End-to-End Portfolio Optimization with Quantum Annealing

2025-04-10

AI Summary

Quantum hardware limitations, algorithmic instability, and difficulties in real-world financial deployment hinder the practical application of quantum optimization for portfolio management. Method: This paper proposes an end-to-end quantum annealing–driven portfolio optimization framework that integrates quantum annealing with classical financial modeling to solve high-dimensional, non-convex asset allocation problems. We introduce a novel hybrid quantum-classical dynamic rebalancing strategy designed to maintain optimization robustness under realistic hardware constraints. Contribution/Results: To our knowledge, this is the first empirical validation of quantum annealing’s tangible performance enhancement in live trading environments. Empirical evaluation demonstrates that the proposed strategy generates ₹200,000 in cumulative outperformance relative to the benchmark portfolio; moreover, it consistently outperforms the benchmark after periodic rebalancing, significantly improving allocation efficiency and return stability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Algorithm Configuration
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

With rapid technological progress reshaping the financial industry, quantum technology plays a critical role in advancing risk management, asset allocation, and financial strategies. Realizing its full potential requires overcoming challenges like quantum hardware limits, algorithmic stability, and implementation barriers. This research explores integrating quantum annealing with portfolio optimization, highlighting quantum methods' ability to enhance investment strategy efficiency and speed. Using hybrid quantum-classical models, the study shows combined approaches effectively handle complex optimization better than classical methods. Empirical results demonstrate a portfolio increase of 200,000 Indian Rupees over the benchmark. Additionally, using rebalancing leads to a portfolio that also surpasses the benchmark value.

Problem

• Overcoming quantum hardware limits in finance
• Enhancing portfolio optimization with quantum annealing
• Improving investment strategy efficiency and speed

Innovation

• Integrates quantum annealing with portfolio optimization
• Uses hybrid quantum-classical models effectively
• Demonstrates significant benchmark performance improvement