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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 郭斯彥 | zh_TW |
| dc.contributor.advisor | Sy-Yen Kuo | en |
| dc.contributor.author | 江育綺 | zh_TW |
| dc.contributor.author | Yu-Chi Jiang | en |
| dc.date.accessioned | 2023-09-22T17:40:16Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-09 | - |
| dc.identifier.citation | [1] B. K. Abdalhaq, A. Awad, and A. Hawash. Reversible Logic Synthesis Using Binary Decision Diagrams With Exploiting Efficient Reordering Operators. IEEE Access, 8:156001–156016, 2020.
[2] M. Y. Abubakar, L. T. Jung, N. Zakaria, A. Younes, and A.-H. Abdel-Aty. Reversible Circuit Synthesis by Genetic Programming Using Dynamic Gate Libraries. Quantum Information Processing, 16(6):1–24, 2017. [3] S. B. Akers. Binary Decision Diagrams. IEEE Transactions on Computers, 27(6):509–516, 1978. [4] J. M. Baker, C. Duckering, P. Gokhale, N. C. Brown, K. R. Brown, and F. T. Chong. Improved Quantum Circuits via Intermediate Qutrits. ACM Transactions on Quantum Computing, 1(1):1–25, 2020. [5] C. Bandyopadhyay, H. Rahaman, and R. Drechsler. A Cube Pairing Approach for Synthesis of ESOP-based Reversible Circuit. In Proceedings of the 44th IEEE International Symposium on Multiple-Valued Logic, pages 109–114, 2014. [6] C. Bandyopadhyay, D. Roy, D. K. Kole, K. Datta, and H. Rahaman. ESOP-based Synthesis of Reversible Circuit Using Improved Cube List. In Proceedings of the IEEE International Symposium on Electronic System Design, pages 26–30, 2013. [7] A. Barenco, C. H. Bennett, R. Cleve, D. P. DiVincenzo, N. Margolus, P. Shor, T. Sleator, J. A. Smolin, and H. Weinfurter. Elementary Gates for Quantum Computation. Physical Review A, 52(5):3457, 1995. [8] C. H. Bennett. Logical Reversibility of Computation. IBM Journal of Research and Development, 17(6):525–532, 1973. [9] T. G. d. Brugière, M. Baboulin, B. Valiron, S. Martiel, and C. Allouche. Reducing the Depth of Linear Reversible Quantum Circuits. IEEE Transactions on Quantum Engineering, 2:1–22, 2021. [10] C. Bandyopadhyay, H. Rahaman, and R. Drechsler. Improved Cube List Based Cube Pairing Approach for Synthesis of ESOP Based Reversible Logic. Transactions on Computational Science XXIV, 8911:129–146, 2014. [11] T.-J. Chang, N. Meade, J. E. Beasley, and Y. M. Sharaiha. Heuristics for Cardinality Constrained Portfolio Optimisation. Computers & Operations Research, 27(13):1271–1302, 2000. [12] C.-H. Chen, B.-Y. Chiang, and T.-P. Hong. An Approach for Optimizing Group Stock Portfolio Using Multi-Objective Genetic Algorithm. In International Conference on Behavioral, Economic, and Socio-Cultural Computing, pages 213–215. IEEE, 2018. [13] Y. Chen and A. Zhou. MOEA/D With an Improved Multi-Dimensional Mapping Coding Scheme for Constrained Multi-Objective Portfolio Optimization. In IEEE Congress on Evolutionary Computation, pages 1742–1749. IEEE, 2019. [14] Y. Chen, A. Zhou, R. Zhou, P. He, Y. Zhao, and L. Dong. An Evolutionary Algorithm With a New Coding Scheme for Multi-Objective Portfolio Optimization. In Asia-Pacific Conference on Simulated Evolution and Learning, pages 97–109. Springer, 2017. [15] H.-P. Chiang, Y.-H. Chou, C.-H. Chiu, S.-Y. Kuo, and Y.-M. Huang. A QuantumInspired Tabu Search Algorithm for Solving Combinatorial Optimization Problems. Soft Computing, 18(9):1771–1781, 2014. [16] M. Choey and A. S. Weigend. Nonlinear Trading Models Through Sharpe Ratio Maximization. International Journal of Neural Systems, 8(04):417–431, 1997. [17] Y.