Devi Satya Prasad
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414200209
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