Quantum Finance

Finance is one of the most practical entry points for quantum computing. The videos in this section show how portfolio optimization, option pricing, and fraud detection can be formulated as quantum problems.

Many videos are supported by notebooks so you can follow the code, understand the algorithms, and evaluate where quantum may outperform classical methods.

Applied Quantum Models for Finance

Not just theory, but concrete prototypes: real datasets, working notebooks, and step-by-step videos that make the methods transparent and testable. Test it yourself.

Model	Business Problem	Data	Quantum Method	Video	Code
Credit Spread Tail-Risk Scenario Generation	Generate synthetic credit spread tail-risk scenarios from empirical copula dependence data.	Real daily credit spread index data from 2020 to 2025 via FRED.	QCBM / quantum generative modeling	#19	Code
Portfolio Optimization	Optimize a Sharpe-ratio-driven portfolio of up to 10 assets under budget constraints.	Real daily equity price data from mid-2024 to mid-2025 via yfinance.	QUBO / QAOA	#7	Code
Credit Card Fraud Detection	Detect fraudulent card transactions from labeled payment data.	Public credit card transaction dataset (Mastercard), PCA-preprocessed, widely used for fraud-detection benchmarking.	VQC / quantum feature mapping	#9	Code
Derivative Pricing	Estimate derivative prices using Monte Carlo methods and quantum amplitude-estimation concepts.	Market-based option inputs and simulated payoff paths.	Quantum Monte Carlo / amplitude estimation	#8	Code
Market Stress Regime Classification	Classify market stress regimes from equity, volatility, and derived market signals.	Real daily S&P 500 and implied-volatility data with derived indicators.	Quantum classification / hybrid quantum-classical model	#12	Code

Finance Videos

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QAOA vs. Classical Search: How Quantum Optimization Redirects the Search

FINANCE#20

Released 02 Apr 2026

QAOA vs. Classical Search: How Quantum Optimization Redirects the Search

Open on YouTube

This video visually compares a classical sequential search with quantum optimization using QAOA. While the classical approach evaluates candidates one by one, QAOA reshapes probabilities across the full solution space to guide the search toward better outcomes. The side-by-side visualization shows how both approaches behave and why quantum optimization differs fundamentally from classical candidate-by-candidate evaluation. The QAOA search shown here is based on a real QAOA run, not a mockup.

Links

Short Clip Version

Finance Deep Dive

QCBM for Quantum Finance: Credit Spread Copulas and Tail Risk

FINANCE#19

Released 23 Mar 2026

QCBM for Quantum Finance: Credit Spread Copulas and Tail Risk

Open on YouTube

This video presents a structured quantum-finance use case built around copula-based dependence modeling and quantum scenario generation for credit spread tail risk. It explains how empirical credit spread data can be transformed into a joint dependence distribution, why tail regions are difficult to model with sparse classical observations alone, and how a Quantum Circuit Born Machine can be used to learn and sample from that distribution. The discussion covers the intuition behind empirical, Gaussian, and t-copulas, the role of tail dependence in credit risk, and the practical logic of discretizing the joint space before training a quantum generative model. It also positions the workflow in the broader context of Quantum GenAI, highlighting how quantum models may eventually support scenario generation for rare but relevant financial stress events. The goal is to provide a technically grounded but accessible walkthrough of a realistic quantum-finance modeling pipeline.

Links

Open Colab Notebook A Simple Introduction to Copulas (by Dirty Quant)

Finance

FINANCE#16

Released 19 Feb 2026

Quantum Initiatives in Finance: Landscape of Methods and Domains (2021–2025)

Open on YouTube

The video maps publicly documented quantum initiatives in finance from 2021 to the end of 2025 in a structured, non-ranking overview. Each initiative is placed in a grid by business domain and by the quantum method or topic used, showing where activity clusters and how it evolves over time. Coverage spans portfolio allocation, credit risk, derivatives pricing, trading, fraud and AML, and cybersecurity. The method and topic columns include quantum annealing, QAOA, quantum Monte Carlo and quantum machine learning, as well as security efforts such as quantum key distribution and post-quantum cryptography migration programs. A legend section provides partner context and references for each entry. This video was created for a finance audience.

