Paper: arXiv 2604.08180

Authors: Hui Gong, Akash Sedai, Thomas Schroeder, Francesca Medda

Abstract

Quantum computing is becoming strategically relevant to finance because several core financial bottlenecks are already defined by combinatorial search, expectation estimation, rare-event analysis, representation learning, and long-horizon cryptographic resilience. This review examines that landscape across five connected domains: constrained portfolio optimisation, derivative pricing, tail-risk and scenario estimation, quantum machine learning, and post-quantum security. Rather than treating these topics as isolated demonstrations, the article studies them as linked layers of a financial-computation stack. Across all five domains, the review applies a common evaluative logic: identify the financial bottleneck, specify the relevant quantum primitive, compare it with an explicit classical benchmark, and assess the result under realistic implementation and governance constraints. The main conclusion is measured but consequential. The strongest near-term case for quantum finance lies in carefully designed hybrid workflows rather than blanket claims of universal advantage. Quantum optimisation is most credible when constrained search dominates; amplitude-estimation methods matter most when repeated expectation evaluation is the binding cost; quantum machine learning remains task dependent; and post-quantum cryptography is already strategically necessary because financial infrastructures must migrate before fault-tolerant attacks arrive. By combining system-level synthesis with locally reproducible small-scale case studies on simulated qubit registers, the article is intended both as a review of the field and as a handbook-style entry point for future work.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper provides a comprehensive, structured overview of quantum computing applications across the quantitative finance stack, supported by simulated small-scale case studies. While it functions primarily as a handbook-style literature review rather than introducing a single novel empirical pipeline, its system-level synthesis and mathematical framework are rigorous and highly actionable.

Research Flowchart

  flowchart TD
    A["Research Goal: Evaluate Quantum Computing across Financial Bottlenecks"] --> B["Apply Common Evaluative Logic across 5 Domains"]
    
    B --> C["Data & Inputs: Simulated Qubit Registers & Hybrid Workflows"]
    
    C --> D["Computational Processes: Quantum Primitives vs. Classical Benchmarks"]
    
    D --> E["Domain Evaluation: Optimisation, Pricing, Risk, QML & PQC"]
    
    E --> F["Key Outcomes: Near-term Hybrid Workflows & Immediate PQC Migration Needed"]