-H. Chou and S.-Y. Kuo. Improving Boolean Circuit Testing by using Quantum Search. In IEEE Conf. Nanotechnology, pages 617–620, 2008. [18] Y.-H. Chou, S.-Y. Kuo, and Y.-C. Jiang. A Novel Portfolio Optimization Model Based on Trend Ratio and Evolutionary Computation. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(4):337–350, 2019. [19] Y.-H. Chou, S.-Y. Kuo, and Y.-T. Lo. Portfolio Optimization Based on Funds Standardization and Genetic Algorithm. IEEE Access, 5:21885–21900, 2017. [20] S. Chua and A. Singh. Search-based Reversible Logic Synthesis Using MixedPolarity Gates. Design and Testing of Reversible Logic, 577:93–113, 2020. [21] D. G. Cortés, A. J. C. Jofré, and L. San Martín. Artificial Intelligence Based Method for Portfolio Selection. In Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI), pages 1–4. IEEE, 2018. [22] T. Cui, R. Bai, A. J. Parkes, F. He, R. Qu, and J. Li. A Hybrid Genetic Algorithm for a Two-Stage Stochastic Portfolio Optimization With Uncertain Asset Prices. In IEEE Congress on Evolutionary Computation, pages 2518–2525. IEEE, 2015. [23] E. Dalcumune, L. A. Brasil Kowada, A. C. Ribeiro, C. M. Herrera de Figueiredo, and F. d. L. Marquezino. A Reversible Circuit Synthesis Algorithm with Progressive Increase of Controls in Generalized Toffoli Gates. Journal of Universal Computer Science, 27(6):544–563, 2020. [24] T. J. E. Dandin, D. Krishnaveni, and K. Chandrasekhar. ”light weight cryptography and its application in resource constrained environment using reversible logic”. In V. K. Gunjan and J. M. Zurada, editors, Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, pages 473–489, Singapore, 2022. Springer Nature Singapore. [25] J. Dang, D. Edelman, R. Hochreiter, and A. Brabazon. Swarm Intelligence-Based Stochastic Programming Model for Dynamic Asset Allocation. In IEEE Congress on Evolutionary Computation, pages 1–8. IEEE, 2010. [26] K. Datta, I. Sengupta, and H. Rahaman. Particle Swarm Optimization Based Circuit Synthesis of Reversible Logic. In Proceedings of the IEEE International Symposium on Electronic System Design, pages 226–230, 2012. [27] G. Deng, T. Dulaney, C. McCann, and O. Wang. Robust Portfolio Optimization With Value-at-Risk-Adjusted Sharpe Ratios. Journal of Asset Management, 14(5):293–305, 2013. [28] D. Deutsch and R. Jozsa. Rapid solution of problems by quantum computation. Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences, 439(1907):553–558, 1992. [29] J. Donald and N. K. Jha. Reversible Logic Synthesis with Fredkin and Peres Gates. ACM Journal on Emerging Technologies in Computing Systems, 4(1):1–19, 2008. [30] R. Drechsler, A. Finder, and R. Wille. Improving ESOP-based Synthesis of Reversible Logic Using Evolutionary Algorithms. In Proceedings of the European Conference on the Applications of Evolutionary Computation, pages 151–161, 2011. [31] K. Fazel, M. A. Thornton, and J. E. Rice. ESOP-based Toffoli Gate Cascade Generation. In Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pages 206–209, 2007. [32] D. E. Goldberg. Genetic Algorithms. Pearson Education India, 2006. [33] O. Golubitsky, S. M. Falconer, and D. Maslov. Synthesis of the Optimal 4-bit Reversible Circuits. In Proceedings of the IEEE Design Automation Conference, pages 653–656, 2010. [34] O. Golubitsky and D. Maslov. A Study of Optimal 4-bit Reversible Toffoli Circuits and Their Synthesis. IEEE Transactions on Computers, 61(9):1341–1353, 2011. [35] L. K. Grover. A Fast Quantum Mechanical Algorithm for Database Search. pages 212–219, 1996. [36] P. Gupta, A. Agrawal, and N. K. Jha. An Algorithm for Synthesis of Reversible Logic Circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(11):2317–2330, 2006. [37] K.