THESIS
2022
1 online resource (ix, 49 pages) : illustrations (some color)
Abstract
The field of quantum computing is quickly gaining attention from the society since the quantum supremacy claimed by Google in 2019. It can bring many applications such as many body system simulation and cryptography to a more advance level, makes it a multi-discipline subject. Same as the classical computers, error correction is essential in order to do any quantum computation, which is called quantum error correction(QEC). However, designing QEC codes require an error model to predict the error types, which is manufactured and may not be compatible for any situation. Also, since we are in the noisy intermediate-scale quantum(NISQ) era, QEC is hard to implement due to technical issues. In this work, I trained a neural network to mitigate the error instead of using QEC code to correct it...[
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The field of quantum computing is quickly gaining attention from the society since the quantum supremacy claimed by Google in 2019. It can bring many applications such as many body system simulation and cryptography to a more advance level, makes it a multi-discipline subject. Same as the classical computers, error correction is essential in order to do any quantum computation, which is called quantum error correction(QEC). However, designing QEC codes require an error model to predict the error types, which is manufactured and may not be compatible for any situation. Also, since we are in the noisy intermediate-scale quantum(NISQ) era, QEC is hard to implement due to technical issues. In this work, I trained a neural network to mitigate the error instead of using QEC code to correct it. The network can identify the background noise and tells us what the uninterrupted states look like by reproducing them. Yet, the accuracy can be further improved by adopting different classification approach, which may also reduce the memory requirement so that it can deal with more qubits. The present work gives a concept on building a fast and neat neural network to mitigate general quantum errors, as well as to realize the background error, which may help us better understand the noise and describe them in a model.
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