Quantum error correction using quantum convolutional neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Glavni autori: | , , , , |
|---|---|
| Daljnji autori: | |
| Format: | Disertacija |
| Jezik: | English |
| Izdano: |
Brac University
2021
|
| Teme: | |
| Online pristup: | http://hdl.handle.net/10361/14966 |
| id |
10361-14966 |
|---|---|
| record_format |
dspace |
| spelling |
10361-149662022-01-26T10:15:48Z Quantum error correction using quantum convolutional neural network Mishu, Niloy Deb Roy Meem, Fatema Islam Ridwan, A. E. M Rahman, Mohammad Mushfiqur Mary, Mekhala Mariam Ahmed, Shahnewaz Das, Sowmitra Department of Computer Science and Engineering, Brac University Quantum Computing Quantum Machine Learning Qiskit Deep Neural Network CNN QCNN Quantum computing. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-40). Quantum computing with its powerful attributes - entanglement and superposition, is revolutionizing modern computation. Moreover, quantum computation can be applied to a wide range of real-world applications, including cybersecurity and cryptography, computational chemistry, artificial intelligence, prime factorization, and so on. However, the biggest impediment of quantum computation is quantum error. Often the decoherence caused by the environment or any other malfunction creates quantum errors which can arbitrarily change the state of a quantum system and destroying its information content - bit flip & phase flip error, unwanted measurement error, etc. A specific Quantum Error Correction (QEC) code can rectify some particular errors. But, it is usually not optimized for any random error. As of today, ‘Deep Learning’ techniques in analyzing data have been very promising which is influencing researchers to apply these methods to ‘Quantum Computation’ problems. We have implemented a Quantum Convolutional Neural Network (QCNN) using Parameterized Quantum Circuit (PQC) on IBM’s open source Quantum Computing framework QISKIT. The generic structure of the QCNN consists of variational forms of the encoder and decoder of the error correction code, which is optimized during training. In this way, it constructs a quantum error correction method for a certain error model. We were able to retrieve quantum states with as much as 90% fidelity rate from the experiments. As a result, our model achieves a high fidelity while using relatively few parameters, which can be generalized for any error model. Niloy Deb Roy Mishu Fatema Islam Meem A. E. M Ridwan Mohammad Mushfiqur Rahman Mekhala Mariam Mary B. Computer Science 2021-09-03T12:08:43Z 2021-09-03T12:08:43Z 2021 2021-06 Thesis ID 17301081 ID 21141074 ID 17301208 ID 17301097 ID 17101368 http://hdl.handle.net/10361/14966 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 40 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Quantum Computing Quantum Machine Learning Qiskit Deep Neural Network CNN QCNN Quantum computing. |
| spellingShingle |
Quantum Computing Quantum Machine Learning Qiskit Deep Neural Network CNN QCNN Quantum computing. Mishu, Niloy Deb Roy Meem, Fatema Islam Ridwan, A. E. M Rahman, Mohammad Mushfiqur Mary, Mekhala Mariam Quantum error correction using quantum convolutional neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Ahmed, Shahnewaz |
| author_facet |
Ahmed, Shahnewaz Mishu, Niloy Deb Roy Meem, Fatema Islam Ridwan, A. E. M Rahman, Mohammad Mushfiqur Mary, Mekhala Mariam |
| format |
Thesis |
| author |
Mishu, Niloy Deb Roy Meem, Fatema Islam Ridwan, A. E. M Rahman, Mohammad Mushfiqur Mary, Mekhala Mariam |
| author_sort |
Mishu, Niloy Deb Roy |
| title |
Quantum error correction using quantum convolutional neural network |
| title_short |
Quantum error correction using quantum convolutional neural network |
| title_full |
Quantum error correction using quantum convolutional neural network |
| title_fullStr |
Quantum error correction using quantum convolutional neural network |
| title_full_unstemmed |
Quantum error correction using quantum convolutional neural network |
| title_sort |
quantum error correction using quantum convolutional neural network |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14966 |
| work_keys_str_mv |
AT mishuniloydebroy quantumerrorcorrectionusingquantumconvolutionalneuralnetwork AT meemfatemaislam quantumerrorcorrectionusingquantumconvolutionalneuralnetwork AT ridwanaem quantumerrorcorrectionusingquantumconvolutionalneuralnetwork AT rahmanmohammadmushfiqur quantumerrorcorrectionusingquantumconvolutionalneuralnetwork AT marymekhalamariam quantumerrorcorrectionusingquantumconvolutionalneuralnetwork |
| _version_ |
1814308323704438784 |