-H. Han and J.-H. Kim. Quantum-Inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE transactions on evolutionary computation, 6(6):580–593, 2002. [38] Y.-R. Hsu, Y.-Z. Chen, S.-Y. Kuo, and Y.-H. Chou. Using GNQTS to Solve Portfolio Optimization with Fund Allocation in the U.S. Market. In IEEE International Conference on Systems, Man, and Cybernetics, pages 4038–4043. IEEE, 2020. [39] C.-F. Huang, C.-H. Chang, B. R. Chang, and D.-W. Cheng. A Study of a Hybrid Evolutionary Fuzzy Model for Stock Selection. In IEEE International Conference on Fuzzy Systems, pages 210–217. IEEE, 2011. [40] C.-F. Huang, C.-H. Chang, B. R. Chang, and T.-N. Hsieh. A Genetic-Based Stock Selection Model Using Investor Sentiment Indicators. In IEEE International Conference on Granular Computing, pages 262–267. IEEE, 2011. [41] C.-F. Huang, T.-N. Hsieh, B. R. Chang, and C.-H. Chang. A Comparative Study of Stock Scoring Using Regression and Genetic-Based Linear Models. In IEEE International Conference on Granular Computing, pages 268–273. IEEE, 2011. [42] C.-F. Huang, M.-Y. Tsai, T.-N. Hsieh, L.-M. Kuo, and B. R. Chang. A Study of Hybrid Genetic-Fuzzy Models for IPO Stock Selection. In International conference on Fuzzy Theory and Its Applications, pages 357–362. IEEE, 2012. [43] K. K. Hung, C. C. Cheung, and L. Xu. New Sharpe-Ratio-Related Methods for Portfolio Selection. In Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering, pages 34–37. IEEE, 2000. [44] R. Ionicioiu, T. P. Spiller, and W. J. Munro. Generalized Toffoli Gates Using Qudit Catalysis. Physical Review A, 80(5):012312, 2009. [45] J. Jiang, X. Sun, Y. Sun, K. Wu, and Z. Xia. Structured Decomposition for Reversible Boolean Functions. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(10):2410–2421, 2020. [46] Y.-C. Jiang, X.-J. Cheam, C.-Y. Chen, S.-Y. Kuo, and Y.-H. Chou. A Novel Portfolio Optimization With Short Selling Using GNQTS and Trend Ratio. In IEEE International Conference on Systems, Man, and Cybernetics, pages 1564–1569, 2018. [47] D. Karaboga and B. Basturk. A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of global optimization, 39(3):459–471, 2007. [48] J. Kennedy and R. Eberhart. Particle Swarm Optimization. In Proceedings of ICNN’95-international conference on neural networks, volume 4, pages 1942– 1948. IEEE, 1995. [49] A. Kole, K. Datta, and I. Sengupta. A Heuristic for Linear Nearest Neighbor Realization of Quantum Circuits by SWAP Gate Insertion Using N-Gate Lookahead. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 6(1):62– 72, 2016. [50] S. Kuo, Y. Jiang, Y. Chou, S. Kuo, and S. Kung. ”quantum computer-aided design automation: Optimization algorithms and visualization interfaces”. IEEE Nanotechnology Magazine, 17(2):15–25, Apr. 2023. [51] S.-Y. Kuo and Y.-H. Chou. Entanglement-Enhanced Quantum-Inspired Tabu Search Algorithm for Function Optimization. IEEE Access, 5:13236–13252, 2017. [52] Z.-R. Lai, P.-Y. Yang, L. Fang, and X. Wu. Reweighted Price Relative Tracking System for Automatic Portfolio Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(11):4349–4361, 2020. [53] R. Landauer. Irreversibility and Heat Generation in the Computing Process. IBM Journal of Research and Development, 5(3):183–191, 1961. [54] M.