Preparing random state for quantum financing with quantum walks

TL;DR

This paper introduces a method using split-step quantum walks to efficiently prepare quantum states for quantum finance applications, enabling better data loading and distribution optimization in quantum computing.

Contribution

It proposes a novel approach combining split-step quantum walks with parameterized quantum circuits for state preparation in quantum finance, with an open-source implementation.

Findings

Demonstrates the effectiveness of SSQW in generating desired probability distributions.

Provides an open-source implementation using Qiskit.

Highlights potential for quantum simulation in option pricing.

Abstract

In recent years, there has been an emerging trend of combining two innovations in computer science and physics to achieve better computation capability. Exploring the potential of quantum computation to achieve highly efficient performance in various tasks is a vital development in engineering and a valuable question in sciences, as it has a significant potential to provide exponential speedups for technologically complex problems that are specifically advantageous to quantum computers. However, one key issue in unleashing this potential is constructing an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. Therefore, the split-step quantum walks (SSQW) algorithm was proposed to address this limitation. We facilitate SSQW to design parameterized quantum circuits (PQC) that can generate probability distributions and optimize the parameters to achieve the desired distribution using a variational solver. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.