The QML Curriculum

Jump directly to the routine or algorithm you want to understand and fill in any gaps in your knowledge as needed

How To Use The Cube

Forget the map, there's something better!

The cube is not meant to be a puzzle to be solved. It is meant to be a thinking tool. A mental model to help you find your way around the entire Quantum Machine Learning landscape. So, do not try to learn side after side, as textbooks often prescribe.

The cube offers a dynamic way to learn. Choose a topic or project that excites you. Internal links automatically pull the relevant parts you actually need from the adjacent areas. Combine theory and practical examples to your convenience.

So what do you really want to learn?

Part I – Foundations

You really want to start with the theory? But don't say I didn't warn you.
Quantum computing, machine learning, and ultimatively quantum machine learning share a common language. The Foundations-Part is all about this language. The mathematics, physics, and computational ideas that make Quantum Machine Learning make sense.
Fear The Terror Waiting For You Down The Rabbit Hole

Fear The Terror Waiting For You Down The Rabbit Hole

Part II - Quantum Information

Understand the most important concepts underlying to quantum computing
    Core Components

    Core Components

    The quantum superposition is much easier to understand than you think.

    Quantum States
    Quantum Operators
    Key Theorems
    Measurement

    Measurement

    Born Rule
    Projection
    Expectation Value
    Advanced Techniques
    Quantum Data and Encoding

    Quantum Data and Encoding

    Basis encoding
    Amplitude encoding
    Angle encoding
    Structured state preparation
    QRAM
    Oracle state preparation

    Quantum Information Measures

    Fidelity
    Entropy
    Information Bounds

    Quantum Similarity

    Similarity Estimation
    Quantum Kernels
    Kernel Optimization

Part III – Implementation

Understand the most important concepts underlying to quantum computing
    Quantum Software Engineering Environment

    Quantum Software Engineering Environment

    IDEs
    QDKs
    Libraries
    Hardware
    Algorithm Engineering and Execution

    Algorithm Engineering and Execution

    Circuit Construction
    Compilation and Transpilation
    Experiment Management

Part IV – Quantum Algorithmic Paradigms

What kinds of quantum algorithms can we actually build?
Explore the key algorithmic ideas that give quantum computers their unique power. Take the tour of the quantum algorithmic toolbox.

    Algorithmic Primitives

    Algorithmic Primitives are the reusable building blocks that most quantum algorithms rely on.

    QFT
    QPE
    QSVT
    Quantum Walk

    Query Algorithms

    Deutsch-Jozsa
    Bernstein-Vazirani
    Grover
    Fourier-Based
    Variational and Hybrid Algorithms

    Variational and Hybrid Algorithms

    Variational Principle
    Algorithms
    Optimization

    Simulation and Sampling

    Hamiltonian
    Sampling

    Communication and Security

    Communication
    Cryptography
    Distributed

Part V – Quantum Machine Learning

Conceptual Overview

Quantum Learning Theory

Quantum Models and Architectures

Training and Optimization

    Real-World Quantum Machine Learning Applications

    Real-World Quantum Machine Learning Applications

    Cognitive Fallacies
    Source-Apportionment
    ATM Withdrawals
    Portfolio Optimization
    Space Exploration

Part VI – Hardware and Validation

Choose how you want to explore Quantum Machine Learning
    Hardware and Physical Realization

    Hardware and Physical Realization

    Qubit Technologies
    Noise and Control
    Benchmarking Hardware
    Noise and Decoherence

    Noise and Decoherence

    Physical vs. Logical Qubits
    Sources of Noise
    Noise Models
    Error Mitigation

    Error Mitigation

    ZNE
    PEC
    CDR
    MEM
    Fault Tolerance and Error Correction

    Fault Tolerance and Error Correction

    Classical
    Stabilizer
    Surface Code
    Topological