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Qubit Lab

Quantum Computing. Demystified.

Unlock the secrets of quantum computing - step by step.

Quantum Computing. Straight Talk.

Qubit Lab translates quantum computing into practical guidance for decision makers and practitioners. I explain how the technology works, where it matters for business and finance, and how to move from curiosity to concrete next steps. The content is designed for clarity and impact: no unnecessary physics, no hype, and a focus on what you can evaluate, pilot, and prepare for. Training and advisory are available for teams that want a structured path.

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New tool

Quantum Portfolio Optimizer

Test the new Rapid Quantum Prototyping Tool by qubit-lab.ch. Define a portfolio optimization problem in Excel and let the tool build the QUBO, QAOA setup, simulation outputs, and diagnostics.

Open QAOA RQP Tool

Latest 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

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.

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

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.

Finance
Quantum Chemistry 2: Hamiltonian Simulation Algorithms (VQE, ADAPT-VQE, QPE, Trotterization, Qubitization)
CHEMISTRY#18
Released 06 Mar 2026

Quantum Chemistry 2: Hamiltonian Simulation Algorithms (VQE, ADAPT-VQE, QPE, Trotterization, Qubitization)

The video builds on the first chemistry introduction and compares the main quantum algorithm choices for estimating molecular ground-state energies in a structured, step-by-step overview. It explains which approaches are relevant on current NISQ hardware, which belong to the fault-tolerant era, and why that distinction matters when assessing realistic timelines for quantum advantage in chemistry. Coverage includes VQE and ADAPT-VQE as near-term hybrid methods, as well as Quantum Phase Estimation with trotterization and with qubitization, including the key tradeoffs in circuit depth, precision, measurement effort, and asymptotic scaling. The goal is to provide a clear framework for understanding what is feasible now and what may become important later. This video was created for an audience seeking a technically informed comparison.

ChemistryDeep Dive
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