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Robust implementation of a repeater node using a quantum neural network
Fuenteal, Diego Dahn, Jack
Fuenteal, Diego
Dahn, Jack
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2024-04-12
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Fuenteal, Diego; Dahn, Jack. 2024. Robust implementation of a repeater node using a quantum neural network. -- In Proceedings: 23rd Annual Undergraduate Research and Creative Activity Forum. Wichita, KS: Wichita State University, p. 29.
Abstract
A crucial component of computer algorithms is the ability to swap information in two bits. For quantum computing, this is even more crucial, because it is the only way to transmit information, since quantum states cannot be copied. Indeed, the SWAP gate is the basis of all quantum communication. Unfortunately, current quantum computers and communication systems are not robust. Classically, we can protect against noise by making many copies of the information but of course this is not possible quantum mechanically. An algorithm that swaps two entangled quantum bits, or qubits (a SWAP) exists, but it is sensitive to noise and becomes increasingly difficult to implement as the number of qubits in the system grows. This research, building on previous work by Jack Dahn, shows that a Quantum Neural Network (QNN) can be used to perform the swap without having to create a complicated algorithm by hand and with considerably more resistance to noise. This is achieved by training the neural network on small systems and then using the obtained parameters to scale up the system to more qubits (called bootstrapping). The system can be trained with or without noise, with the goal making it as resistant as possible to it.
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Presented to the 23rd Undergraduate Research and Creative Activity Forum (URCAF) held at the Rhatigan Student Center, Wichita State University, April 12, 2024.
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Wichita State University
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URCAF;v.23