Abstract
The paper presents an approach to represent a quantum bit as a neuron, which uses a threshold function. The functionality of the perceptron model of a neuron is modeled using qubit, which can be used as a building block for the quantum neural network. Several approaches for building and training a Quantum Neural Network have been proposed, namely step function as measurement, quantum dots, quantum associative memory, and perceptron models. But, the functional model of the elementary quantum neuron is not described in much detail. We attempt to build such quantum neuron using qubits and perform its function using qubit rotation operation.
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Venkat Raman, B., Hedge, N.P., Mallesh, D., Anjaneyulu, B. (2019). Quron: Basic Representation and Functionality. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-13-7082-3_39
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DOI: https://doi.org/10.1007/978-981-13-7082-3_39
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