AWS and JPMorganChase Study Smaller Inputs for Quantum Optimization
Their research combines classical preprocessing and problem decomposition with quantum workflows to reduce the burden placed on hardware.
Smaller portfolio subproblems improve a classical baseline
- Reported
- Acharya and colleagues report portfolio decomposition that reduces subproblem size by about 80% in their tested data.
- Supported observation
- For linear-constraint problems around 1,500 assets, the study reports roughly threefold runtime savings with solution quality within 5% of the optimum.
- Conditions
- The checked v2 preprint reports Gurobi benchmarks, sequential subproblem solving and explicit approximation gaps. Its decomposition uses correlation preprocessing, clustering and risk rebalancing. Timing compares decomposed and direct optimization using the same classical solver. It is not a quantum-versus-classical timing result.
Results depend on the tested data and constraints. Potential compatibility with smaller quantum devices does not demonstrate quantum speedup or production portfolio returns.
- Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum ComputingarXiv · Primary research preprint, version 2 · Source published Source checked
- AWS and JPMorganChase collaborate to advance quantum computing R&DAWS Quantum Technologies Blog · Primary collaboration report · Source published Source checked
Inspect the exact evidence and citation
This source-locator check adds context to the existing record. Its original publication and claim-review dates remain unchanged. No independent scientific replication was performed.
- Evidence context
- Reported classical numerical benchmark
- Independent replication
- not assessed. This describes what was assessed, rather than evidence that replication failed.
- Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing →
Where to look: Abstract; Introduction, Our contribution; IV Numerical Results
- Cited version
- arXiv:2409.10301v2, 20 November 2024
- Source origin
- Organization and collaborator originated
- Publication stage
- Version 2 preprint; related journal article listed separately
- Primary evidence for
- The tested decomposition method and its comparison with the same classical solver.
- Does not establish
- Quantum speedup, investment returns or identity with every result in the related journal version.
Four dates, four meanings
- Underlying event
- Numerical experiment date not separately established
- Source publication
- · arXiv
- Record first published
- Original claim review
- · Attributed primary-source summary; no independent scientific review.
Connected current views
These selected views resolve the same ledger claim. Historical citations keep their cited revision.
Selectable text with sources
Atithi Acharya et al.. Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing. arXiv. Source published 2024-11-20. arXiv:2409.10301v2, 20 November 2024. Abstract; Introduction, Our contribution; IV Numerical Results. https://arxiv.org/html/2409.10301v2#S4 Source authority: The tested decomposition method and its comparison with the same classical solver. Does not establish: Quantum speedup, investment returns or identity with every result in the related journal version. QubitWire: Smaller portfolio subproblems improve a classical baseline. Record first published 2026-09-26. Cited revision claim-portfolio-decomposition-classical-baseline-r1. https://qubitwire.com/evidence/portfolio-decomposition-classical-baseline?revision=claim-portfolio-decomposition-classical-baseline-r1 Current record and corrections: https://qubitwire.com/evidence/portfolio-decomposition-classical-baseline Limitation: Results depend on the tested data and constraints. Potential compatibility with smaller quantum devices does not demonstrate quantum speedup or production portfolio returns.
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AWS and JPMorganChase are studying how to give quantum hardware a smaller problem to solve. Their collaboration report describes three studies using classical preprocessing and problem decomposition for quantum optimization. Reducing the workload before quantum execution can make limited hardware useful for larger original tasks.
In one portfolio-optimization workflow, the report describes about an 80% reduction in problem size and a threefold reduction in solution time on the tested instances.
The figures belong to that specific workflow and its comparisons. They neither establish a general quantum speedup nor remove the cost of classical preprocessing. A fuller assessment would include preprocessing time and quantum execution, with complete workflows measured against classical alternatives on representative instances.