Acquiring knowledge in quantum technologies at this time will make you a leader in programming in the near-term future” 

Training a model from scratch implies having a large amount of previously catalogued information, approximately 10,000 photos of each of the types to be differentiated. This takes hours to achieve good results. In these cases, previously trained models can be used as a starting point, through the Transfer Learning resource. This Postgraduate diploma examines what network models are currently available to facilitate model training using this technique. 

The graduate will analyze the main use cases that exist for computer vision: classification, object detection, object identification and object tracking. For example, Google uses these algorithms to be able to search from images. Facebook, for example, uses them to automatically identify and tag people in a photo. 

Quantum computing has advanced rapidly in both theory and practice in recent years and, with it, the hope of potential impact on real applications. 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 program analyzes in which situations a quantum advantage, in the context of advanced analytics and artificial intelligence, could be achieved for the engineering world. The objective of this Postgraduate diploma is to show what benefits current and future quantum technologies can provide to machine learning, focusing on algorithms that are challenging for classical digital computers, such as Kernel-based models, optimization and convolutional networks. 

As it is a 100% online University Expert, the student is not conditioned by fixed schedules or the need to move to another physical location. Using a device with Internet access, you can 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 your work and personal life with your academic life at your own pace. 

This training will allow you to advance in your career in a seamless way”

This Postgraduate diploma in Computer Vision and Quantum Computing the most comprehensive 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 they are created, provide practical information on the disciplines that are essential for professional practice
• Practical exercises, where the self-assessment 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 are facing an emerging market where getting the right knowledge and advice is going to be paramount in order to take advantage of evolutions”  

The program’s teaching staff includes professionals from the 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.

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

You will see the benefits that current and future quantum technologies can provide to machine learning, focusing on algorithms"

Professionals from the sector have brought together in three modules the latest advances in Computer Vision and Quantum Computing. This Postgraduate diploma covers the construction of convolutional neural networks, quantum circuits and classical Machine Learning  algorithms, including 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.

You will have a global vision of the application of the different technologies involved in global digitalization and the ability to apply them" 

Module 1. R&D+I.A. Computer Vision. Object Identification and Tracking

1.1. Computer Vision 

1.1.1. Computer Vision 
1.1.2. Computational Vision 
1.1.3. Machine Interpretation of 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. Convolutional Neural Network Construction 

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

1.4. Convolution Process 

1.4.1. Functioning 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. Fast Detection with YOLO 
1.8.4. Other Possible Solutions 

1.9. GAN Generative Adversarial Networks, or Generative Adversarial Networks 

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

1.10. Computer Vision Model Application 

1.10.1. Organization of Contents 
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. Plague Identification 
1.10.8. Health 

Module 2. Quantum Computing A New Model of Computing 

2.1. Quantum Computing 

2.1.1. Differences with Classic 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. Context 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. Entanglement 

2.4. Geometries 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 Linear Algebra 

2.5.1. The Internal 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 Qiskit Program (IBM) 
2.9.2. My First Ocean Program (Dwave) 
2.9.3. My First Cirq Program (Google) 

2.10. Application on Quantum Computers 

2.10.1. Creating Logistic 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 (AI) of the Future 

3.1. Classic 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: the Gradient Descent 

3.2. Classic Machine Learning  Algorithms 

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

3.3. Quantum Classifiers 

3.3.1. Generating 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 Logistic Routes 

3.6. Quantum Kernels Machine Learning 

3.6.1. Variational Quantum Classifiers. QKA 
3.6.2. Quantum Kernel 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 the Perceptron 
3.7.2. Quantum Neural Networks and the Perceptron 
3.7.3. Quantum Convolutional Neural Networks 

3.8. Deep Learning (DL) Advanced 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 are in front of the best degree to learn about the latest advances in Computer Vision and Quantum Computing"