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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.

Rapid Quantum Prototyping

The RQP Suite

Test quantum on your own use cases: configurable tools for portfolio optimization, index replication, feature selection, credit allocation, machine learning, and risk — defined in Excel, benchmarked against certified classical referees.

Explore the RQP Suite

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.

ModelBusiness problemDataQuantum methodVideoTry it
Credit Spread Tail-Risk Scenario GenerationGenerate 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
Video #19
Notebook
Portfolio OptimizationOptimize 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
Video #7Video #21
RQP*Notebook
Credit Card Fraud DetectionDetect 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
Video #9
RQP*Notebook
Derivative PricingEstimate derivative prices using Monte Carlo methods and quantum amplitude-estimation concepts.Market-based option inputs and simulated payoff paths.Quantum Monte Carlo / amplitude estimation
Video #8
RQP*Notebook
Market Stress Regime ClassificationClassify 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
Video #12
RQP*Notebook
Index ReplicationReplicate the S&P 500 with a compact basket under mandate rules (fixed core, theme bundles, exclusions), with every rule priced in tracking error.Real SPY constituent weights and covariance.QUBO / QAOA + certified exact referee
Video #22Video #23
RQP*
Feature SelectionSelect a compact, low-redundancy feature set for downstream ML models from your own tabular data.Your dataset, uploaded with an Excel configuration workbook.QUBO / QAOA + certified exact referee–
RQP*
Credit AllocationApprove or decline credit facilities under a capital budget, sector limits, and correlated default risk, with connected-client groups decided together.Facility list (EAD, PD, LGD, margins) plus a sector correlation matrix in Excel.QUBO / QAOA + certified exact referee–
RQP*
Collateral AllocationPost collateral to margin obligations at minimum cost while scenario covariance keeps the coverage stable - locked lines, whole-moving pledge pools, and concentration limits included.Obligations, collateral inventory, eligibility schedule, and stress-scenario returns in Excel.QUBO / QAOA + certified exact referee–
RQP*
Credit & Capital RiskUnderstand how quantum amplitude estimation changes credit risk and capital computations, on transparently provable mechanisms.Configurable credit portfolio scenarios in Excel.Quantum amplitude estimation–
RQP*

* RQP = Rapid Quantum Prototyping Tool: the ready-to-run version of the model with a certified classical referee — your own data in, a certified report out. Access via the Resource Room. “Notebook” opens the open-source Colab notebook behind the video.

Finance Videos

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Agentic Quantum Finance: One Prompt, One Certified Report
FINANCE#23
Released 17 Jul 2026

Agentic Quantum Finance: One Prompt, One Certified Report

Open on YouTube

We typed one paragraph into an AI chat: replicate the EURO STOXX 50 with about 15 names, keep the tech and energy leaders, exclude ASML, bundle the rest by CIO themes - and get the certified report. The agent pulled live index data, built and validated the workbook, ran QAOA on the Index Replication RQP against the exact classical referee, and delivered the certified PDF. When we asked whether it could do better, the answer was no - with the mathematical proof attached. Works from Claude, ChatGPT, Gemini - any assistant that speaks MCP. Not investment advice; a technical demonstration on real market data.

Links
Explore the RQP Suite
Finance
Replicate the S&P 500 With Just 20 Stocks - Index Replication RQP
FINANCE#22
Released 17 Jul 2026

Replicate the S&P 500 With Just 20 Stocks - Index Replication RQP

Open on YouTube

All 500 S&P constituents stay in the objective - nothing swept under the rug. The Index Replication RQP selects a 20-name basket (fixed mega-cap core, CIO theme bundles, ESG exclusion) that tracks the index, with QAOA benchmarked against a SCIP-certified exact referee. The referee proves the optimum, prices every mandate rule in tracking error, and reports the irreducible floor of the instrument pool. Real SPY weights, real covariance, every number certified.

Links
Explore the RQP Suite
Finance
Optimize an Existing Portfolio + New Capital - Quantum, With a Certified Referee
FINANCE#21
Released 17 Jul 2026

Optimize an Existing Portfolio + New Capital - Quantum, With a Certified Referee

Open on YouTube

A Swiss private-bank use case, end to end: an existing global portfolio of 30 positions plus USD 250k of new capital, allocated under real mandate rules - budget, EU/Swiss exposure, tech bands, and thematic bundles. 78 positions evaluated, 23 optimization decisions. The Portfolio Optimization RQP runs QAOA and checks it against an exact classical solver (MIQP/SCIP) as a second opinion: the quantum result matches the certified optimum to the digit. Excel workbook in - certified PDF report, Qiskit/PennyLane/Cirq notebooks, and raw JSON out.

Links
Open the Portfolio Optimization RQP
Finance
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
FinanceDeep 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 NotebookA Simple Introduction to Copulas (by Dirty Quant)
Finance
Quantum Initiatives in Finance: Landscape of Methods and Domains (2021–2025)
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.

StrategyFinance
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.

StrategyFinance
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.

FinanceDeep 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 NotebookQML/VQC Colab Notebook
FinanceStrategy
QML reality check: classical vs quantum
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 NotebookHSBC Press Release 25-Sep-2025Bloomberg Podcast on HSBC QML 25-Sept-2025
Finance
QML/VQC for Fraud Detection
FINANCE#9
Released 03 Aug 2025

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 NotebookHSBC Press Release 25-Sep-2025Bloomberg Podcast on HSBC QML 25-Sept-2025
Finance
Quantum Monte Carlo for Option Pricing
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
QAOA Portfolio Optimization
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
Testing Quantum Computing in Finance: The RQP Suite
STQ#5
Released 17 Jun 2026

Testing Quantum Computing in Finance: The RQP Suite

Open on YouTube

Quantum computing is often discussed for finance use cases such as optimization, machine learning, and simulation. But before organizations can assess its relevance, they need a structured way to test quantum algorithms against classical baselines. This video introduces the Rapid Quantum Prototyping Suite by qubit-lab.ch, including modules for QAOA portfolio optimization, quantum machine learning with VQC and QSVM, and Quantum Monte Carlo experiments. The goal is practical experimentation, transparent benchmarking, and informed evaluation.

STQFinance
QAOA Today: How Far Can NISQ Hardware Really Go? (as of April 2026)
STQ#4
Released 10 Apr 2026

QAOA Today: How Far Can NISQ Hardware Really Go? (as of April 2026)

Open on YouTube

QAOA is often presented as a promising approach for tackling complex optimization problems. But its real value depends on what today’s hardware can actually support. Current NISQ devices come with strict limitations: noise, limited connectivity, and shallow circuit depths. These are not minor details, they fundamentally shape what QAOA can and cannot achieve in practice. This video takes a grounded look at these constraints and translates them into realistic expectations. Not as a final verdict, but as a clearer picture of where we stand today and what remains out of reach.

STQDeep DiveFinance