-F. Leung and J. Wang. Minimax and Biobjective Portfolio Selection Based on Collaborative Neurodynamic Optimization. IEEE Transactions on Neural Networks and Learning Systems, 32(7):2825–2836, 2021. [55] M. Li, Y. Zheng, M. S. Hsiao, and C. Huang. Reversible Logic Synthesis through Ant Colony Optimization. In Proceedings of the IEEE Design, Automation & Test in Europe Conference & Exhibition, pages 307–310, 2010. [56] B.-Y. Liao, H.-W. Chen, S.-Y. Kuo, and Y.-H. Chou. Portfolio Optimization Based on Novel Risk Assessment Strategy With Genetic Algorithm. In IEEE International Conference on Systems, Man, and Cybernetics, pages 2861–2866, 2015. [57] H. Markowitz. Portfolio Selection. Journal of Finance, 7(1):77–91, 1952. [58] D. Maslov and G. W. Dueck. Reversible Cascades with Minimal Garbage. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 23(11):1497–1509, 2004. [59] D. Maslov, C. Young, D. M. Miller, and G. W. Dueck. Quantum Circuit Simplification Using Templates. In Proceedings of the Conference on Design, Automation and Test in Europe, pages 1208–1213, 2005. [60] S. N. Maslov D, Dueck G. Reversible Logic Synthesis Benchmarks page. https: //reversiblebenchmarks.github.io/, 2005. [61] O. D. Matteo, V. Gheorghiu, and M. Mosca. Fault-Tolerant Resource Estimation of Quantum Random-Access Memories. IEEE Transactions on Quantum Engineering, 1:1–13, 2020. [62] D. M. Miller and G. W. Dueck. Function Translations and Aearch-based Transformation for MVL Reversible Circuit Synthesis. Science of Computer Programming, 212:102704, 2021. [63] O. Montiel, Y. Rubio, C. Olvera, and A. Rivera. Quantum-inspired acromyrmex evolutionary algorithm. Scientific Reports, 9(1), 2019. [64] H. Nguyen and L. H. Tran. Synthesis of Reversible and Quantum Circuit Using ROCBDD and Mixed-Polarity Toffoli Gate. IEEE Access, 9:135432–135439, 2021. [65] T. T. Nguyen, L. Gordon-Brown, A. Khosravi, D. Creighton, and S. Nahavandi. Fuzzy Portfolio Allocation Models Through a New Risk Measure and Fuzzy Sharpe Ratio. IEEE Transactions on Fuzzy Systems, 23(3):656–676, 2015. [66] M. A. Nielsen and I. L. Chuang. Quantum Computation and Quantum Information. Cambridge University Press, 2019. [67] G. V. Pai. Multi-Objective Differential Evolution Based Optimization of Risk Budgeted Global Asset Allocation Portfolios. In International Symposium on Computational and Business Intelligence, pages 17–20. IEEE, 2014. [68] G. V. Pai. Multi-Objective Metaheuristics for Managing Futures Portfolio Risk. In IEEE Symposium Series on Computational Intelligence, pages 1204–1211. IEEE, 2018. [69] G. V. Pai and T. Michel. Metaheuristic Optimization of Risk Budgeted Global Asset Allocation Portfolios. In World Congress on Information and Communication Technologies, pages 154–159. IEEE, 2011. [70] G. V. Pai and T. Michel. Differential Evolution Based Optimization of Risk Budgeted Equity Market Neutral Portfolios. In IEEE Congress on Evolutionary Computation, pages 1–8. IEEE, 2012. [71] K. Podlaski. Ant Colony Optimization Implementation for Reversible Synthesis in Walsh-Hadamard Domain. In Proceedings of the International Conference on Computational Science, pages 230–243, 2020. [72] J. Preskill. Quantum Computing in the NISQ era and beyond. Quantum, 2:79, 2018. [73] R. Wille and R. Drechsler. Effect of BDD Optimization