Large-scale portfolio optimization on a trapped-ion quantum computer
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915823296184320 |
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| author | Cadavid, Alejandro Gomez Kaushik, Ananth Chandarana, Pranav Lopez-Ruiz, Miguel Angel Dev, Gaurav Aboumrad, Willie Zhang, Qi Girotto, Claudio Romero, Sebastián V. Roetteler, Martin Solano, Enrique Pistoia, Marco Hegade, Narendra N. |
| author_facet | Cadavid, Alejandro Gomez Kaushik, Ananth Chandarana, Pranav Lopez-Ruiz, Miguel Angel Dev, Gaurav Aboumrad, Willie Zhang, Qi Girotto, Claudio Romero, Sebastián V. Roetteler, Martin Solano, Enrique Pistoia, Marco Hegade, Narendra N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23976 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-scale portfolio optimization on a trapped-ion quantum computer Cadavid, Alejandro Gomez Kaushik, Ananth Chandarana, Pranav Lopez-Ruiz, Miguel Angel Dev, Gaurav Aboumrad, Willie Zhang, Qi Girotto, Claudio Romero, Sebastián V. Roetteler, Martin Solano, Enrique Pistoia, Marco Hegade, Narendra N. Quantum Physics 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. |
| title | Large-scale portfolio optimization on a trapped-ion quantum computer |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2602.23976 |