End-to-End Portfolio Optimization with Quantum Annealing
Sai Nandan Morapakula, Sangram Deshpande, Rakesh Yata, Rushikesh Ubale, Uday Wad, Kazuki Ikeda
TL;DR
This work demonstrates an end-to-end hybrid quantum-classical portfolio optimization pipeline that maps continuous mean-variance and Sharpe-ratio objectives into discrete QUBO/Ising problems for asset selection, while leveraging classical optimization for weight allocation and a quarterly rebalancing mechanism. By integrating D-Wave's hybrid solver with traditional optimization steps, the study assesses feasibility, reproducibility, and real-world applicability using Indian market data, without claiming quantum supremacy. Key contributions include a practical end-to-end workflow, a two-step cardinality determination via convex optimization, and demonstrations of competitive performance relative to benchmarks. The results highlight both the potential of quantum-assisted selection and the current hardware-induced limitations, setting the stage for scalable, hybrid workflows as annealing technology matures.
Abstract
Hybrid-quantum classical optimization has emerged as a promising direction for addressing financial decision problems under current quantum hardware constraints. In this work we present a practical end-to-end portfolio optimization pipeline that combines (i) a continuous mean-variance and Sharpe-ratio formulation, (ii) a QUBO/CQM-based discrete asset selection stage solved using D-Wave's hybrid quantum annealing solver, (iii) classical convex optimization for computing optimal asset weights, and (iv) a quarterly rebalancing mechanism. Rather than claiming quantum advantage, our goal is to evaluate the feasibility and integration of these components within a deployable financial workflow. We empirically compare our hybrid pipeline against a fund manager in real time and indexes used in Indian stock market. The results indicate that the proposed framework can construct diversified portfolios and achieve competitive returns. We also report computational considerations and scalability observations drawn from the hybrid solver behaviour. While the experiments are limited to moderate sized portfolios dictated by current annealing hardware and QUBO embedding constraints, the study illustrates how quantum assisted selection and classical allocation can be combined coherently in a real-world setting. This work emphasizes methodological reproducibility and practical applicability, and aims to serve as a step toward larger-scale financial optimization workflows as quantum annealers continue to mature.