on Synthesis of Reversible and Quantum Logic. Electronic Notes in Theoretical Computer Science, 253(6):57– 70, 2010. [74] M. M. Rahman, G. W. Dueck, A. Chattopadhyay, and R. Wille. Integrated Synthesis of Linear Nearest Neighbor Ancilla-free MCT Circuits. In Proceeding of the 46th IEEE International Symposium on Multiple-Valued Logic, pages 144–149, 2016. [75] A. Raudys and Ž. Pabarškaitė. Discrete Portfolio Optimisation for Large Scale Systematic Trading Applications. In International Conference on BioMedical Engineering and Informatics, pages 1566–1570. IEEE, 2012. [76] M. Saeedi, M. Sedighi, and M. S. Zamani. A Novel Synthesis Algorithm for Reversible Circuits. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design, pages 65–68, 2007. [77] Y. Sanaee and G. W. Dueck. ESOP-based Toffoli Network Generation with Transformations. In Proceedings of the 40th IEEE International Symposium on MultipleValued Logic, pages 276–281, 2010. [78] W. F. Sharpe. The Sharpe Ratio. Journal of Portfolio Management, 21(1):49–58, 1994. [79] W. F. Sharpe. Investors and markets: portfolio choices, asset prices, and investment advice. Princeton university press, Princeton, NJ, USA, 2011. [80] V. V. Shende, A. K. Prasad, I. L. Markov, and J. P. Hayes. Synthesis of Reversible Logic Circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 22(6):710–722, 2003. [81] P. W. Shor. Algorithms for Quantum Computation: Discrete Logarithms and Factoring. In Proceedings of the 35th IEEE Annual Symposium on Foundations of Computer Science, pages 124–134, 1994. [82] H. Siljak. Reversible Computation in Wireless Communications, pages 208–221. Springer International Publishing, Cham, 2020. [83] P. Skolpadungket, K. Dahal, and N. Harnpornchai. Portfolio Optimization Using Multi-Objective Genetic Algorithms. In IEEE Congress on Evolutionary Computation, pages 516–523. IEEE, 2007. [84] V. Soam, L. Palafox, and H. Iba. Multi-Objective Portfolio Optimization and Rebalancing Using Genetic Algorithms With Local Search. In IEEE Congress on Evolutionary Computation, pages 1–7. IEEE, 2012. [85] F. A. Sortino and L. N. Price. Performance Measurement in a Downside Risk Framework. the Journal of Investing, 3(3):59–64, 1994. [86] S. Stojković, R. Stanković, C. Moraga, and M. Stanković. Reversible Circuits Synthesis from Functional Decision Diagrams by using Node Dependency Matrices. Journal of Circuits, Systems and Computers, 29(5), 2020. [87] R. Storn and K. Price. Differential Evolution–A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. Journal of global optimization, 11(4):341–359, 1997. [88] M. Szyprowski and P. Kerntopf. A Study of Optimal 4-bit Reversible Circuit Synthesis from Mixed-Polarity Toffoli Gates. In Proceedings of the 12th IEEE International Conference on Nanotechnology, pages 1–6, 2012. [89] K. Tabei and T. Yamada. On Generating Test Sets for Reversible Circuits. In Proceedings of the IEEE International Conference on Computer Engineering & Systems, pages 94–99, 2009. [90] T. Toffoli. Reversible Computing. In Proceedings of the International Colloquium on Automata, Languages, and Programming, pages 632–644, 1980. [91] I.-M. Tsai and S.