A Generative model to represent real time classical data as quantum state

Existence of quantum systems in superposition states gives an exponential advantage over classical system in both time and space. In this paper we will look at reconstructing real time classical data as a pure quantum state. Each classical data sample eg. each pixel location in an images is indexed by a
$\mathbf{n}$
bit string representing one of the basis superposition states. The amplitude of the state which represents probability of measuring the state can be encoded as the normalized value of the sample at that location. A parameterized circuit[2] is used to prepare the quantum state as superposition of basis states from initial ground state. Each basis state corresponds to a sample classical data (eg. a pixel in case of an image). Quantum amplitude estimation based on Grovers algorithm[15] is used to estimate the amplitudes of each of these states. The parameters of the circuit is adjusted to minimize a loss function using classical gradient descent algorithm. The normalized amplitude at the output of our circuit is multiplied by a normalization factor that can also be tuned to represent the original data sample. This enables us to use the original real time data in our loss function computation. Section 1 of the paper gives a brief introduction. Section II covers the related works. Section III talks about amplitude encoding. Section IV describes quantum state preparation using supervised Learning. Section V describes implementation of the algorithms and finally Section VI gives the final conclusion of the research paper along with a result from the algorithm.