Teaching Assistant (TA): 高偉翔
Time: 週二 14:00~16:50
Place: 週二 工五館 E6-A210
EEClass: (CE5082)
Goal: Leading students to understand the basic principles of quantum computing and the developments and the applications of the latest quantum computing technologies. (帶領學生了解量子計算基本原理及最新量子計算技術之發展與應用)
- midterm project (programming) (30%)
- oral report in class (25%)
- term project (programming) (30%)
- homework, reports and in-class participation (15%)
- 江振瑞, 輕鬆學量子程式設計 -- 從量子位元到量子演算法, 碁峰資訊, ISBN: 9786263242715, 2022/08.
- 張元翔, 量子電腦與量子計算, 碁峰資訊, 2020.
- 陳建宏(譯), 量子計算實戰, 碁峰資訊, 2020.
- 莊永裕(譯), 圖解量子電腦入門, 臉譜, 2020.
- 林志鴻, 張仁瑀, 徐育兆, 林橋毅, 劉子睿, 林侑恆, 量子電腦應用與世界級競賽實務, 2021.
- Jack D. Hidary, Quantum Computing: An Applied Approach (2nd Ed), 2021.
- Chris Bernhardt, Quantum Computing for Everyone, 2020.
- Nihal Mehta, Quantum Computing -- Program Next-Gen Computers for Hard, Real-World Applications, 2020.
- Michael A. Nielsen, and Isaac L. Chuang, Quantum Computation
and Quantum Information, 2002.
- (2026/09/08) Week 1. Introduction to Quantum Computing -- From quantum bit to quantum algorithm (qc-talk.pptx)
- (2026/09/15) Week 2.
Introduction to Quantum Annealing (quantum-annealing.pptx)(QUBO-Example.ipynb)<==建議此
檔案應複製到Google雲端硬碟以Colab方式執行;(QUBO-Example.pdf)
Homework 1. Write a PyQUBO program to solve the subset sum problem announced by TA. Deadline: 9/21 23:59. - (2026/09/22) Week 3.
Gate-based quantum programming for the first time
(Introduction to IBM Q quantum computer and Google Sycamore)
(QBookCh1.zip) Homework2:
Textbook Exercises (Select one from 1.1-1.3)(and 1.4)(Annealing to solve the VCP: Annealing_example_with_constraints.ipynb)(deadline: by 9/28 23:59)
- (2026/09/27) Week 4. Using
IBM Qiskit 2.x in Google Colab (Qiskit2_Bell_State.ipynb)(Qiskit2_Colab_Bell_State_q2q1q0_Beamer_English.pdf)(Qiskit2_Colab_Bell_State_q2q1q0_Beamer_Traditional_Chinese.pdf)
(QBookCh2.zip)(Dirac_Bloch_H_CX_Bell_GHZ_q2q1q0_Beamer_corrected.pdf)(QUBO_WSMs_Beamer.pdf)(QUBO_Decomposition_Beamer.pdf)
Homework3: Textbook Exercises (Select two from 2.1-2.5)
(Annealing to solve the 0/1 Knapsack Problem: Annealing_example_with_constraints.ipynb)(deadline: by10/05 23:59) - (2026/10/06) Week 5. (QBookCh3.zip)(QBookCh4.zip)(QUBO_Penalty_GPBO_Compact_Cost_20slides.pdf)
Homework4: Textbook Exercises (Select two from 3.1-3.5 and two from 4.1-4.5)(deadline: by10/05 23:59)
- (2026/10/13, 2026/10/20)
Week 6 and Week 7
Papers for oral report (20-min presentation and 5-min Q&A)
1. Drug Combination Optimization
Ramos, D., