L'Académie Quantique

À qui c'est destiné: Business leaders, investors, students, and curious professionals with no physics or coding background

Vous serez capable de:

• Explain why quantum computing is a specialized tool rather than a universal replacement for classical computing.
• Describe a qubit, measurement, probability amplitudes, phase, superposition, entanglement, and interference in plain language.
• Identify the major hardware approaches and the importance of noise and error correction.
• Evaluate common application claims in chemistry, optimization, finance, AI, sensing, and cybersecurity.
• Use a five-part framework to separate credible progress from hype.

Évaluation: Module quizzes 40% · terminology and concept exercises 20% · final capstone 40%

Prérequis: No prior physics, calculus, linear algebra, or coding is required.

À qui c'est destiné: Learners who understand basic quantum concepts and want circuit-level and industry fluency

Vous serez capable de:

• Represent one- and two-qubit states using vectors and tensor products.
• Explain how common gates transform amplitudes and phase.
• Build and interpret small circuits, including Bell states.
• Compare Grover search, phase estimation, Shor, and variational approaches at a conceptual and workflow level.
• Evaluate NISQ experiments, hardware benchmarks, and quantum advantage claims.
• Design a credible proof-of-concept plan for an organization.

Évaluation: Labs 30% · module quizzes 30% · capstone 40%

Prérequis: Completion of the beginner course or equivalent understanding of qubits, measurement, superposition, entanglement, interference, and error correction. Basic algebra is expected; introductory Python is helpful but optional.

À qui c'est destiné: Technical leaders, analysts, developers, researchers, and investors seeking advanced technical and strategic fluency

Vous serez capable de:

• Work with state vectors, operators, density matrices, channels, and measurement formalism.
• Analyze algorithmic complexity and distinguish query, gate, sample, and end-to-end cost.
• Explain stabilizer codes, syndrome extraction, thresholds, logical gates, and fault-tolerant overhead.
• Perform a high-level resource estimate for a fault-tolerant workload.
• Compare hardware architectures through control, connectivity, fidelity, scaling, and error-correction requirements.
• Critique quantum advantage claims and construct a technically defensible roadmap.

Évaluation: Problem sets and labs 40% · quizzes 20% · capstone 40%

Prérequis: Comfort with linear algebra, complex numbers, probability, basic algorithms, and the intermediate course material. Python experience is strongly recommended.