Those who acquire knowledge now in quantum technologies will be the leaders in programming in the near-term future"

Quantum Computing has advanced rapidly in both theory and practice in recent years and with it the hope of potential impact on real applications. Quantum Computing are able to naturally solve certain problems with complex correlations between inputs that can be incredibly difficult for traditional computers. This Postgraduate certificate Analyzes in which situations a quantum advantage could be achieved ”, in the context of advanced analytics and artificial intelligence in the industrial field."

Learning models developed on quantum computers are much more powerful for applications in the search for an optimal solution, both at the level of the best selection of hyperparameters in machine learning algorithms, as well as in cases of scenario optimization. This is because they allow much faster computation, better generalization with less data, or both. Those the computer scientists who acquire knowledge now in quantum technologies will be the leaders in programming in the near-term future."

Additionally, the student has the best study methodology 100% online, which eliminates the need to attend classes in person or have to comply with a predetermined schedule. To this end, in only 6 weeks will delve into the scope of application of Quantum Computing, understanding the competitive advantages they provide, so they will be positioned at the technological forefront and will be able to lead ambitious projects in the present and in the future.

A historic technological revolution associated with the development of new quantum platforms is underway"

This Postgraduate certificate in Quantum Computing contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Computing quantum
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection 

Quantum sensors and actuators will enable computer scientists to navigate the nanoscale world with remarkable precision and sensitivity"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

Its multimedia content, developed with the latest educational technology, will allow professionals to learn in a contextual and situated learning environment, i.e., a simulated environment that will provide immersive education programmed to prepare in real situations.

The design of this program focuses on Problem-Based Learning, by means of which professionals must try to solve the different professional practice situations that arise during the academic course. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.

The quantum revolution is already underway, and the possibilities ahead of you are limitless"

Determine the main quantum operators and develop operational circuits"

A curriculum has been established that offers a broad perspective on Quantum Computing, a technology that has advanced rapidly in both theory and practice in recent years and with it, the hope of potential impact on real applications.This Diploma course provides theoretical and practical insights into the design, development and applications, with a focus on quantum machine learning. 

Delve into the conception, development and applications of Quantum Computing, focusing on quantum machine learning"

Module 1. Quantum Computing. A New Model of Computing

1.1. Quantum Computing

1.1.1. Differences with Classical Computing
1.1.2. Need for Quantum Computing
1.1.3. Quantum Computers Available: Nature and Technology

1.2. Applications of Quantum Computing

1.2.1. Quantum Computing vs. Classical Computing Applications
1.2.2. Contexts of Use
1.2.3. Application in Real Cases

1.3. Mathematical Foundations of Quantum Computing

1.3.1. Computational Complexity
1.3.2. Double Slit Experiment. Particles and Waves
1.3.3. Intertwining

1.4. Geometric Foundations of Quantum Computing

1.4.1. Qubit and Complex Two-Dimensional Hilbert Space
1.4.2. Dirac's General Formalism
1.4.3. N-Qubits States and Hilbert Space of Dimension 2n

1.5. Mathematical Fundamentals of Linear Algebra

1.5.1. The Domestic Product
1.5.2. Hermitian Operators
1.5.3. Eigenvalues and Eigenvectors

1.6. Quantum Circuits

1.6.1. Bell States and Pauli Matrices
1.6.2. Quantum Logic Gates
1.6.3. Quantum Control Gates

1.7. Quantum Algorithms

1.7.1. Reversible Quantum Gates
1.7.2. Quantum Fourier Transform
1.7.3. Quantum Teleportation

1.8. Algorithms Demonstrating Quantum Supremacy

1.8.1. Deutsch´s Algorithm
1.8.2. Shor´s Algorithm
1.8.3. Grover´s Algorithm

1.9. Quantum Computer Programming

1.9.1. My First Program on Qiskit (IBM)
1.9.2. My First Program on Ocean (Dwave)
1.9.3. My First Program on Cirq (Google)

1.10. Application on Quantum Computers

1.10.1. Creation of Logical Gates

1.10.1.1. Creation of a Quantum Digital Adder

1.10.2. Creation of Quantum Games
1.10.3. Secret Key Communication between Bob and Alice

Module 2. Quantum Machine Learning. Future Artificial Intelligence

2.1. Classical Machine Learning Algorithms

2.1.1. Descriptive, Predictive, Proactive and Prescriptive Models
2.1.2. Supervised and Unsupervised Models
2.1.3. Feature Reduction, PCA, Covariance Matrix, SVM, Neural Networks
2.1.4. ML Optimization: Gradient Descent

2.2. Classic Deep Learning Algorithms

2.2.1. Boltzmann Networks. The Machine Learning Revolution
2.2.2. Deep Learning Models. CNN, LSTM, GANs
2.2.3. Encoder-Decoder Models
2.2.4. Signal Analysis Models. Fourier Analysis

2.3. Quantum Classifiers

2.3.1. Quantum Classifier Generation
2.3.2. Amplitude Coding of Data in Quantum States
2.3.3. Encoding of Data in Quantum States by Phase/Angle
2.3.4. High-Level Coding

2.4. Optimization Algorithms

2.4.1. Quantum Approximate Optimization Algorithm (QAOA)
2.4.2. Variational Quantum Eigensolvers (VQE)
2.4.3. Quadratic Unconstrained Binary Optimization (QUBO)

2.5. Optimization Algorithms Examples:

2.5.1. PCA with Quantum Circuits
2.5.2. Optimization of Stock Packages
2.5.3. Optimization of logistics routes

2.6. Quantum Kernels Machine Learning

2.6.1. Variational Quantum Classifiers. QKA
2.6.2. Quantum Kernels Machine Learning
2.6.3. Classification Based on Quantum Kernel
2.6.4. Clustering Based on Quantum Kernel

2.7. Quantum Neural Networks

2.7.1. Classical Neural Networks and Perceptron
2.7.2. Quantum Neural Networks and Perceptron
2.7.3. Quantum Convolutional Neural Networks

2.8. Advanced Deep Learning (DL) Algorithms

2.8.1. Quantum Boltzmann Machines
2.8.2. General Adversarial Networks
2.8.3. Quantum Fourier Transformation, Quantum Phase Estimation and Quantum Matrix

2.9. Machine Learning Use Case

2.9.1. Experimentation with VQC (Variational Quantum Classifier)
2.9.2. Experimentation with Quantum Neural Networks
2.9.3. Experimentation with GANS

2.10. Quantum Computing and Artificial Intelligence

2.10.1. Quantum Capacity in ML Models
2.10.2. Quantum Knowledge Graphs
2.10.3. The Future of Quantum Artificial Intelligence

Quantum algorithms are already changing the world of computing. Perform a theoretical-practical analysis of them"