quantum-portfolio-benchmark-audit

name	quantum-portfolio-benchmark-audit
description	Critical benchmark methodology for evaluating quantum portfolio optimization claims. Provides systematic comparison framework testing quantum annealing and QAOA against classical solvers (MIP, simulated annealing, tabu search, problem-tailored heuristics) on real-world instances up to 1,000 assets. Key finding: classical MIP solves all instances to proven optimality in seconds; problem-tailored heuristics consistently outperform quantum approaches in solution quality for fixed runtime. Use when: evaluating quantum advantage claims for portfolio optimization, benchmarking quantum vs classical solvers, designing fair quantum-classical comparisons.
license	Complete terms in LICENSE.txt
metadata	{"arxiv_id":"2509.17876","published":"2025-09-22","authors":"Eric Stopfer, Friedrich Wagner","tags":["quantum","portfolio","optimization","benchmark","classical-comparison","quantum-annealing","qaoa","mixed-integer-programming","quantum-advantage-audit"]}

Quantum Portfolio Benchmark Audit

Overview

Critical benchmark methodology for evaluating quantum portfolio optimization claims against state-of-the-art classical solvers. Demonstrates that for practical portfolio optimization, classical mixed-integer programming and problem-tailored heuristics significantly outperform quantum approaches (quantum annealing, QAOA) on real-world instances up to 1,000 assets.

Source

• Paper: "Quantum Portfolio Optimization: An Extensive Benchmark"
• arXiv: 2509.17876 (Sep 2025)
• Authors: Eric Stopfer, Friedrich Wagner
• Enhanced: 2026-06-14 - Added Expert Analysis Evaluation cross-reference (arXiv:2507.20532) and Hot-Starting methodology (arXiv:2510.11153)

Core Methodology

1. Benchmark Instance Design

• 250 portfolio optimization instances from actual stock data
• Up to 1,000 assets per instance
• Variant of portfolio optimization particularly difficult for classical solvers
• Real-world data, not synthetic benchmarks

2. Classical Baselines (Must-Compare)

Any quantum portfolio optimization study MUST compare against:

• Mixed-Integer Programming (MIP): Proven optimality in seconds for all instances
• Simulated Annealing: General-purpose metaheuristic
• Steepest Descent Local Search: Simple but effective baseline
• Tabu Search: Memory-enhanced local search
• Problem-Tailored Heuristics: Custom algorithms exploiting problem structure

3. Quantum Approaches Tested

• Quantum Annealing: D-Wave Advantage quantum annealer
• QAOA: Quantum Approximate Optimization Algorithm

4. Evaluation Metrics

• Solution Quality: Optimality gap from proven optimal
• Runtime: Fixed runtime comparison
• Scalability: Performance as asset count increases
• Proven Optimality: Whether solver can certify optimality

5. Key Findings

• MIP solves ALL instances to proven optimality in seconds
• Problem-tailored heuristic consistently outperforms quantum approaches
• Very limited room for potential quantum advantage in portfolio optimization
• Classical solvers dominate in both solution quality and runtime

Benchmark Protocol

Step 1: Define Problem Instance

Input: N assets, return matrix R, covariance matrix Sigma
Constraints: Cardinality, budget, sector limits
Objective: Minimize risk (variance) subject to return target

Step 2: Establish Classical Baselines

# MIP baseline (Gurobi/CPLEX)
model = mip_model(assets, returns, covariances, constraints)
solution, gap, time = model.solve()

# Heuristic baselines
sa_solution = simulated_annealing(assets, returns, covariances)
tabu_solution = tabu_search(assets, returns, covariances)
custom_solution = problem_tailored_heuristic(assets, returns, covariances)

Step 3: Run Quantum Solvers

# Quantum Annealing
qa_solution = quantum_annealer(assets, returns, covariances, QPU=QPU_DWAVE)

# QAOA
qaoa_solution = qaoa_solver(assets, returns, covariances, p_depth=p)

Step 4: Compare Results

• Optimality gap for each method
• Runtime comparison
• Solution quality under fixed time budget
• Scalability analysis (10, 50, 100, 500, 1000 assets)

Critical Evaluation Checklist

When reviewing quantum portfolio optimization papers, check:

1. Are classical baselines included? (MIP, simulated annealing, tabu search)
2. Is problem-tailored heuristic tested?
3. Are instances from real-world data?
4. Is solution quality measured against proven optimal?
5. Is runtime comparison fair (fixed time budget)?
6. Is scalability analyzed across instance sizes?
7. Are problem-tailored classical algorithms considered?

Applications

• Quantum Advantage Verification: Critical assessment of quantum portfolio claims
• Algorithm Benchmarking: Fair comparison framework for quantum vs classical solvers
• Investment Decision: When to use quantum vs classical for portfolio problems
• Research Quality Control: Preventing over-claiming of quantum advantage

Pitfalls

1. Over-Claiming: Many papers claim quantum advantage without proper classical baselines
2. Synthetic Instances: Synthetic benchmarks may not reflect real-world difficulty
3. Small Instances: Quantum may work for tiny instances but classical dominates at scale
4. Unfair Comparison: Comparing against naive classical algorithms, not state-of-the-art
5. Ignoring Heuristics: Problem-tailored heuristics often outperform both quantum and MIP

Activation

• When: Evaluating quantum portfolio optimization papers, designing benchmark studies, assessing quantum advantage claims, comparing quantum vs classical solvers
• Keywords: quantum portfolio benchmark, portfolio optimization audit, quantum vs classical comparison, quantum advantage verification, portfolio benchmarking, QAOA portfolio audit, quantum annealing portfolio comparison

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