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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"

The postgraduate diploma in Computer Vision and Quantum Computing is oriented toward approaching the subject from a practical point of view.  In this way, a sense of security is generated in the engineer, which will allow him to be more effective in his daily practice. The direct application of the knowledge acquired in real projects is an added professional value that very few professionals specialized in Information and Communication Technologies can offer.  This is precisely what makes this postgraduate diploma unique in the market, since the engineers who take it will be unique professionals in their sector.

Obtaining the right knowledge and advice will be essential in order to take advantage of the developments that are taking place and will take place in the coming years"

General Objectives

• Analyze how a computer is capable of identifying image
• Determine how the convolution layer works and how Transfer Learning works
• Identify the different types of algorithms mainly used in computer vision
• Demonstrate the differences between quantum computing and classical computing
• Analyze the mathematical foundations of quantum computing
• Determine the main quantum operators and develop operational quantum circuits
• Analyze the advantages of quantum computing in examples of quantum "type" problem solving
• Develop and demonstrate the advantages of quantum computing in application solving examples (games, examples, programs)
• Demonstrate the different types of projects achievable with classical Machine Learning techniques and the state of the art in quantum computing
• Develop the key concepts of quantum states, as a generalization of classical probability distributions, and thus to be able to describe quantum systems of many states
• Analyze how to encode classical information in quantum systems.
• Determine the concept of "Kernel Methods" used in classic Machine Learning algorithms
• Develop and implement learning algorithms for classical ML models in quantum models, such as PCA, SVM, neural networks, etc.
• Implement DL model learning algorithms on quantum models, such as GAN

Specific Objectives

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

• Analyze what Computer Vision is
• Determine typical computer vision tasks
• Analyze, step by step, how convolution works and how transfer learning works Transfer Learning 
• Identify what mechanisms we have available to create modified images from our own and have more training data
• Compile typical tasks that can be performed with Computer Vision
• Examine commercial Computer Vision case studies

Module 2. Quantum Computing. A New Model of Computing

• Analyze the need for quantum computing and identify the different types of quantum computers currently available
• Specify the fundamentals of quantum computing and its characteristics
• Examine the applications of quantum computing, advantages and disadvantages
• Determine the basic fundamentals of quantum algorithms and their internal mathematics
• Examine Hilbert space of dimension 2n, n-Qubits, states, quantum gates and their reversibility
• Demonstrating Quantum Teleportation
• Analyze Deutsch's Algorithm, Shor's Algorithm and Grover's Algorithm
• Develop examples of applications with quantum algorithms

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

• Analyze quantum computing paradigms relevant to machine learning
• Examine the various ML algorithms available in quantum computing, both supervised and unsupervised
• Determine the different DL algorithms available in quantum computing
• Fundamentals of the use of the Quantum Fourier Transform in the integration of indicators for quantum ML models, as well as for feature selection
• Develop pure quantum algorithms for solving optimization problems
• Generate specialized knowledge on hybrid algorithms (quantum computation and classical computation) to solve learning problems
• Implementing learning algorithms on quantum computers
• Establish the current status of QML and its immediate future

It addresses quantum computing, in an understandable, simple and friendly way, in order to get into what is undoubtedly the future in the coming years”