https://doi.org/10.1140/epjqt/s40507-024-00262-w
Research
Exponential qubit reduction in optimization for financial transaction settlement
1
D-CHAB Interdisciplinary Sciences, ETH Zürich, Vladimir-Prelog-Weg 1-5/10, 8093, Zurich, Switzerland
2
Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543, Singapore, Singapore
3
AngelQ Quantum Computing, 531A Upper Cross Street, 04-95 Hong Lim Complex, 051531, Singapore, Singapore
4
School of Computing and Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore, Singapore
5
School of Electrical and Computer Engineering, Technical University of Crete, 73100, Chania, Greece
Received:
27
March
2024
Accepted:
29
July
2024
Published online:
14
August
2024
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.
Key words: Quantum Computing / Quantum Optimization / NISQ / QUBO / Mixed binary optimization / Quantum Finance / Qubit reduction
© The Author(s) 2024
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