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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 管希聖 | zh_TW |
| dc.contributor.advisor | Hsi-Sheng Goan | en |
| dc.contributor.author | 林晉揚 | zh_TW |
| dc.contributor.author | Chin-Yang Lin | en |
| dc.date.accessioned | 2024-03-17T16:14:36Z | - |
| dc.date.available | 2024-03-18 | - |
| dc.date.copyright | 2024-03-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-02-19 | - |
| dc.identifier.citation | Shor, P. W. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5), 1484-1509.
Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, 212-219. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) Proximal policy optimization algorithms. arXiv:1707.06347. An, Z. & Zhou, D. (2019). Deep reinforcement learning for quantum gate control. Europhysics Letters, 126, 60002. Bukov, M., Day, A. G. R., Sels, D., Weinberg, P., Polkovnikov, A. & Mehta, P. (2018). Reinforcement Learning in Different Phases of Quantum Control. Physical Review X, 8, 031086. Daraeizadeh, S., Premaratne, S. P. & Matsuura, A. Y. (2020). Designing high- fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning.2020 IEEE International Conference on Quantum Computing and Engineering (QCE), 2020, 30-36. Lin, J. H. (2022). Simulation of Quantum Gate Control via Proximal Policy Optimization Algorithm [master''s thesis, National Taiwan University]. Airiti 71 Library. https://doi.org/10.6342/NTU202104530 Niu, M. Y., Boixo, S., Smelyanskiy, V. N. & Neven, H. (2019). Universal quantum control through deep reinforcement learning. NPJ Quantum Information, 5, 33. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K. & Hassabis, D. (2017). Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv:1712.01815. Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2018). High-dimensional continuous control using generalized advantage estimation. arXiv:1506.02438. Sarkar, S., Paruchuri, P. & Khaneja, N. (2021). Error Analysis of Rotating Wave Approximation in Control of Spins in Nuclear Magnetic Resonance Spectroscopy. 60th IEEE Conference on Decision and Control (CDC), 2021, 605-610. Schuch, N. & Siewert, J. (2003). Natural two-qubit gate for quantum computation using the XY interaction. Physical Review A, 67, 032301. Long, J., Zhao, T., Bal, M., Zhao, R., Barron, G. S., Ku, H. S., ... Pappas, D. P. (2021). A universal quantum gate set for transmon qubits with strong ZZ interactions. arXiv:2103.12305. Orlando, T. P., Mooij, J. E., Tian, L., van der Wal, C. H., Levitov, L. S., Lloyd, S., & Mazo, J. J. (1999). Superconducting persistent-current qubit. Physics Review B, 60, 15398. Porras, D. & Cirac, J. I. (2004). Effective Quantum Spin Systems with Trapped Ions. Physics Review Letters, 92, 207901. Britton, J. W., Sawyer, B. C., Keith, A., Wang, C.-C. J., Freericks, J. K., Uys, H., ...Bollinger, J. J. (2012). Engineered two-dimensional Ising interactions in a trapped-ion quantum simulator with hundreds of spins. Nature, 484, 489–492. Simon, J., Bakr, W. S., Ma, R., Tai, M. E., Preiss, P. M. & Greiner, M. (2011). Quantum simulation of antiferromagnetic spin chains in an optical lattice. Nature, 472, 307–312. Hahn, E. L. (1950). Spin echoes. Physical Review, 80, 580. Viola, L., Knill, E. & Lloyd, S. (1999). Dynamical Decoupling of Open Quantum Systems. Physical Review Letters, 82(12), 2417-2421. Huang, C. H. & Goan, H. S. (2017). Robust quantum gates for stochastic time-varying noise. Physical Review A, 95, 062325. Dyson, F. J. (1949). The radiation theories of Tomonaga, Schwinger, and Feynman. Physical Review, 75, 486. Chen, C. L., Dong, D. Y., Long, R. X., Ian R. Petersen, I. R. & Rabitz, H. A. (2014). Sampling-based learning control of inhomogeneous quantum ensembles. Physical Review A, 89, 023402. Kingma, D. P. & Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv:1412.6980. Baum, Y., Amico, M., Howell, S., Hush, M., Maggie Liuzzi, M., Mundada, P., Merkh, T., Carvalho, A. R. R. & Biercuk, M. J. (2021). Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum Computer. Physical Review X Quantum, 2, 040324. Gottesman, D. (2009). An introduction to quantum error correction and fault- tolerant quantum computation. arXiv:0904.2557. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92226 | - |