-Y. Kuo. An Algorithm for Minimum Space Quantum Boolean Circuits Construction. Journal of Circuits, Systems, and Computers, 15(5):719– 738, 2006. [92] W. Zhu, Z. Li, G. Zhang, S. Pan, and W. Zhang. A Reversible Logical Circuit Synthesis Algorithm Based on Decomposition of Cycle Representations of Permutations. International Journal of Theoretical Physics, 57(8):2466–2474, 2018. [93] B. Wang, Y. Li, S. Wang, and J. Watada. A Multi-Objective Portfolio Selection Model With Fuzzy Value-at-Risk Ratio. IEEE Transactions on Fuzzy Systems, 26(6):3673–3687, 2018. [94] N. Wesselhöfft and W. K. Härdle. Risk-constrained kelly portfolios under alphastable law. Computational Economics, 55:801–826, 2020. [95] R. Wille, A. Chattopadhyay, and R. Drechsler. From Reversible Logic to Quantum Circuits: Logic Design for an Emerging Technology. In Proceedings of the International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, pages 268–274, 2016. [96] R. Wille and R. Drechsler. BDD-based Synthesis of Reversible Logic for Large Functions. In Proceedings of the 46th ACM Annual Design Automation Conference, pages 270–275, 2009. [97] R. Wille, M. Soeken, C. Otterstedt, and R. Drechsler. Improving the Mapping of Reversible Circuits to Quantum Circuits Using Multiple Target Lines. In Proceedings of the 18th Asia and South Pacific Design Automation Conference, pages 145–150, 2013. [98] M.-E. Wu, C.-J. Lee, W.-H. Chung, and D.-Y. Yang. Determination of profit quantification on kelly betting based on relative entropy. Quality Technology & Quantitative Management, 18(4):468–483, 2021. [99] M.-E. Wu, H.-H. Tsai, W.-H. Chung, and C.-M. Chen. Analysis of kelly betting on finite repeated games. Applied Mathematics and Computation, 373:125028, 2020. [100] Y. Xu. Optimal growth under model uncertainty. Available at SSRN:3677659, 2020. [101] L. Yu, L. Hu, and L. Tang. Stock Selection With a Novel Sigmoid-Based Mixed Discrete-Continuous Differential Evolution Algorithm. IEEE Transactions on Knowledge and Data Engineering, 28(7):1891–1904, 2016. [102] X. Yu and L. Xu. Adaptive Improved Portfolio Sharpe Ratio Maximization With Diversification. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, volume 4, pages 472–476. IEEE, 2000. [103] D. V. Zakablukov. Application of Permutation Group Theory in Reversible Logic Synthesis. In Proceedings of the International Conference on Reversible Computation, pages 223–238, 2016. [104] M. Zhang, S. Zhao, and X. Wang. Automatic Synthesis of Reversible Logic Circuit Based on Genetic Algorithm. In Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, pages 542–546, 2009. [105] X. Zhang. Separable reversible data hiding in encrypted image. IEEE Transactions on Information Forensics and Security, 7(2):826–832, 2012. [106] L. Zhao and D. P. Palomar. A Markowitz Portfolio Approach to Options Trading. IEEE Transactions on Signal Processing, 66(16):4223–4238, 2018. [107] A. Zulehner and R. Wille. One-Pass Design of Reversible Circuits: Combining Embedding and Synthesis for Reversible Logic. IEEE Transactions on ComputerAided Design of Integrated Circuits and Systems, 37(5):996–1008, 2018. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90161 | - |
| dc.description.abstract | 量子計算是能在現實應用中實現強大量子優勢的關鍵。目前的量子電腦還處在具中尺度雜訊的裝置階段，量子電腦的運算都還處在較低的保持特性相干時間以及較高的雜訊，使運算不穩定，無法獲得精確的結果。因此，這篇論文主要是希望能推進量子計算進展以利用量子優勢協助解決實際應用問題，並且從兩方面問題著手：(a)量子電路最小化以實現量子實際應用的功能、(b)量子啟發式最佳化以展示如何透過量子特性設計一個演算法以解決實際應用問題。在第一個問題中，這篇論文提出了以超立方體為基礎的量子布林電路合成方法，可以達到高位元量子函式並表現比其他現有的方法更好。超立方體和可逆函式有相似特性，因此將可逆函式轉換至超立方體上可以透過視覺化觀察整體電路特性，並幫助實現合成更簡短的量子布林電路。因此，新穎的兩個指標，相鄰漢明距離及整體循環距離被提出，幫助有效進行決策及產生較短電路。當實現較小的量子電路穩定量子電腦效能後，就能考慮現實應用，其中金融應用是常見的重要研究議題。然而，目前經典電腦依然具有許多比量子電腦更具優勢的特點。量子啟發式最佳化是在目前量子電腦成熟之前的過渡期期間，以實現量子計算的一種混合式方法，能處理實際應用問題並且在經典電腦上發展量子計算理論。除此之外，本研究也透過量子啟發式最佳化技術應用在具加權的投資組合最佳化問題上，使搜尋最佳化並擴展量子計算的適用性。這篇論文考慮多變市場情況並且利用量子啟發式最佳化搭配趨勢值模型找到穩定的加權投資組合，並且提出心情值以計算投資期間的投資者情緒波動。在這兩項最佳化問題中，視覺化呈現能幫助理解複雜問題及促使這篇論文提出有效解法以解決量子布林電路合成及金融應用問題的重要部分。 | zh_TW |
