Identifying Gamma Radiation Anomaly Signals Using Quantum Computation Methods
Date
2022-07-28
Authors
Foate, Joshua
Valdez, Luis
Alamaniotis, Miltos
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Abstract
Collected data using a radiation detector to identify anomalies in the presence of naturally occurring radioactive material. Samples of the anomaly signals were put into a Hopfield neural network to train the network to identify whether data from the detector was an anomaly or background radiation. Converting our data into a 3SAT (3- Satisfiability) problem and use Grover's algorithm to find the solutions for Hopfield Artificial Neural Network memory
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undergraduate student works
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Department
Electrical and Computer Engineering