Large-scale portfolio optimization on a trapped-ion quantum computer

TL;DR

This paper introduces a comprehensive quantum computing pipeline for large-scale portfolio optimization, demonstrating its effectiveness on trapped-ion quantum processors and highlighting improvements in solution quality with larger subproblem sizes.

Contribution

It presents a novel end-to-end quantum optimization workflow for finance, combining correlation analysis, hardware-aware decomposition, and non-variational quantum algorithms on real hardware.

Findings

Larger subproblem sizes improve solution quality and reduce decomposition errors.

The workflow successfully runs on a 64-qubit trapped-ion quantum processor.

Hardware-aware decomposition enhances scalability and performance in quantum financial optimization.

Abstract

We present an end-to-end pipeline for large-scale portfolio selection with cardinality constraints and experimentally demonstrate it on trapped-ion quantum processors using hardware-aware decomposition. Building on RMT-based correlation-matrix denoising and community detection, we identify correlated asset groups and introduce a correlation-guided greedy splitting scheme that caps each cluster by the executable qubit budget. Each cluster defines a hardware-embeddable QUBO subproblem that we solve using bias-field digitized counterdiabatic quantum optimization (BF-DCQO), a non-variational method that avoids classical parameter-training loops. We recombine low-energy candidates into global portfolios and enforce feasibility with a two-stage post-processing routine: fast repair followed by a cardinality-preserving swap local search. We benchmark the workflow on a 250-asset universe taken from the S&P 500 and execute subproblems on a 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. We observe that larger executable subproblem sizes reduce decomposition error and systematically improve final objective values and risk-return trade-offs relative to randomized baselines under identical post-processing. Overall, the results establish a hardware-tested route for scaling financial optimization problems, defined by a trade space in which executable problem size and circuit cost are balanced against the resulting solution quality.