We extend the qubit-efficient encoding presented in [Tan et al., 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.Comment: 16 pages, 8 figure