Smaller portfolio subproblems improve a classical baseline
A dated, source-bounded reading of the original report.
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.
Not established
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