| dc.description.abstract | 量子計算在加密、金融、科學模擬等領域革新中深具潛力，然而現實世界中，量子硬體的雜訊會嚴重地妨礙實行量子演算法，因此實現抗噪量子閘是使量子計算發揮成效的重要前提。本文以創新的方法將量子控制問題整合進強化學習框架中，並使用一種稱為近似策略最佳化的強化學習演算法配合深度神經網路，建立出容錯量子計算所需的高保真、抗雜訊的量子閘。 | zh_TW |
| dc.description.abstract | Quantum computing holds immense promise to revolutionize several industries such as cryptography, finance, scientific simulations and so on. However, the real-world application of quantum algorithms is severely hindered by the presence of noise in quantum hardware. Achieving noise-robust quantum gates is an important prerequisite to harness the power of quantum computing. This thesis presents an innovative way to address the challenge by mapping the quantum gate control problem into the reinforcement learning (RL) framework. Utilizing a RL algorithm called proximal policy optimization equipped with deep neural networks, we achieve constructing high-fidelity and robust single-qubit and two-qubit quantum gates in the presence of quasi-static noise, paving the way for fault-tolerant quantum computation. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-17T16:14:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-03-17T16:14:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
摘要 ii Abstract iii Contents iv List of figures vii List of tables x Chapter 1 Introduction 1 Chapter 2 Deep reinforcement learning 4 2.1 Reinforcement learning 4 2.2 Markov decision process 5 2.3 Proximal policy optimization 5 2.4 Value function 7 2.5 Advantage function 8 2.5.1 Temporal difference error 8 2.5.2 General advantage estimation 8 2.6 Deep neural network 10 Chapter 3 Quantum computing 12 3.1 Qubit 12 3.2 Quantum gate 13 3.3 Quantum control 13 3.3.1 Hamiltonian and propagator 13 3.3.2 Rotating wave approximation 14 3.3.3 Effective control Hamiltonian 16 3.3.4 Piecewise constant control 18 3.3.5 Exponential of Pauli vector 18 3.3.6 Dynamic decoupling 19 3.4 Gate infidelity 19 3.4.1 Definition of infidelity 19 3.4.2 Dyson expansion 20 3.5 Quasistatic noise model 22 3.6 Gate infidelity estimation 23 3.6.1 Noise contribution 23 3.6.2 Control deviation 24 Chapter 4 Integration 25 4.1 Framework mapping 25 4.2 Reward design 26 4.2.1 Sampling-based method 26 4.2.2 Weighted infidelity 27 4.2.3 Hyperparameters 𝛾 and λ 28 4.3 Neural network design 28 4.3.1 Network size 28 4.3.2 Network initialization 29 4.4 Adaptive learning 30 Chapter 5 Result 31 5.1 𝑋 gate 31 5.1.1 Trivial case 32 5.1.2 Ideal and noisy cases 35 5.2 𝐻 gate 40 5.2.1 Trivial case 40 5.2.2 Ideal and noisy cases 43 5.3 CNOT gate 47 Chapter 6 Discussion 53 Chapter 7 Conclusion 59 Reference 61 | - |
| dc.language.iso | en | - |
| dc.title | 以深度強化學習實現抗噪量子閘 | zh_TW |
| dc.title | Robust quantum gates by deep reinforcement learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林俊達;任祥華 | zh_TW |
| dc.contributor.oralexamcommittee | Guin-Dar Lin;Hsiang-Hua Jen | en |
| dc.subject.keyword | 強化學習,機器學習,神經網路,近似策略最佳化,量子控制,抗噪量子閘, | zh_TW |
| dc.subject.keyword | Reinforcement learning,Machine learning,Neural networks,Proximal policy optimization,Quantum control,Robust quantum gates, | en |
| dc.relation.page | 64 | - |
| dc.identifier.doi | 10.6342/NTU202400695 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-02-19 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 物理學系 | - |
| 顯示於系所單位： | 物理學系 | |
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