Quantum Information and Machine Learning via Rust

Contents

Information → algorithms → error correction → learning with amplitudes.

• Part IThe Machinery of Quantum Information5 chaptersHide

  From a classical bit to a qubit, gates, entanglement, noise — and a simulator you build yourself.

  • 01Bits to QubitsClassical information, the qubit, the Bloch sphere, and superposition as a countable resource.
  • 02Gates and CircuitsUnitary gates, universal sets, the circuit model, and measurement — a full state-vector simulator.
  • 03Entanglement as a ResourceBell states, the Schmidt decomposition, teleportation, superdense coding, and monogamy.
  • 04Quantum Channels and NoiseDensity matrices, Kraus operators, depolarizing and dephasing — the honest math of real machines.
  • 05The Stabilizer FormalismThe Pauli group, stabilizer states, the Gottesman–Knill theorem — simulating 1000 qubits classically.
• Part IIAlgorithms and What They Really Buy4 chaptersOpen

  Interference as computation: the query model, the Fourier family, search, and simulating physics itself.

  • 06Interference as ComputationDeutsch–Jozsa, Bernstein–Vazirani, the oracle model, and what a quantum speedup honestly is.
  • 07The Fourier FamilyThe quantum Fourier transform, phase estimation, and Shor's algorithm — why periods are quantum-easy.
  • 08Search and Amplitude AmplificationGrover's algorithm, amplitude amplification, quantum walks, and the provable √N limit.
  • 09Simulating Quantum SystemsHamiltonian simulation, Trotter–Suzuki splitting, and error referees — Feynman's original killer app.
• Part IIIKeeping Quanta Alive4 chaptersOpen

  Errors, codes, thresholds, and the near-term reality of noisy machines — how quantum computing survives itself.

  • 10The Problem of ErrorsDecoherence digitized, the bit-flip and phase-flip codes, and why continuous errors become discrete.
  • 11Quantum Error CorrectionThe Shor code, stabilizer and CSS codes, and the surface code — decoded, in Rust.
  • 12Thresholds and Fault ToleranceThe threshold theorem, fault-tolerant gates, magic-state distillation — the phase transition that saves the field.
  • 13NISQ and Variational CircuitsVariational quantum eigensolvers, QAOA, and what noisy near-term machines can honestly do.
• Part IVLearning with Amplitudes4 chaptersOpen

  Machine learning from the ground up, then rebuilt in Hilbert space — with an honest scorecard of where advantage lives.

  • 14Learning from DataClassical machine learning bottom-up: linear models, kernels, gradient descent, and the learning problem.
  • 15Feature Maps and Quantum KernelsEncoding data into Hilbert space, quantum feature maps, and kernels no classical computer can cheaply forge.
  • 16Training Quantum ModelsVariational classifiers, the parameter-shift rule, and the barren-plateau problem that flattens the landscape.
  • 17The Honest ScorecardDequantization, quantum data, tensor networks as the classical shadow of entanglement — where advantage might truly live.