Lesson 0a · Week 1 · Classroom
From classical bitstrings to quantum measurement
Song-code metaphor: 011 is ‘Progression is U’. Classical stores that string. Equal amplitudes do not favor it.
Takeaway. Before the qubit, understand the bitstring. Classical stores one answer. Quantum shapes the chances of possible answers.
Lesson 0b · Week 1 · Classroom
Before the qubit, understand the bitstring
Bit → bitstring → encoding → register → basis states → one measured string.
Takeaway. We do not manipulate final answers. We manipulate the state so useful bitstrings become more likely.
Lesson 0c · Week 2 · Classroom
From qubits to bitstrings
Gates change amplitudes. One shot returns one string. Many shots return a distribution. Grover / QAOA / sampling live here.
Takeaway. Useful bitstrings are made more likely — not typed in.
Lesson 1 · Week 2 · Classroom
Vectors and quantum states
Column vectors, basis kets, linear combinations, then amplitudes vs probabilities.
Takeaway. Order matters. Amplitudes — not raw probabilities — carry the quantum information.
Lesson 2 · Week 2 · Classroom
Matrices and quantum gates
X|0⟩=|1⟩, H|0⟩ equal superposition, X·X=I, order XH ≠ HX.
Takeaway. Gates are unitary matrices. Sequence is the program.
Lesson 3 · Week 2 · Classroom
Complex numbers, magnitude, and phase
z = a+bi, |z|, conjugate, polar form. Golden rule: magnitude → probability, phase → interference.
Takeaway. That split is most of the ‘quantum magic’ beginners miss.
Lesson 4 · Week 2 · Classroom
Probability, measurement, and shots
A shot is prepare → measure → record. Counts / N estimate probabilities. Error ~ 1/√N.
Takeaway. Always report shot count next to a quantum number.
Lesson 5 · Week 4 · Classroom
Statistics, noise, and quantum results
Mean, variance, SE = σ/√n. Gate error, decoherence, readout. Ideal vs noisy histograms.
Takeaway. Results are statistical. Benchmarking is statistics plus an honest noise label.
Lesson 6 · Week 2 · Classroom
Tensor products and multiple qubits
n qubits → 2ⁿ amplitudes. Product vs Bell state. CNOT after H.
Takeaway. Entanglement is a state you cannot factor — not a vibe.
Lesson 7 · Week 4 · Classroom
Eigenvalues, eigenvectors, and observables
A|ψ⟩=λ|ψ⟩. Pauli Z example. Expectation ⟨Z⟩. VQE minimizes ⟨H⟩.
Takeaway. Observables ask questions. Eigenvalues are the only allowed answers.
Lesson 8 · Week 2 · Classroom
Trigonometry and the Bloch sphere
|ψ(θ,φ)⟩ = cos(θ/2)|0⟩ + e^{iφ}sin(θ/2)|1⟩. Rx, Ry, Rz. DJ-knob analogy.
Takeaway. Radians, sine/cosine, and rotations are the single-qubit control language.
Lesson 9 · Week 4 · Classroom
Calculus and variational algorithms
Gradient descent on C(θ). Hybrid loop: circuit U(θ) → measure cost → classical update. VQE / QAOA.
Takeaway. Calculus steers. The optimizer drives. The circuit does the lift.
Lesson 10 · Week 4 · Classroom
Graph theory, Boolean logic, and quantum optimization
Graph → bitstring decisions → XOR/CNOT → Max-Cut → QAOA. Map → encode → optimize → act.
Takeaway. Before the circuit, build the map. This is the classical-baseline week in graph form.