Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2024

Abstract

We extend the qubit-efficient encoding presented in (Tan et al. in Quantum 5:454, 2021) and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also propose optimality-preserving methods to reduce variance in real-world data and substitute continuous slack variables. We benchmark our methods against standard QAOA for problems consisting of 16 transactions and obtain competitive results. Our newly proposed variational ansatz performs best overall. We demonstrate tackling problems with 128 transactions on real quantum hardware, exceeding previous results bounded by NISQ hardware by almost two orders of magnitude.

Keywords

Mixed binary optimization, NISQ, Quantum Computing, Quantum Finance, Quantum Optimization, Qubit reduction, QUBO

Discipline

Finance and Financial Management | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

EPJ Quantum Technology

Volume

11

Issue

1

First Page

1

Last Page

36

ISSN

2662-4400

Identifier

10.1140/epjqt/s40507-024-00262-w

Publisher

SpringerOpen

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1140/epjqt/s40507-024-00262-w

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