Chapter 9: Quantum Computing for Finance

Natural language processing in finance is redefining how institutions analyze text data, assess risk, and extract insights from markets. Quantum computing, which allows machines to explore many possibilities in parallel so certain tasks can run dramatically faster than on today’s computers, will not instantly transform finance — but that day is coming, and practitioners should plan for it, according to the author of this chapter of AI in Asset Management: Tools, Applications, and Frontiers.

The author argues that quantum computing will not remake finance overnight, but firms can gain near-term value from hybrid quantum–classical methods for hard optimization and simulation while preparing for quantum-safe security. In summary, the authors suggest that practitioners experiment pragmatically now (portfolio optimization, Monte Carlo, targeted machine learning) and begin their shift to post-quantum cryptography.

Firms that begin testing mixed quantum-and-classical methods will grab early wins (faster optimization and simulations) and reduce cyber risk. Reliable, large-scale quantum computers are still far off, so near-term benefits will come from practical, small-scale quantum techniques and a careful shift to new, post-quantum encryption.

This chapter shows what the move to quantum means in practice and refreshes machine learning (ML) basics — supervised, unsupervised, and neural nets — behind credit scoring, fraud detection, market/risk analytics, and portfolio construction. It spotlights the workhorses: k-Nearest Neighbor (kNN) for credit and fraud calls via nearest-neighbor similarity; k-means to flag anomalies and surface anti-money-laundering (AML) patterns; and principal component analysis (PCA) to compress correlated factors for cleaner risk and smarter allocation.

• We’re in the NISQ (noisy intermediate-scale quantum) era. Practitioners should plan around today’s quantum machines being small and noisy. Expect fault-tolerant systems to take years, and do not rely on them for now.
• Chase near-term wins with hybrids. Practitioners should run hybrid quantum–classical pilots (e.g., Quantum Approximate Optimization Algorithm/Variational Quantum Eigensolver, or QAOA/VQE) for tough optimization problems, and benchmark them hard against strong classical baselines.
• Treat quantum machine learning (QML) as R&D. Users should focus on cracking the data-encoding bottleneck and keep quantum ML in the lab — not in production — until it proves itself.
• Prioritize Monte Carlo. Target quantum-enhanced Monte Carlo first; theory (e.g., amplitude estimation) points to real speedups when hardware is ready.
• Move on security now. Practitioners should begin migrating to post-quantum cryptography to counter future breaks of RSA/ECC (Rivest–Shamir–Adleman/Elliptic-Curve Cryptography); reserve quantum key distribution (QKD) for niche, ultra-secure links, and track National Institute of Standards and Technology’s (NIST’s) 2030–2035 milestones.
• Make data and governance your edge. Build clean pipelines and run rigorous A/B tests with a small cross-functional team to verify when (and if) quantum beats classical in practice.

• Tackling optimization problems at scale. Finance runs on hard, constraint-heavy choices (e.g., portfolio picks). Hybrid quantum–classical methods (QAOA, VQE) search those spaces differently and could beat classical heuristics. Practitioners should start with small NISQ pilots and measure results.
• Boosting machine learning and simulation. Quantum ML is in its early days but intriguing. The near-term standout is Monte Carlo — quantum techniques can cut required samples, speeding pricing and risk runs once hardware allows.
• Securing financial data. Future quantum machines will break RSA/ECC. Firms should start migrating to post-quantum cryptography now, keeping QKD for rare, ultra-secure links.

The following exhibit explores the motivations for practitioners in various roles.

Quantum is not yet transforming finance, but ignoring it risks being behind on security and missing a head start on practical use cases where speedups will eventually be decisive. Quantum computing in finance matters now for two reasons:

• Security risk is immediate. Even though large fault-tolerant quantum computers do not yet exist, they will eventually break today’s standard encryption (RSA, ECC). Adversaries can already harvest encrypted financial data today and decrypt it later. That makes migrating to post-quantum cryptography (PQC) urgent.
• Early advantage comes from learning. Although the hardware is still noisy and small (NISQ era), firms that experiment with hybrid quantum–classical algorithms now — especially in portfolio optimization, Monte Carlo risk simulation, and machine learning — will build expertise, IP, and infrastructure ahead of competitors. This creates “option value”: the ability to scale quickly once more powerful quantum machines arrive.

This chapter demonstrates that quantum computing threatens today’s encryption. As such, the author suggests that firms start migrating to post-quantum cryptography now to avoid “harvest-now, decrypt-later” risk. 

Quantum computing will not overhaul finance overnight, but this chapter directly addresses the industry’s biggest computational pain points — optimization, simulation, and security. It suggests that in the short term, firms that experiment with hybrid algorithms and start migrating to quantum-safe security will be best prepared to capture upside and manage risk as the technology matures.

This summary is based on the CFA Institute Research Foundation and CFA Institute Research and Policy Center chapter “Quantum Computing for Finance,” by Oswaldo Zapata, PhD, which explores how quantum computing fits finance.

When will quantum matter for my business?
It matters now for security (start moving to post-quantum cryptography) and for learning (run small hybrid pilots). Do not expect fault-tolerant breakthroughs soon; focus on practical NISQ-era experiments that build skills and IP.

What use cases should we try first?
Start with Monte Carlo for pricing and risk (most theory-backed upside), then discrete optimization (e.g., constrained portfolio selection). Treat QML as exploratory—use it where feature encoding is manageable and classical baselines are strong.

How do we measure if quantum helps?
Do A/B benchmarks against tuned classical solvers with equal engineering effort. Track clear KPIs: time-to-solution, solution quality (P&L/risk), cost, and stability under noise, including data loading and error-mitigation overhead.

What should we do on security today?
Begin a PQC migration: Inventory crypto, prioritize high-value data, and roll out hybrid classical+PQC schemes. Keep QKD for niche, ultra-secure links; for most firms, PQC plus strong key management is the right path.

What team and tools do we need?
Stand up a small cross-functional squad: quant finance + quantum algorithms + software/DevOps + security. Use hybrid-friendly software development kits, classical emulators/HPC, clean data pipelines, and a vendor plan that avoids lock-in.

What is the biggest technical hurdle in QML?
Data encoding. Getting classical features efficiently into qubits is hard and can erase theoretical gains. Start with simple schemes (e.g., angle encoding), limit feature counts, and prove value on narrow problems before you scale.