A novel approach for quantum financial simulation and quantum state preparation

TL;DR

This paper introduces the multi-Split-Steps Quantum Walk (multi-SSQW), a novel quantum simulation algorithm that models complex financial distributions with high accuracy, stability, and rapid computation, leveraging quantum computing for financial analysis.

Contribution

The paper presents a new multi-SSQW algorithm that enhances quantum state preparation and financial market modeling using a multi-agent quantum walk approach with variational circuits.

Findings

Demonstrates high accuracy in simulating financial distributions

Shows stable convergence and rapid computation in models

Provides valuable insights for financial decision-making

Abstract

Quantum state preparation is vital in quantum computing and information processing. The ability to accurately and reliably prepare specific quantum states is essential for various applications. One of the promising applications of quantum computers is quantum simulation. This requires preparing a quantum state representing the system we are trying to simulate. This research introduces a novel simulation algorithm, the multi-Split-Steps Quantum Walk (multi-SSQW), designed to learn and load complicated probability distributions using parameterized quantum circuits (PQC) with a variational solver on classical simulators. The multi-SSQW algorithm is a modified version of the split-steps quantum walk, enhanced to incorporate a multi-agent decision-making process, rendering it suitable for modeling financial markets. The study provides theoretical descriptions and empirical investigations of the multi-SSQW algorithm to demonstrate its promising capabilities in probability distribution simulation and financial market modeling. Harnessing the advantages of quantum computation, the multi-SSQW models complex financial distributions and scenarios with high accuracy, providing valuable insights and mechanisms for financial analysis and decision-making. The multi-SSQW's key benefits include its modeling flexibility, stable convergence, and instantaneous computation. These advantages underscore its rapid modeling and prediction potential in dynamic financial markets.