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Quantum Learning Theory for Future Prospects

The famous theoretical physicist Richard Phillips Feynman had an idea on quantum theory regarding quantum computation. The idea was that computational perspective on quantum physics might produce an efficient method to simulate complex quantum physical system like molecules. This idea was carried further by David Elieser Deutsch. Theory and research given by Feynman and David Elieser Deutsch motivates the scientists to look into theoretical approach of quantum computers. This theoretical approach leads the scientists to look in the Turing machine which utilize the quantum mechanics principles and expect to give theoretical computational speedups over classical Turing machines. This is the beginning of new era in the field of quantum computing.

Quantum computing is an upcoming way of computation that is purely based on equation of quantum mechanics and on the laws of quantum mechanics to process information. Classical computing is based on small unit (based on binary digits that are 0 or 1). These 0 and 1 digits are known as Bits in computer field. All data processed in the classical computer, in the form of binary digits but quantum computing uses bits which are named as Qubits. These Qubits are present in two states in the quantum system. As compared to classical bits these quantum bits can store more information because of the quantum properties like superposition and entanglement. Superposition is the most important property of quantum system in which the system holds multiple states in the same time. Entanglement is the back bone of quantum information theory that has no classical counterpart. This investigation and theory of quantum system got the attention of organization. Quantum computation, a part of quantum system, introduced the quantum learning theory which attracted the organizations and data scientists into computing field to show that there is a change from classical to quantum world. The different communities in the field of security and intelligence existing worldwide got the attention and led to an outbreak of interest in academia as well as industrial point of view in quantum computer science. One of the growing fields of quantum computer science is Quantum Learning Theory. As compared to classical machine learning, quantum machine learning processed data in efficient manner because of entanglement and superposition of quantum system. Basically machine learning is a part of computer science in which some patterns are made from unknown input data. They have their own algorithm to process data known as machine learning algorithm. There are lots of data present in this world and we have to process them to get desired goal. For this we use machine learning algorithm, it has the ability to make sense of previously unknown inputs to achieve desired goal. The machine learning algorithm performs different tasks like image processing, speech recognition, pattern identification or strategy optimization. These are the basic task that have significant role in today's digital scenario.

There are lot of examples that exist in this computer edge of machine learning algorithms. For example in 1977, Larry Page invented Google page ranking machine learning algorithm for search engine, which led to the rise in biggest IT organization in the world. There are more important application of machine learning algorithm like spam mail filters, IRIS recognition for security systems, assessing risk in the financial sector. Basically machine learning algorithm encountered huge amount of previously collected input-output data. Machine learning algorithm has the ability or we can say that they are very efficient to deal with this type of data. This data is growing year by year (approximately 21 % every year). For handling and processing this data called big data, scientists and researchers exploit the potential of quantum computing in order to optimize classical machine learning algorithms.