| dc.description.abstract | Quantum computing is the key to realizing the decisive quantum advantage in real-world applications. The current quantum computer is in a noisy intermediate-scale quantum (NISQ) device stage. The operation in a quantum computer has a low coherence time and high noise, making the result unstable to realize the precise result. This study aims to progress the development of quantum computing, addressing real-world applications through quantum advantage. This study addresses two aspects of quantum computing: (a) Quantum circuit minimization to realize the functionality of quantum applications. (b) Quantum-inspired optimization (QIO) to demonstrate how to design an algorithm by utilizing quantum property in solving applications. In the first topic, this study proposes a hypercube-based quantum Boolean circuit synthesis method that can achieve a higher-bit function and outperform other present works. The hypercube has a similar property to the reversible function. Transforming the reversible function into the hypercube can visually observe the overall circuit properties to help realize a compact quantum Boolean circuit. As a result, novel indicators, the adjacent Hamming distance (AHD) and total cycle distance (TCD), are proposed to aid in effective decision-making and generate shorter circuits. Real applications can be considered after a smaller quantum circuit stabilizes the quantum computer performance. Financial applications are a critical issue. However, classical computer still has many advantages over quantum computer currently. QIO is a hybrid method in the transitional period to realize quantum computing, address real-world applications, and develop theory in classical computers. This study utilizes the QIO technique to optimize the search and expand the applicability of quantum computing in the weighted portfolio optimization problem. This study considers the dynamic market situation and utilizes QIO to search for a stable weighted portfolio through the trend ratio model. The emotion index is proposed to evaluate the investor's fluctuating emotions in the investment period. In these two topics, visualization presentation is a critical issue in comprehending the complicated problem and urges this study to propose a practical solution to solve quantum Boolean circuit synthesis and financial application. | en |
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| dc.description.provenance | Made available in DSpace on 2023-09-22T17:40:16Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要iii
Abstract v Contents vii List of Figures xi List of Tables xix Chapter 1 Introduction 1 Chapter 2 Quantum Reversible Circuit Synthesis 5 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Notation for the reversible circuit . . . . . . . . . . . . . . . . . . . 9 2.2.2 Definition of the reversible circuit synthesis problem . . . . . . . . 10 2.2.3 Cycle Representation . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.4 Relationship between the Hypercube and the GT Gate . . . . . . . . 11 2.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.4.1 Determine the Selected Element, DSE . . . . . . . . . . . . . . . . 