Strategy Finance

Quantum Readiness for Banking Executives: A Practical Playbook

FINANCE#15

Released 16 Feb 2026

Quantum Readiness for Banking Executives: A Practical Playbook

Open on YouTube

The video presents quantum readiness for banks as a practical capability build, independent of betting on a specific hardware timeline. It starts by separating what a bank can control today from what it cannot, then lays out a staged playbook from initial enablement and use case triage to baseline-first benchmarking, vendor assessment, and controlled pilots. Along the way, it highlights the minimum evidence artifacts a leadership team should expect, including a simple executive dashboard and reusable templates to keep efforts measurable and avoid hype. It concludes with a pragmatic 12 to 24 month roadmap that banks can start this quarter. This video was created for a banking audience.

Strategy Finance

QAOA Masterclass for Finance: From QUBO to Circuits, Mixers, Constraints, and Credible Reporting

FINANCE#14

Released 09 Feb 2026

QAOA Masterclass for Finance: From QUBO to Circuits, Mixers, Constraints, and Credible Reporting

Open on YouTube

This QAOA masterclass is a practical, end to end deep dive into constrained optimization with quantum circuits. A quick word of warning: we go all the way down to the low level, including unitaries, gate mappings, and mixer design details. The session starts from problem formulation and shows how to translate real objectives and constraints into QUBO and Ising models, then builds the corresponding cost unitary and maps it to gates. It then focuses on mixer choices, including why the standard X mixer can fail for constrained problems and how constraint-aware mixers can improve feasibility and sampling quality. We close with parameter optimization patterns, common pitfalls, and what to report so results are credible, such as best feasible samples, feasibility rates, and robustness across seeds. This session was created for finance-style optimization use cases.

Finance Deep Dive

Quantum Algorithms in Finance: The “Inner Life” of a Qubit, QAOA + QML/VQC, Objectives 2026

FINANCE#13

Released 24 Jan 2026

Quantum Algorithms in Finance: The “Inner Life” of a Qubit, QAOA + QML/VQC, Objectives 2026

Open on YouTube

The talk opens with a simulator-based view of a qubit’s dual life before and after measurement, building intuition for quantum behavior. It then introduces key quantum algorithms, including QAOA and Quantum Machine Learning via Variational Quantum Circuits (VQC), and explains how these approaches are used in practice. The presentation concludes with an outlook on a phased approach to onboarding quantum computing in a financial institution, including concrete objectives for 2026. This session was delivered for a bank.

Links

QAOA Colab Notebook QML/VQC Colab Notebook

Finance Strategy

FINANCE#12

Released 26 Nov 2025

QML reality check: classical vs quantum

Open on YouTube

Discover how Quantum Machine Learning performs in a real finance scenario - we test a hybrid quantum-classical model on S&P 500 market regime prediction and benchmark it against a strong classical baseline. You will see the full experiment end-to-end: engineered features, 4-class regime labels, reproducible runs over many seeds, and a proper statistical test using confidence intervals. This is the same rigorous approach used in industry pilots today. A clear look at whether QML delivers real value beyond decoration.

Links

Open Colab Notebook HSBC Press Release 25-Sep-2025 Bloomberg Podcast on HSBC QML 25-Sept-2025

Finance

FINANCE#9

Released 03 Aug 2026

QML/VQC for Fraud Detection

Open on YouTube

Discover how Quantum Machine Learning powers fraud detection in finance - see QNNs in action with real transaction data, hands-on Python code, and practical performance analysis. The approach used here is similar to the one used by HSBC for their bond trading use case. See also the coverage below. QML/VQC is a promising option today for quantum computing in finance.

Links

Open Colab Notebook HSBC Press Release 25-Sep-2025 Bloomberg Podcast on HSBC QML 25-Sept-2025

Finance

FINANCE#8

Released 24 Jul 2025

Quantum Monte Carlo for Option Pricing

Open on YouTube

Explore how Quantum Monte Carlo with QAE can achieve a quadratic speed-up against classical simulations in pricing financial options - featuring live Python code and real-world insights.

Links

Open Colab Notebook

Finance

FINANCE#7

Released 13 Jul 2025

QAOA Portfolio Optimization

Open on YouTube

Explore quantum portfolio optimization: QAOA, real-world finance, and the future beyond classical methods - all explained step by step supported by a Python notebook.

Links

Open Colab Notebook

Finance