Problem
Research questions and friction points this paper is trying to address.
quantum computing
financial optimisation
derivative pricing
tail-risk estimation
post-quantum security
Quantum Computing for Financial Transformation: A Review of Optimisation, Pricing, Risk, Machine Learning, and Post-Quantum Security
🤖 AI Summary
This study addresses core computational bottlenecks in finance—spanning portfolio optimization, derivative pricing, tail risk assessment, machine learning, and post-quantum security—by proposing a hierarchical, stack-based analytical framework. The framework systematically aligns quantum primitives with financial problems, integrates hybrid quantum-classical workflows, and establishes a reproducible evaluation paradigm grounded in small-scale simulations and real-world constraints. Encompassing quantum optimization, amplitude estimation, quantum machine learning, and post-quantum cryptography, the work identifies the most promising near-term applications: constrained search optimization, nested expectation estimation, and migration to post-quantum secure systems. These insights provide both theoretical grounding and practical guidance for advancing the real-world deployment of quantum technologies in finance.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
quantum finance
hybrid quantum-classical workflows
financial computation stack
amplitude estimation
post-quantum cryptography
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