If you are looking for professional excellence, join us and we'll help you achieve it”

Training and specializing in quantum computing is a winning bet. It is today and will undoubtedly be even more so in the future. A key area of interest and where quantum computing is proving to be most efficient is in the field of Machine Learning and its application in real proactive, predictive and prescriptive problems.

This Postgraduate diploma analyzes in which situations a quantum advantage could be achieved in the context of advanced analytics and artificial intelligence for the engineering world. The goal is to show what benefits current and future quantum technologies can provide to machine learning, focusing on algorithms such as Kernel-based models, optimization and convolutional networks. 

In addition, in this training the graduate will analyze the main case studies that exist for computer vision: classification, object detection, object identification, object tracking. In addition, through the Transfer Learning, resource, you will examine what network models are currently available to facilitate model training, applying this technique to your industrial project. 

As it is a 100% online Postgraduate diploma, the student is not conditioned by fixed schedules or the need to move to another physical location.  Using a device with internet access, you will be able to consult the rich content that will help you acquire quantum computing techniques, to reach the elite in the computer industry.  All of this, at any time of the day, combining, at your own pace, your work and personal life with your academic life.

You are looking at a qualification that will progressively and steadily lead you to the acquisition of the knowledge and competencies you need" 

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

• The development of case studies presented by experts in Computer Vision 
  and Quantum Computing
• The graphic, schematic and eminently practical contents, with which it is conceived, provide practical information on those disciplines that are essential for professional practice
• Practical exercises, where the self-evaluation process can be carried out 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

You will examine which network models are currently available, in order to facilitate the training of our model by applying the Transfer Learning technique"

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

The multimedia content, developed with the latest educational technology, will provide professionals with situated and contextual learning, i.e., a simulated environment that will provide immersive training, designed for training oneself in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.

Increase your skills in developing industry solutions with Machine Vision and set yourself up for success"

Training and specializing in Quantum Computing is a winning bet to boost your career"

Renowned engineers have selected the best didactic material and have gathered in three modules the latest advances in Computer Vision and Quantum Computing. Thus, this Postgraduate diploma covers everything from the construction of convolutional neural networks, quantum circuits and classical Machine Learning algorithms, to the Transfer Learning concept and the programming of quantum computers, among others. To this end, this program delves into the scope of application of each technology in the world of engineering, understanding the competitive advantages they provide in the industrial sector.

Analyze in which situations a quantum advantage could be achieved in the context of advanced analytics and artificial intelligence in the industrial field"

Module 1. R&D+A.I. Computer Vision: Identification and Tracking of Objects

1.1. Computer Vision

1.1.1. Computer Vision
1.1.2. Computational Vision
1.1.3. Interpretation of the Machines in an Image

1.2. Activation Functions

1.2.1. Activation Functions
1.2.2. Sigmoid
1.2.3. ReLU
1.2.4. Hyperbolic Tangent
1.2.5. Softmax

1.3. Construction of Convolutional Neural Networks

1.3.1. Convolution Operation
1.3.2. Capa ReLU
1.3.3. Pooling
1.3.4. Flattering
1.3.5. Full Connection

1.4. Convolution Process

1.4.1. Operation of a Convolution
1.4.2. Convolution Code
1.4.3. Convolution: Application

1.5. Transformations with Images

1.5.1. Transformations with Images
1.5.2. Advanced Transformations 
1.5.3. Transformations with Images. Application
1.5.4. Transformations with Images. Use Case

1.6. Transfer Learning

1.6.1. Transfer Learning
1.6.2. Transfer Learning. Typology
1.6.3. Deep Networks to Apply Transfer Learning

1.7. Computer Vision. Use Case

1.7.1. Image Classification
1.7.2. Object Detection
1.7.3. Object Identification
1.7.4. Object Segmentation

1.8. Object Detection

1.8.1. Detection from Convolution
1.8.2. R-CNN, Selective Search
1.8.3. Rapid Detection with YOLO
1.8.4. Other Possible Solutions

1.9. GAN. Generative Adversarial Networks

1.9.1. Generative Adversarial Networks
1.9.2. Code for a GAN
1.9.3. GAN. Application

1.10. Application of Computer Vision Models

1.10.1. Content Organization
1.10.2. Visual Search Engines
1.10.3. Facial Recognition
1.10.4. Augmented Reality
1.10.5. Autonomous Driving
1.10.6. Fault Identification at each Assembly
1.10.7. Pest Identification
1.10.8. Health

