Quantum Machine Learning
Quantum Machine Learning
In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning.
What is quantum machine learning?
Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum physics and machine learning. ~Wikipedia
Quantum Computing is a way of parallel execution of multiple processess in a same time using qubit ,It reduces the computation time and size of the processor probably in neuro size.
Quantum computing unlocks amazing parallelism, it takes every path in the corn maze simultaneously; thus, leading to an exponential reduction in the number of steps required to solve a problem. The parallelism comes from the concept of ‘qubit’, ‘superposition’ and ‘entanglement’ derived from Quantum Physics.
Lets understand Quantum Computing in detail first: 1. Quantum is the smallest possible unit of any physical entity, such as energy or mass. 2. Quantum computing uses quantum bits, also called ‘qubits’ to perform operations. 3. Qubits can be represented by: -An electron orbiting a nucleus: where |1> and |0> are the excited state and ground state respectively -A photon: where |1> and |0> are polarizations of the photon. 4. Qubits exist as both 0 AND 1 at the same time. This phenomenon is called ‘superposition’. 5. ‘Quantum entanglement’ is the phenomenon in which quantum particles interact with each other and are described with reference to each other, not independently, even if the particles are separated by a large distance.
Hence, Quantum machine learning is a sub-discipline of quantum information processing research, with the goal of developing quantum algorithms that learn from data in order to improve existing methods in machine learning. A quantum algorithm is a routine that can be implemented on a quantum computer, a device that exploits the laws of quantum theory in order to process information.
Why quantum machine learning is important?
CML Vs QML
As mathematics reached the level of time travel concepts but the computing is still running under classical mechanics . the companies understood, the computing field must have a change from classical to quantum, and they started working on the big Quantum computing field, and the market named this field as Quantum Information Science .The kick start is from Google and IBM with the Quantum Computing processor (D-Wave) for making Quantum Neural Network .The field of Quantum Computer Science and Quantum Information Science will do a big change in AI in the next 10 years.
How quantum machine learning works?
One qubit can exist in both states (0 AND 1) at once. Thus, two interacting qubits can store all 4 binary configurations simultaneously. In general, ’n’ qubits can simultaneously represent ‘2^n’ classical binary configurations. Thus, a 300–qubit quantum computer can explore 2³⁰⁰ possible solutions at the same time, unlike 1 solution at a time in a classical computer, causing immense parallelism. Adding more qubits to a quantum computer would exponentially increase the power of the computer.
In brief, this is how Quantum Computing fit in with Machine Learning:
Quantum versions of ML algorithms -Finding eigenvalues and eigenvectors of large matrices -Finding nearest neighbours on a quantum computer -Quantum methods to improve the Higgs Boson experiment -Quantum algorithm to solve linear systems
Classical ML to analyze quantum systems -Detecting the Quantum Change Point -Binary classification of qubit states -Quantum decoherence -Recreating the values of thermodynamic observables
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