ADEQUATE (Advanced End-to-end QUAntum computing TEchnical) Course

Duration

Approx. 50 hours

Difficulty

Intermediate

Online Courses

Course Overview

The ADvanced End-to-end QUAntum computing TEchnical course (ADEQUATE), funded by the EIT Deep Tech Talent Initiative, provides a complete overview of the established quantum algorithms and computational techniques, accessible to a broad audience, with no requirement of prior familiarity with quantum physics. Following a hands-on, interactive learning approach using quantum programming languages such as Qiskit and PennyLane, it provides a solid understanding of the fundamental principles of quantum computing, building the necessary skills to independently apply it to solving specific problems.

The course takes the approach of learning by application and problem solving, consisting of content and projects which are suitable for testing on actual quantum computing hardware. The training contextualises the historical development of quantum computing as well as its current and future applicability to scientific and industrial challenges.

Here is the ADEQUATE course outline encompassing total of 50 hours or more:

Module 1: Historical Context and Future Outlook

History of quantum computing
Quantum Science and Technology 1.0 & 2.0
Key milestones and technological advancements
National initiatives
Future trends and impact on industries
Quantum computing use cases

Module 2: Mathematical Formalism and Fundamental Concepts

Single Classical System
Classical Coin Flipping
Multiple Classical Systems
Single Quantum System
Quantum Coin Flipping
Multiple Quantum Systems
Correlation and Entanglement
Visualizing (Real) Operations on Unit Circle
Dense Coding and Teleportation

Module 3: Fundamental Quantum Algorithms

Phase-Kickback
Deutsch-Jozsa
Bernstein-Vazirani
Simon’s Algorithm
Grover’s Algorithm for Unstructured Search

Module 4: Quantum Computing Platforms

Quantum Hardware (superconducting, trapped ion, photonics, quantum dots, neutral atoms, nitrogen vacancies)
Quantum Software (SDKs, platforms, error-correction)
Hybrid quantum computing (quantum simulators and emulators, quantum annealing, HPC centres)
Programming on real quantum computers

Module 5: Subroutines (Quantum Fourier Transform and Quantum Phase Estimation)

Complex Numbers
Quantum Fourier transform
Quantum phase estimation algorithm
Reducing factorization to order finding
Shor’s algorithm for integer factorization

Module 6: Advanced Quantum Algorithms

Parametrized quantum circuits, overview of variational quantum algorithms
Time evolution of quantum system, Hamiltonian
Quantum Approximate Optimization Algorithm (QAOA)
Quadratic unconstrained binary optimization (QUBO)
QUBO as Ising Hamiltonian
Variational Quantum Eigensolver (VQE)

Module 7: Understanding and dealing with errors

Errors in quantum computers: gate errors, read-out errors
Basics of Error correction
Quantum error correction codes (QECCs)
Simple quantum codes (repetition, shor)
Error mitigation on NISQ hardware: dynamic decoupling; zero-noise extrapolation

Module 8: Quantum Machine Learning (QML)

QML overview
Optimization (QAOA, VQE)
Quantum Classifier/regression
Quantum data and quantum features
Quantum-Enhanced Algorithms (QSVM, QPCA)

Module 9: Quantum Neural Networks (QNNs)

Introduction to Quantum Neural Networks (QNNs)
Design and Training of Quantum Neural Networks
Quantum Circuit Born Machines (QCBMs)
Quantum Feedforward Neural Networks (QFNNs)
Quantum Convolutional Neural Networks (QCNNs)
Quantum Generative Adversarial Networks (Quantum GANs)
Quantum Autoencoders or Quantum Recurrent Neural Networks (optional)

Who Should Enroll?

This course is ideal for anyone interested in learning about quantum computing.

Why Apply?

Elite Recognition: Backed by the EIT and funded by the European Commission.
Hands-On Training: Dive into practical Quantum Computing applications.
Shape the Future: Join a select group building the next generation of deep tech leaders.