|ψ⟩ = α|0⟩ + β|1⟩U(θ, φ, λ) = RZ(φ) RX(-π/2) RZ(θ) RX(π/2) RZ(λ)H = -∑ J_{ij} σ_i^z σ_j^z - h ∑ σ_i^xCNOT |ab⟩ = |a, a ⊕ b⟩⟨ψ|ψ⟩ = |α|² + |β|² = 1S(ρ) = -Tr(ρ log ρ)QFT |x⟩ = 1/√N ∑ e^{2πixk/N} |k⟩⟨φ|ψ⟩ = Σ φᵢ* ψᵢρ = |ψ⟩⟨ψ|P(|1⟩) = |β|²Grover: O(√N)E(|ψ⟩) = ⟨ψ|H|ψ⟩
|ψ⟩ = α|0⟩ + β|1⟩U(θ, φ, λ) = RZ(φ) RX(-π/2) RZ(θ) RX(π/2) RZ(λ)H = -∑ J_{ij} σ_i^z σ_j^z - h ∑ σ_i^xCNOT |ab⟩ = |a, a ⊕ b⟩⟨ψ|ψ⟩ = |α|² + |β|² = 1S(ρ) = -Tr(ρ log ρ)QFT |x⟩ = 1/√N ∑ e^{2πixk/N} |k⟩⟨φ|ψ⟩ = Σ φᵢ* ψᵢρ = |ψ⟩⟨ψ|P(|1⟩) = |β|²Grover: O(√N)E(|ψ⟩) = ⟨ψ|H|ψ⟩
    QQ
    Quddle Quantum
    🧮Algorithms🧫Quantum Data🌌Spaces🔥Trending🧪QSim📚Learning
    NewSign In
    Back to Learning•Application module

    Quantum Machine Learning

    Feature maps, kernels, and hybrid differentiable models.

    Module outline
    Select any section to jump directly to it. Completion saves locally.
    Want to improve this module?

    Learning notes are stored directly in Supabase as Markdown. Update them via the scripts/generate_learning_content.mjs pipeline or edit the learning_subsections.content_md field manually.

    Add supplementary material by appending items to learning_subsections.resources (title + URL). The references block updates automatically.

    Quantum feature maps
    Quantum data encodings and kernel foundations.