Implementation of Quantum Error Correction Code on Qubit Superconducting to Improve Quantum Computing Stability
Article Sidebar
-
QECC, Quantum Computing, Qubit Superconducting
Abstract
The background of this research focuses on the stability of quantum computing, which is a major challenge in the development of quantum technology. Superconducting qubits are known to be prone to errors due to environmental disturbances and noise, which hinders computational accuracy. Quantum error correction code (QECC) emerged as a solution to solve the problem by detecting and correcting errors that occur in qubits. This study aims to evaluate the application of QECC to superconducting qubits in improving the stability and accuracy of quantum computing. The method used was a quantitative experiment by comparing the qubit error rate before and after the implementation of QECC, with measurements on bit-flip, phase-flip, and decoherence errors. The results showed that the application of QECC successfully reduced the bit-flip and phase-flip error rates from 15.3% to 5.2% and 12.4% to 4.8%, respectively, while the decoherence decreased from 25.6% to 9.3%. These findings suggest that QECC can significantly improve the stability of quantum computing on superconducting qubits. The conclusion of this study is that the implementation of QECC can be an important step in improving efficiency and accuracy in quantum computing systems, although there are still limitations related to scalability and resources required for deployment in larger systems
Full text article
References
Ali, S. (2022). When software engineering meets quantum computing. Communications of the ACM, 65(4), 84–88. https://doi.org/10.1145/3512340
Anwar, K. (2021). Short Quantum Accumulate Codes with High Rate and Multiple Error Corrections Capability. Proceedings of the 2021 IEEE Symposium on Future Telecommunication Technologies, SOFTT 2021, Query date: 2024-11-29 22:20:32, 81–87. https://doi.org/10.1109/SOFTT54252.2021.9673151
Atchade-Adelomou, P. (2021). Qrobot: A quantum computing approach in mobile robot order picking and batching problem solver optimization. Algorithms, 14(7). https://doi.org/10.3390/a14070194
Avron, J. (2021). Quantum advantage and noise reduction in distributed quantum computing. Physical Review A, 104(5). https://doi.org/10.1103/PhysRevA.104.052404
Bepari, K. (2021). Towards a quantum computing algorithm for helicity amplitudes and parton showers. Physical Review D, 103(7). https://doi.org/10.1103/PhysRevD.103.076020
Bernal, D. E. (2022). Perspectives of quantum computing for chemical engineering. AIChE Journal, 68(6). https://doi.org/10.1002/aic.17651
Cai, W. (2021). Bosonic quantum error correction codes in superconducting quantum circuits. Fundamental Research, 1(1), 50–67. https://doi.org/10.1016/j.fmre.2020.12.006
Darmawan, A. S. (2021). Practical Quantum Error Correction with the XZZX Code and Kerr-Cat Qubits. PRX Quantum, 2(3). https://doi.org/10.1103/PRXQuantum.2.030345
Gill, S. L. (2020). Qualitative Sampling Methods. Journal of Human Lactation, 36(4), 579–581. https://doi.org/10.1177/0890334420949218
Grimm, M. (2021). Universal Quantum Computing Using Electronuclear Wavefunctions of Rare-Earth Ions. PRX Quantum, 2(1). https://doi.org/10.1103/PRXQuantum.2.010312
Grimsmo, A. L. (2021). Quantum Error Correction with the Gottesman-Kitaev-Preskill Code. PRX Quantum, 2(2). https://doi.org/10.1103/PRXQuantum.2.020101
Han, J., Xu, K., Yan, Q., Sui, W., Zhang, H., Wang, S., Zhang, Z., Wei, Z., & Han, F. (2022). Qualitative and quantitative evaluation of Flos Puerariae by using chemical fingerprint in combination with chemometrics method. Journal of Pharmaceutical Analysis, 12(3), 489–499. https://doi.org/10.1016/j.jpha.2021.09.003
Hastrup, J. (2022). All-optical cat-code quantum error correction. Physical Review Research, 4(4). https://doi.org/10.1103/PhysRevResearch.4.043065
Ji, H., Qin, W., Yuan, Z., & Meng, F. (2021). Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas sensor with dynamic measurement and PCA weak separation. Sensors and Actuators B: Chemical, 348, 130698. https://doi.org/10.1016/j.snb.2021.130698
Jiulin, S., Quntao, Z., Xiaojin, G., & Jisheng, X. (2021). Quantitative Evaluation of Top Coal Caving Methods at the Working Face of Extra?Thick Coal Seams Based on the Random Medium Theory. Advances in Civil Engineering, 2021(1), 5528067. https://doi.org/10.1155/2021/5528067
Jünger, M. (2021). Quantum Annealing versus Digital Computing: An Experimental Comparison. ACM Journal of Experimental Algorithmics, 26(Query date: 2024-11-29 22:06:54). https://doi.org/10.1145/3459606
Kavokin, A. (2022). Polariton condensates for classical and quantum computing. Nature Reviews Physics, 4(7), 435–451. https://doi.org/10.1038/s42254-022-00447-1
