Securities Transaction Settlement Optimization on superconducting quantum devices

TL;DR

This paper presents a quantum variational algorithm tailored for securities transaction settlement optimization, leveraging superconducting quantum devices and novel constraint encoding to improve performance on real transactional data.

Contribution

It introduces a new quantum algorithm with constraint encoding and noise mitigation for securities settlement, specifically designed for superconducting quantum hardware.

Findings

Benchmarking on real data shows competitive performance.

Customized noise mitigation improves solution quality.

Algorithm effectively encodes complex settlement constraints.

Abstract

We describe a quantum variational algorithm for securities transactions settlement optimization, based on a novel mathematical formalization of the problem that includes the most relevant constraints considered in the pan-European securities settlement platform TARGET2-Securities. The proposed algorithm is designed for Noisy Intermediate-Scale Quantum devices, specifically targeting IBM's superconducting qubit machines. We adopt non-linear activation functions to encode inequality constraints in the objective function of the problem, and design customized noise mitigation techniques to alleviate the effect of readout errors. We consider batches of up to 40 trades obtained from real transactional data to benchmark our algorithm on quantum hardware against classical and quantum-inspired solvers.