21 2.4.2 Determine the Exchanged Edge, DEE . . . . . . . . . . . . . . . . 24 2.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5.1 3-Bit reversible functions with a GT gate set . . . . . . . . . . . . . 32 2.5.2 High-bit reversible functions with a GT gate set . . . . . . . . . . . 33 2.5.3 Comparison with Other Gate Sets . . . . . . . . . . . . . . . . . . . 35 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Chapter 3 Quantum-inspired Optimization Solving Portfolio Optimization 41 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.1 Sharpe Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 Funds Standardization . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.2.3 Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4 Basic Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.1 Trend Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.5.2 Emotion Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.5.3 Global-Best Guided Quantum-Inspired Tabu Search with Self-Adaptive Strategy and Quantum-NOT Gate, ANGQTS . . . . . . . . . . . . 60 3.5.3.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . 63 3.5.3.2 Measurement . . . . . . . . . . . . . . . . . . . . . . 63 3.5.3.3 Normalization . . . . . . . . . . . . . . . . . . . . . . 64 3.5.3.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 64 3.5.3.5 Quantum-NOT Gate . . . . . . . . . . . . . . . . . . . 65 3.5.3.6 Self-Adaptive Update . . . . . . . . . . . . . . . . . . 67 3.5.4 Sliding Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.6 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.6.1 Comparing ANGQTS with GNQTS . . . . . . . . . . . . . . . . . 70 3.6.2 Comparing the NEWFA and EWFA . . . . . . . . . . . . . . . . . 72 3.6.3 Comparing the ANGQTS-NEWFA and DJIA . . . . . . . . . . . . 75 3.6.4 Comparing the Trend Ratio with the Sharpe Ratio . . . . . . . . . . 77 3.6.5 Testing under the global COVID-19 pandemic during 2020 –June 2021 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.6.6 Self-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Chapter 4 Contributions and Future Work 89 References 93 Appendix A — Dow Jones 30 Components 107 | - |
| dc.language.iso | en | - |
| dc.subject | 加權投資組合最佳化 | zh_TW |
| dc.subject | 量子計算 | zh_TW |
| dc.subject | 視覺化呈現 | zh_TW |
| dc.subject | 量子布林電路合成 | zh_TW |
| dc.subject | 量子啟發式最佳化 | zh_TW |
| dc.subject | weighted portfolio optimization | en |
| dc.subject | quantum computing | en |
| dc.subject | quantum-inspired optimization | en |
| dc.subject | quantum Boolean circuit synthesis | en |
| dc.subject | visualization | en |
| dc.title | 以可視化模型協助之量子計算解決最佳化問題 | zh_TW |
| dc.title | Visualization Model Assisted Quantum Computing in Solving Optimization Problems | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 周耀新;雷欽隆;林宗男;管希聖;陳俊良;陳英一 | zh_TW |
| dc.contributor.oralexamcommittee | Yao-Hsin Chou;Chin-Laung Lei;Tsungnan Lin;Hsi-Sheng Goan;Jiann-Liang Chen;Ing-Yi Chen | en |
| dc.subject.keyword | 量子計算,視覺化呈現,量子布林電路合成,加權投資組合最佳化,量子啟發式最佳化, | zh_TW |
| dc.subject.keyword | quantum computing,visualization,quantum Boolean circuit synthesis,weighted portfolio optimization,quantum-inspired optimization, | en |
| dc.relation.page | 108 | - |
| dc.identifier.doi | 10.6342/NTU202303207 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-11 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | 2028-08-06 | - |
| Appears in Collections: | 電機工程學系 | |
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