5-Day Quantum Capability Kickstart
From quantum concepts to hands-on quantum coding.
The Quantum Capability Kickstart is designed for quant, risk, data science, innovation, and technology teams that want to build practical quantum coding capability. Participants learn the core concepts by writing and running simple quantum circuits, interpreting results, tuning basic quantum algorithms, and using supporting tools such as RQP and LLM-assisted coding to move faster from theory to experimentation. The Kickstart closes with the team's own 3–4 candidate use cases, ready for RQP usage.
Flat team price for up to 8 participants — discussed in the intro call
Format
Five interactive sessions
Each session is about 3–4 hours and combines explanation, coding, walkthroughs, and practical experimentation.
Focus
Quantum coding capability
Participants learn to write, run, inspect, and adapt simple quantum programs, using frameworks such as Qiskit for hands-on coding.
Output
Practical experimentation skills
Participants leave with a working understanding of circuits, simulators, algorithm parameters, result interpretation, and how LLM support can accelerate quantum coding.
What participants say
Kickstart · October 2026 · practitioners from a wealth-tech firm, a consultancy, a family office and a bank
“An excellent introduction to Quantum Computing. Highly enjoyable and motivating!”
“It was the first time I fully grasped how quantum computing can be applied to real-world problems. The relaxed and friendly yet intellectually rigorous environment encouraged a fruitful exchange of ideas.”
“A super interesting and very good introduction to quantum computing. I started with no quantum background and left with a real understanding of the theory and a clear idea of where it can be applied in finance. Mathematical and demanding, but the hands-on exercises and classroom discussions made it tangible.”
“Much less abstract than I expected. After three mornings I understand what is behind it and where it can lead in practice. I would recommend it without hesitation.”
Participants said it plainly: the course is mathematical and demanding. It rewards people who have written Python, can live with a matrix, and want to understand the mechanism rather than hear about it. The self-check tells you beforehand whether that is you.
What the Kickstart is designed to solve
Many teams have heard about quantum computing, but have not yet made the step from concepts to code. The Kickstart closes that gap. It introduces the mathematical and conceptual foundations, but always connects them back to executable quantum circuits, coding workflows, simulators, algorithm tuning, and practical experimentation.
Typical starting questions
- How do we move from quantum theory to executable code?
- How do qubits, gates, circuits, and measurement work in practice?
- How can we write and run simple quantum programs?
- How do simulators, hardware backends, noise, and shots affect results?
- How can LLMs support quantum coding, debugging, and learning?
Typical outcomes
- ability to read and write simple quantum circuits
- practical understanding of quantum coding workflows
- confidence in interpreting measurements and simulator results
- basic ability to tune and inspect quantum algorithm behavior
- clearer judgment about where deeper prototyping may be worthwhile
Curriculum: five interactive sessions
The structure can be adapted, but a typical Kickstart starts with single-qubit foundations and then moves through interference, entanglement, finance algorithms run as prototypes with a referee, and what survives on real hardware.
Session #1
Basics of Quantum Computing
Single-qubit systems
Topics
- intro to qubits, vectors, matrices, and complex numbers
- mathematical description of qubit states and gates
- phases, phase shifts, unitarity, Bloch sphere, and rotations
- measurement and the dual life of a qubit
- your first quantum circuit
Outcome
Participants understand the basic mathematical and conceptual building blocks of single-qubit quantum computing and write their first simple circuit.
Session #2
Quantum Interference
Phases, rotations, and interference
Topics
- complex numbers, phases, and rotations
- overview of single-qubit quantum gates
- how quantum interference works
- the 3-step approach to quantum interference
- coding and inspecting simple interference examples
Outcome
Participants understand why interference is central to quantum algorithms and learn how to reproduce simple interference effects in code.
Session #3
Circuit Calculations and Algorithm Mechanics
Tensor products, phase kickback, the three classic algorithms
Topics
- tensor products and walk-through circuit calculations
- bases and basis changes, eigenstates and eigenvalues
- controlled gates on eigenvectors: phase kickback
- Deutsch-Jozsa as an interactive exercise
- Shor, Q-Day and post-quantum cryptography; Grover for unstructured search
Outcome
Participants calculate multi-qubit circuits by hand and in code, and explain the three classic algorithms as uses of one recipe, including what Shor means for Q-Day and PQC.
Session #4
Quantum Algorithms for Finance, hands-on
Expectation values, the hybrid loop, QAOA and VQC
Topics
- the expectation value as the number an optimizer steers on; Sampler versus Estimator
- parametrized gates, the variational ansatz, data into angles
- QAOA: cost and mixer layers, the QUBO, penalty weights, depth and restarts
- QAOA portfolio optimization as a Rapid Quantum Prototype with an exact referee
- VQC fraud classification as a Rapid Quantum Prototype against tuned classical baselines
Outcome
Participants read a QUBO and tune its penalties, run QAOA and a variational classifier on simulators, and judge every result against the best classical baseline.
