International Journal of Reciprocal Symmetry and Theoretical Physics

Peer Reviewed Article

Vol. 5 (2018)

Quantum Vision Investigations Frame Worked after Long Short-Term Typed Memory

Submitted
9 January 2018
Published
16-02-2018

Abstract

In this paper, we show the manner in which machine learning models experiments when it comes to quantum physics. The cornerstone of the newfangling quantum technologies like quantum cryptography and quantum computation is quantum entanglement. The ones that of greater interest are complicated quantum conditions having over two particles as well as a significant count of entangled quantum stages. Considering a high-dimensional and multi-particle state like this, it is not often possible to reframe an experimental premise that will be able to generate the same results. As such, in order to discover interesting experiments, one needs to, at random, formulate millions of premises or setups on a computer, after which one will compute the output states respectively. This work is used to demonstrate that machine learning models are capable of providing more substantial development compared to random searches. The paper shows how a Long Short-Term Memory Network (also called LSTM) is capable of effectively learning how to handle modelling for quantum experiments through the accurate prediction of the output state attributes for particular setups while not having to make computing states an essential consideration. With this approach, one is not only able to conduct faster searches but also be able to take a critical step towards the modelling of high-dimensional quantum tests with the use of generative machine learning algorithms.

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