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Quantum computing in finance: Portfolio optimization case studies.

The finance industry has been cautiously exploring quantum computing for portfolio optimization—one of the few problems where quantum advantage might actually matter. But beyond the press releases, what do the early case studies actually show?

The Promise vs. Reality

In theory, quantum algorithms like QAOA or VQE could outperform classical methods for:

1. Risk-return balancing (Markowitz portfolio optimization)
2. Constrained asset allocation (regulatory/ESG constraints)
3. High-frequency rebalancing (combinatorial optimization)

But real implementations reveal complications:

Notable Industry Trials

1. Goldman Sachs & QCWare (2024)
2. Tested quantum algorithms for derivative pricing and portfolio optimization
3. Result: Simulated quantum models matched classical performance in some cases, but hardware limitations made real-world deployment impractical
4. Key insight: Hybrid quantum-classical approaches worked best
5. JPMorgan Chase & IBM (2023-2025)
6. Ran portfolio optimization on 127-qubit IBM Eagle processors
7. Found quantum solutions were competitive for small portfolios (<50 assets)
8. Bottleneck: Noise required excessive error mitigation, negating speedup
9. Rigetti & AQR Capital (2024)
10. Focused on constrained optimization with 80+ variables
11. Achieved better Sharpe ratios than classical solvers for specific asset classes
12. Catch: Only worked for highly curated datasets

The Current Verdict

1. NISQ-era usefulness: Limited to small, clean problems
2. Best near-term value: Quantum-inspired algorithms running on classical hardware
3. Long-term hope: Fault-tolerant quantum computers could change the game

What's missing from public case studies?

1. Detailed comparisons to classical heuristics
2. Real transaction cost modeling
3. Benchmarks on truly large portfolios (500+ assets)