Session #5
Noise, Hardware and Quantum Monte Carlo
What survives on a real device; pricing and risk
Topics
- gate fidelity, routing and depth limits: what fits on today's hardware
- the noise ladder from exact simulation to a real IBM device, hands-on with the Session 4 circuit
- simulators: statevector, tensor-network, noisy, and where each stops
- quantum Monte Carlo for pricing and risk: a probability becomes an amplitude, the quadratic promise and its price
- outlook: what is feasible today and what to do next
Outcome
Participants judge what survives on a real device, pick the simulator that answers the question, and run quantum Monte Carlo pricing as a Rapid Quantum Prototype with a classical reference.
Where RQP fits in
The Rapid Quantum Prototyping Suite can be included to make the Kickstart more concrete. Instead of discussing quantum algorithms only in theory, participants can inspect workflows, run experiments, compare against classical baselines, review outputs, and use RQP as a structured bridge between training examples and practical use-case exploration.
QAOA
Optimization workflows for portfolio, allocation, scheduling, and constrained selection examples, including parameter choices and benchmark comparison.
VQC and QSVM
Quantum machine learning workflows for classification-oriented examples, including how feature encoding, training, and evaluation are structured.
QMC and QAE
Quantum Monte Carlo and expectation-estimation workflows for pricing, probability, and risk-style examples, including resource and convergence comparison.
Deliverables
five interactive sessions, each about 3–4 hours
a companion Jupyter notebook for every session: 147 runnable code cells in total, with graded exercises and worked solutions
a slide handout for every session: 327 pages in total, each content slide reproduced with the explanation that accompanied it in the room, plus a prose companion per session
notebooks that run offline on a laptop: no cloud account, no hardware queue, no vendor relationship required
executable quantum circuits and algorithm examples, each checked against a classical reference rather than asserted
common language for business and technical stakeholders
practical understanding of circuits, gates, shots, simulators, and results
introduction to RQP-supported experimentation and benchmarking
guidance on how LLMs can support quantum coding and learning
Course materials
The Kickstart is not a slide deck with a demo attached. Every session pairs a deck with a companion Jupyter notebook, and every participant keeps both. The written material is designed to be readable months later by someone who was in the room — and usable by a colleague who was not.
Companion notebooks
One Jupyter notebook per session, 147 runnable code cells in total. Each cell is numbered to match a slide, so the deck and the notebook can be read side by side. Graded exercises with worked solutions, and nothing that needs a cloud account or a hardware queue — participants can re-run everything the following week.
Extended handouts
One slide handout per session, 327 pages in total. Every slide that carries an argument is reproduced with the explanation given in the room, plus the common pitfalls, the self-check questions, and the pointer to the notebook cell that verifies the claim.
Verified against classical baselines
Every quantitative claim is stated against a classical referee — the exact optimum where one can be computed, a held-out test set where it cannot. Where there is no quantum advantage to report, the material says so.
See the material before you book
A 23-page sample takes one topic from each of the five sessions all the way through — from the slides that build an idea to the notebook page that makes it executable. The first three assume no physics and can be followed line by line; the last two are the judgement a team needs before spending money on hardware or believing a machine-learning result, including a quantum classifier benchmarked against logistic regression, an SVM and a random forest.
Download the sample (PDF, 23 pages)Prerequisites: are you ready?
No quantum knowledge is assumed. What the course does need is a laptop with a working Python notebook, basic Python, and NumPy vectors and matrices; complex numbers help but are introduced. A short online self-check tells you, and us, what to brush up before the first morning. It is a readiness diagnostic, not an entrance exam.
Take the pre-course self-checkBefore the course
- Setting up your notebook ↗ · PDF, 5 pages
- Ready for the Kickstart? Paper self-check ↗ · PDF, 12 pages
Cheat sheets
- Vectors and matrices ↗ · PDF, 4 pages
- Complex numbers ↗ · PDF, 2 pages
Each operation with its structure, one worked example and the NumPy call. Print them and keep them next to the laptop.
Who should participate
The format works particularly well for teams that combine financial, quantitative, technical, and innovation perspectives. It can also be adapted for a more management-oriented or more technical audience.
- quant teams
- risk teams
- data science teams
- innovation teams
- technology and architecture teams
- business leaders who need a realistic view of quantum coding and experimentation
Prerequisites
- Basic Python coding skills are recommended for participants joining the hands-on parts.
- Basic mathematics, including simple vector and matrix calculations, is helpful.
- Prior understanding of complex numbers is helpful but not required.
- No prior quantum computing background is required.
- The format can be adapted for management, mixed teams, or more technical expert groups.
What this is not
Not a hype session
The Kickstart is explicit about limitations, hardware constraints, benchmarking needs, and the difference between promising research and practical business value.
Not only a theory course
The focus is not only on understanding concepts. Participants are guided toward writing, running, inspecting, and adapting simple quantum programs and algorithmic workflows.
Discuss a Kickstart for your team
A short initial discussion is usually enough to clarify the audience, maturity level, preferred format, and whether the Kickstart should focus more on quantum coding, quantum algorithm understanding, RQP-supported experimentation, or management-level orientation.