Khalifa, O. O. (2021). Digital System Design for Quantum Error Correction Codes. Contrast Media and Molecular Imaging, 2021(Query date: 2024-11-29 22:20:32). https://doi.org/10.1155/2021/1101911
Kubica, A. (2022). Single-shot quantum error correction with the three-dimensional subsystem toric code. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-33923-4
Lanham, S. A. (2022). Generalized Noncoherent Space-Time Block Codes from Quantum Error Correction. Proceedings - IEEE Military Communications Conference MILCOM, 2022(Query date: 2024-11-29 22:20:32), 318–323. https://doi.org/10.1109/MILCOM55135.2022.10017902
Liao, P. (2022). Topological graph states and quantum error-correction codes. Physical Review A, 105(4). https://doi.org/10.1103/PhysRevA.105.042418
Mahendran, M., Lizotte, D., & Bauer, G. R. (2022). Quantitative methods for descriptive intersectional analysis with binary health outcomes. SSM - Population Health, 17, 101032. https://doi.org/10.1016/j.ssmph.2022.101032
Micheletti, C. (2021). Polymer Physics by Quantum Computing. Physical Review Letters, 127(8). https://doi.org/10.1103/PhysRevLett.127.080501
Nadkarni, P. J. (2021). Quantum error correction architecture for qudit stabilizer codes. Physical Review A, 103(4). https://doi.org/10.1103/PhysRevA.103.042420
Nishio, S. (2022a). Reducing the resources needed to implement quantum error correction codes using quantum multiplexing. 2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022 - Proceedings, Query date: 2024-11-29 22:20:32. https://doi.org/10.1109/CLEO-PR62338.2022.10432302
Nishio, S. (2022b). Reducing the resources needed to implement quantum error correction codes using quantum multiplexing. Optics InfoBase Conference Papers, Query date: 2024-11-29 22:20:32. https://doi.org/10.1364/CLEOPR.2022.CFA7H_03
Overwater, R. W. J. (2022). Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs. IEEE Transactions on Quantum Engineering, 3(Query date: 2024-11-29 22:20:32). https://doi.org/10.1109/TQE.2022.3174017
?ahinkaya, S. (2022). Maximal entanglement-assisted quantum error correction codes from the skew group ring F4? ?G by a heuristic search scheme. Quantum Information Processing, 21(4). https://doi.org/10.1007/s11128-022-03500-1
Schmidt, F. (2022). Quantum error correction with higher Gottesman-Kitaev-Preskill codes: Minimal measurements and linear optics. Physical Review A, 105(4). https://doi.org/10.1103/PhysRevA.105.042427
Schotte, A. (2022). Quantum Error Correction Thresholds for the Universal Fibonacci Turaev-Viro Code. Physical Review X, 12(2). https://doi.org/10.1103/PhysRevX.12.021012
Singh, S. (2021). Universal quantum computing using single-particle discrete-time quantum walk. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-91033-5
Stetcu, I. (2022). Variational approaches to constructing the many-body nuclear ground state for quantum computing. Physical Review C, 105(6). https://doi.org/10.1103/PhysRevC.105.064308
Suau, A. (2021). Practical Quantum Computing. ACM Transactions on Quantum Computing, 2(1). https://doi.org/10.1145/3430030
Wang, H. W. (2022a). Determination of quantum toric error correction code threshold using convolutional neural network decoders. Chinese Physics B, 31(1). https://doi.org/10.1088/1674-1056/ac11e3
Wang, H. W. (2022b). Determining quantum topological semion code decoder performance and error correction effectiveness with reinforcement learning. Frontiers in Physics, 10(Query date: 2024-11-29 22:20:32). https://doi.org/10.3389/fphy.2022.981225
Wille, R. (2021). Visualizing Decision Diagrams for Quantum Computing (Special Session Summary). Proceedings -Design, Automation and Test in Europe, DATE, 2021(Query date: 2024-11-29 22:06:54), 768–773. https://doi.org/10.23919/DATE51398.2021.9474236
Yan, D. D. (2022). Low-loss belief propagation decoder with Tanner graph in quantum error-correction codes. Chinese Physics B, 31(1). https://doi.org/10.1088/1674-1056/ac11cf
Yang, C. H. H. (2022). WHEN BERT MEETS QUANTUM TEMPORAL CONVOLUTION LEARNING FOR TEXT CLASSIFICATION IN HETEROGENEOUS COMPUTING. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022(Query date: 2024-11-29 22:06:54), 8602–8606. https://doi.org/10.1109/ICASSP43922.2022.9746412
Zhang, J. (2021). Quantum error correction with the color-Gottesman-Kitaev-Preskill code. Physical Review A, 104(6). https://doi.org/10.1103/PhysRevA.104.062434
Zinner, M. (2022). Toward the institutionalization of quantum computing in pharmaceutical research. Drug Discovery Today, 27(2), 378–383. https://doi.org/10.1016/j.drudis.2021.10.006
Authors
Copyright (c) 2024 Lusiana Rahmadani Putri, Shazia Akhtar, Zara Ali
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
