Genetically Engineered Quantum Circuits for Financial Market Indicators

TL;DR

This paper introduces a genetic algorithm-based method to optimize the encoding of stock market data into quantum states, improving fidelity and efficiency for financial analysis on NISQ quantum computers.

Contribution

It presents the GASP framework for enhanced encoding of financial data into quantum states, demonstrating improved fidelity and potential for precise quantum financial analysis.

Findings

Improved fidelity in quantum encoding of stock data.

Successful implementation on simulated and real quantum hardware.

Enhanced potential for quantum advantage in financial analysis.

Abstract

Quantum computing holds immense potential for transforming financial analysis and decision-making. Realising this potential necessitates the efficient encoding and processing of financial data on quantum computers. In this study, we propose using the GASP (Genetic Algorithm for State Preparation) framework to optimise the encoding of stock price data into quantum states and show it can enhance both the fidelity and efficiency of the encoding process. We demonstrate the efficacy of our approach by encoding stock price data onto both a simulated and real quantum computer to calculate the Singular Value Decomposition (SVD) entropy. Our results show improvements in fidelity and the potential for more precise financial analysis. This research provides insights into the applicability of GASP for the efficient encoding of real-world data, specifically stock price data, which is crucial for quantum advantage on noisy intermediate-scale quantum (NISQ) era quantum computers.