locale: en title: "AI, Big Data & Quantum Finance" slug: discipline-ai-markets summary: "Machine learning, big data, LLMs and quantum computing in finance — with limits and governance." status: published pillar: hub discipline_id: ai-markets tagline: "Data, models and new computation in markets." translation_of: it/hub/discipline-ai-mercati updated: 2026-06-23

AI, Big Data & Quantum Finance

Data, models and new computation in markets.

This is not “AI that predicts the market”. These are tools for analysis, features, risk, execution and research — with real risks: overfitting, black boxes, data leakage, fragility and governance.

What it's for

This discipline studies how large data sets, machine learning and advanced computation (including quantum in research phase) support analysis, forecasting, risk management, execution and strategy building.

Area	Applications	Limits
Classical ML	Features, classification, regression	Overfitting, regime change
Deep learning / LLM	News NLP, agents, summarisation	Black box, hallucination
Alternative data	Satellite, card, web, social	Cost, quality, survivorship
Quantum (research)	Portfolio opt, pricing, Monte Carlo	Immature hardware, hybrid classical

In quantum finance the most studied areas are portfolio optimisation, derivatives pricing, Monte Carlo simulations and quantum ML — today often in hybrid workflows, not immediate replacement of classical systems.

Core concepts

Term	In Cyclepedia
AI / ML / deep learning	quantitative-trading
Big data / alternative data	entries coming
NLP / sentiment / news analytics	entries coming
Feature engineering / selection	entries coming
Train / test / validation set	out-of-sample · sample-size
Cross-validation / data leakage	overfitting · backtest
Overfitting / black box / XAI	overfitting
Regime detection / anomaly detection	market-regime · regime-shift
Quantum computing / QML	entries coming
Post-quantum security	entry coming

Infrastructure: matching-engine · execution-latency · market-maker

Tool	Role
Data pipeline (tick → feature)	ML preparation
ML backtest with purge/embargo	Avoid leakage
LLM + RAG on financial documents	Assisted research
Agentic workflows	Analysis automation (with supervision)
Quantum simulators (Qiskit, etc.)	QML prototypes
MLOps / drift monitoring	Model production

Researchers, desks and institutions

Figure / entity	Contribution
Jim Simons	Renaissance, statistics at scale
Marcos López de Prado	ML in finance, validation
D.E. Shaw / David Shaw	Computational finance
Two Sigma / AQR	Quant + alternative data
Doyne Farmer / J.-P. Bouchaud	Complexity and microstructure
IBM Quantum / academic research	Quantum finance prototypes
Patrick Rebentrost, Seth Lloyd, Marco Pistoia	Quantum algorithms for finance

Recommended path

quantitative-trading — quant foundation
backtest · sample-size — data and samples
discipline-systematic-trading — models and metrics
overfitting · out-of-sample — ML backtest and leakage
forward-test · paper-trading — validation
market-regime — context and drift
anti-scam — risks, fake bots, unrealistic promises

Prerequisite: discipline-systematic-trading.