Module 2. Quantum Computing. A New Model of Computing

2.1. Quantum Computing

2.1.1. Differences with Classical Computing
2.1.2. Need for Quantum Computing
2.1.3. Quantum Computers Available: Nature and Technology 

2.2. Applications of Quantum Computing

2.2.1. Applications of Quantum Computing vs. Classical Computing
2.2.2. Contexts of Use
2.2.3. Application in Real Cases

2.3. Mathematical Foundations of Quantum Computing

2.3.1. Computational Complexity 
2.3.2. Double Slit Experiment. Particles and Waves
2.3.3. Intertwining

2.4. Geometric Foundations of Quantum Computing

2.4.1. Qubit and Complex Two-Dimensional Hilbert Space
2.4.2. Dirac's General Formalism
2.4.3. N-Qubits States and Hilbert Space of Dimension 2n

2.5. Mathematical Fundamentals of Linear Algebra

2.5.1. The Domestic Product
2.5.2. Hermitian Operators
2.5.3. Eigenvalues and Eigenvectors

2.6. Quantum Circuits

2.6.1. Bell States and Pauli Matrices
2.6.2. Quantum Logic Gates
2.6.3. Quantum Control Gates

2.7. Quantum Algorithms

2.7.1. Reversible Quantum Gates
2.7.2. Quantum Fourier Transform
2.7.3. Quantum Teleportation

2.8. Algorithms Demonstrating Quantum Supremacy

2.8.1. Deutsch´s Algorithm
2.8.2. Shor´s Algorithm
2.8.3. Grover´s Algorithm

2.9. Quantum Computer Programming

2.9.1. My First Program on Qiskit (IBM) 
2.9.2. My First Program on Ocean (Dwave)
2.9.3. My First Program on Cirq (Google)

2.10. Application on Quantum Computers

2.10.1. Creation of Logical Gates

2.10.1.1. Creation of a Quantum Digital Adder

2.10.2. Creation of Quantum Games
2.10.3. Secret Key Communication between Bob and Alice

Module 3. Quantum Machine Learning: The Artificial Intelligence (A.I) of the Future

3.1. Classical Machine Learning Algorithms

3.1.1. Descriptive, Predictive, Proactive and Prescriptive Models 
3.1.2. Supervised and Unsupervised Models
3.1.3. Feature Reduction, PCA, Covariance Matrix, SVM, Neural Networks
3.1.4. Optimization in ML: Gradient Descent

3.2. Classical Deep Learning Algorithms

3.2.1. Boltzmann Networks: the revolution in Machine Learning 
3.2.2. Models of Deep Learning: CNN, LSTM, GAN
3.2.3. Encoder-Decoder Models 
3.2.4. Signal Analysis Models: Fourier Analysis 

3.3. Quantum Classifiers

3.3.1. Generation of a Quantum Classifier
3.3.2. Amplitude Coding of Data in Quantum States
3.3.3. Encoding of Data in Quantum States by Phase/Angle 
3.3.4. High-Level Coding

3.4. Optimization Algorithms 

3.4.1. Quantum Approximate Optimization Algorithm (QAOA)
3.4.2. Variational Quantum Eigensolvers (VQE)
3.4.3. Quadratic Unconstrained Binary Optimization (QUBO)

3.5. Optimization Algorithms Examples:

3.5.1. PCA with Quantum Circuits
3.5.2. Optimization of Stock Packages
3.5.3. Optimization of logistics routes

3.6. Quantum Kernels Machine Learning

3.6.1. Variational Quantum Classifiers. QKA
3.6.2. Quantum Kernels Machine Learning
3.6.3. Classification Based on Quantum Kernel
3.6.4. Clustering Based on Quantum Kernel

3.7. Quantum Neural Networks

3.7.1. Classical Neural Networks and Perceptron
3.7.2. Quantum Neural Networks and Perceptron
3.7.3. Quantum Convolutional Neural Networks

3.8. Advanced Deep Learning (DL) Algorithms 

3.8.1. Quantum Boltzmann Machines
3.8.2. General Adversarial Networks
3.8.3. Quantum Fourier Transformation, Quantum Phase Estimation and Quantum Matrix

3.9. Machine Learning Use Case

3.9.1. Experimentation with VQC (Variational Quantum Classifier)
3.9.2. Experimentation with Quantum Neural Networks
3.9.3. Experimentation with qGANS

3.10. Quantum Computing and Artificial Intelligence

3.10.1. Quantum Capacity in ML Models
3.10.2. Quantum Knowledge Graphs
3.10.3. The future of Quantum Artificial Intelligence

You will be up to date on the latest advances in Computer Vision and Quantum Computing in the engineering field"