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Quantum Computing for Machine Learning

Have you ever wondered about beating the time? And solving a large number of mathematical matrices, data and queries within just a fraction of second. Then without dilemma, there comes a word “quantum”.

Quantum computers unseal parallelism leading to the reduction of steps required to solve the problems. It uses the qubits that can be represented by the electrons orbiting the nucleus. As compared to our classical computers which work on bits and lies either in zero states or one state but not both at the same time, something strange happens with quantum computers.

The electrons of the qubits fall in an exciting state and ground state and even both states at the same time are called superposition. Besides these, the quantum particles interact with each other and are measured concerning each other. It means if one quantum particle in a pair is spin downstate then the other will be switched to spin up called quantum entanglement. Thus the computations in quantum computers are driven by the phenomenon of superposition and entanglement following the Wave-particle duality principle.

Featuring the superposition of qubits, ‘n’ qubits can explore 2ⁿ binary configurations. For example, ‘50’ qubits can perform 2⁵⁰ solutions at the same pulse, beating the time of the classical computers. The increment of qubits leads to the exponential rise of massive parallelism.

Apart from Quantum computing, fitting with Machine learning has always been challenging in today’s eras. As a subset of Artificial Intelligence, Machine learning algorithms are generally concerned with the collection of data, training of data, evaluation and deployment of the model for the prediction. Recommendation systems, AI-driven chatbot like Alexa, Siri, Prediction Machine, Natural language processing, Speech Recognizer are all the creation of Machine learning and Deep learning algorithms.

While training these models in classical computers, it generally deploys high cost and takes a long time and some can not be computed. But this can be made efficient with quantum support vector machines that facilitate the quantum algorithms and properties like entanglement and interference creating a massive quantum state space causing to fasten and improve kernel evaluation.

Similarly, for finding the Eigenvalues and large matrices of Eigenvectors, Quantum PCA and topology features can be used to find the Eigenvectors and values of high dimensional matrices. Also, clustering of high dimensional data can be done using quantum clustering finding. From Simulation algorithms of Quantum mechanics to the quantum Fourier transform algorithms various evolution has been carried out in Quantum Machine learning to learn the pattern of data that cannot be learnt by classical computers.

But the things are not as easy to implement. It takes about -460F for the qubits to become stable, discouraging the quantum computers with large numbers of qubits. Similarly, a unified quantum learning theory has not been developed, challenging us in this field to solve the unsolved queries.

Taking the problem as a challenge, IBM, Google and Microsoft are making an effort to create the revolutions. Research is carried out to reconstruct the new technology of physics and big data. From new algorithms like Simple quantum Neural Network and Training for solving the problem of training quantum networks to Quantum perceptron operating on classical neurons having non-linear functions are the revolutionary effort. Similarly, experiments like Smoking gun experiments for quantum speedups for Machine learning is yet another discovery in this field.

Quantum Computers for Machine Learning seems weird and tough. But it can be a remarkable innovation in optimizing the problems and changing the pattern of data security to the reduction of power consumption and accelerating the operations. Thus, there is no doubt that quantum machine learning can transform the future world of big data and science.



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