Classification with Quantum Machine Learning: A Survey

Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more. Fully towards to combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) to solve and improve problems that displayed in classical machine learning (e.g. time and energy consumption, kernel estimation). The aim of this paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML). Especially, recent QML classification works. Also, we cover about 30 publications that are published lately in Quantum Machine Learning (QML). we propose a classification scheme in the quantum world and discuss encoding methods for mapping classical data to quantum data. Then, we provide quantum subroutines and some methods of Quantum Computing (QC) in improving performance and speed up of classical Machine Learning (ML). And also some of QML applications in various fields, challenges, and future vision will be presented

State transfer with separable optical beams and variational quantum algorithms with classical light

Classical electromagnetic fields and quantum mechanics -- both obey the principle of superposition alike. This opens up many avenues for simulation of a large variety of phenomena and algorithms, which have hitherto been considered quantum mechanical. In this paper, we propose two such applications. In the first, we introduce a new class of beams, called equivalent optical beams, in parallel with equivalent states introduced in [Bharath & Ravishankar, https://doi.org/10.1103/PhysRevA.89.062110]. These beams have the same information content for all practical purposes. Employing them, we show how to transfer information from one degree of freedom of classical light to another, without any need for classically entangled beams. Next, we show that quantum machine learning can be performed with OAM beams through the implementation of a quantum classifier circuit. We provide explicit protocols and explore the possibility of their experimental realization.Comment: 12 pages, 8 figures. Comments are welcom