Quantum Machine Learning Concepts and Algorithms Course

Explore quantum machine-learning concepts, circuits, data encoding, variational algorithms, quantum kernels and realistic feasibility assessment.

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Quantum Machine Learning Concepts and Algorithms Course

Training Outlines

Introduction

Quantum machine learning explores whether quantum computation can represent or optimise learning problems in ways that differ from classical systems. The field is scientifically promising but remains constrained by limited hardware, noise, data-loading costs and uncertain practical advantage. Professionals therefore need both technical foundations and the judgement to distinguish credible experiments from unsupported claims.

This five-day course provides a rigorous introduction to quantum machine-learning concepts and algorithms. Participants will build quantum circuits in simulation, examine data encoding, implement hybrid variational and kernel methods, evaluate noise and resource demands, and compare results with classical baselines. The course focuses on informed experimentation rather than promising premature commercial superiority.

Objectives

By the end of this course, participants will be able to:

Explain qubits, gates, measurement and entanglement.
Construct and simulate basic quantum circuits.
Compare quantum data-encoding strategies.
Apply variational and quantum-kernel methods.
Build hybrid quantum–classical workflows.
Recognise noise, barren plateaus and scaling limits.
Evaluate claims against classical baselines.
Assess quantum-ML feasibility responsibly.

Course Content

Day 1: Quantum Computing Foundations

Qubits and quantum states
Superposition, interference and measurement
Single and multi-qubit gates
Entanglement and circuit composition
Quantum simulators and hardware noise
Quantum-circuit laboratory

Day 2: Quantum Data Representation

Basis, angle and amplitude encoding
Quantum feature maps
Qubit and circuit-depth requirements
Measurement sampling and estimation
Data-loading bottlenecks
Encoding-strategy laboratory

Day 3: Quantum Machine-Learning Algorithms

Variational quantum circuits
Parameter-shift gradient estimation
Quantum kernels and classifiers
Quantum neural-network concepts
Hybrid quantum–classical optimisation
Variational-model laboratory

Day 4: Training and Experimental Evaluation

Classification and regression workflows
Quantum generative-model concepts
Noise effects and error mitigation
Barren plateaus and trainability
Classical baselines and fair comparison
Quantum-ML experiment review

Day 5: Feasibility and Responsible Adoption

NISQ capabilities and limitations
Resource and scalability assessment
Quantum-advantage evidence standards
Cloud hardware and execution workflows
Organisational use-case screening
Capstone quantum-ML feasibility study

Training Locations

This Quantum Machine Learning Concepts and Algorithms Course is available in multiple cities. Please select your preferred location from the list below

Durrës

Albania

Tirana

Albania

Andorra la Vella

Andorra

Escaldes-Engordany

Andorra

Innsbruck

Austria

Salzburg

Austria

Vienna

Austria

Gomel

Belarus

Minsk

Belarus

Antwerp

Belgium

Brussels

Belgium

Banja Luka

Bosnia and Herzegovina

Sarajevo

Bosnia and Herzegovina

Plovdiv

Bulgaria

Sofia

Bulgaria

Dubrovnik

Croatia

Split

Croatia

Zagreb

Croatia

Limassol

Cyprus

Nicosia

Cyprus

Brno

Czech Republic

Prague

Czech Republic

Aarhus

Denmark

Copenhagen

Denmark

Tallinn

Estonia

Tartu

Estonia

Helsinki

Finland

Tampere

Finland

Lyon

France

Marseille

France

Nice

France

Paris

France

Berlin

Germany

Frankfurt

Germany

Hamburg

Germany

Munich

Germany

Athens

Greece

Thessaloniki

Greece

Budapest

Hungary

Debrecen

Hungary

Akureyri

Iceland

Reykjavík

Iceland

Jakarta

Indonesia

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Osaka

Japan

Tokyo

Japan

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

Kuala Lumpur

Malaysia

Malé

Maldives

St. Julian's

Malta

Valletta

Malta

Bălți

Moldova

Chișinău

Moldova

La Condamine

Monaco

Monte Carlo

Monaco

Budva

Montenegro

Podgorica

Montenegro

Amsterdam

Netherlands

Rotterdam

Netherlands

The Hague

Netherlands

Abuja

Nigeria

Lagos

Nigeria

Port Harcourt

Nigeria

Ohrid

North Macedonia

Skopje

North Macedonia

Bergen

Norway

Oslo

Norway

Gdańsk

Poland

Kraków

Poland

Warsaw

Poland

Faro

Portugal

Lisbon

Portugal

Porto

Portugal

Bucharest

Romania

Cluj-Napoca

Romania

City of San Marino

San Marino

Serravalle

San Marino

Belgrade

Serbia

Novi Sad

Serbia

Singapore

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Incheon

South Korea

Seoul

South Korea

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Sri Jayawardenepura Kotte

Sri Lanka

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kaohsiung

Taiwan

Taipei

Taiwan

Ankara

Turkey

Istanbul

Turkey

Kyiv

Ukraine

Lviv

Ukraine

Odesa

Ukraine

Dubai

United Arab Emirates

Birmingham

United Kingdom

Edinburgh

United Kingdom

London

United Kingdom

Manchester

United Kingdom

Rome (Vatican-adjacent)

Vatican City

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Quantum Machine Learning Concepts and Algorithms Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.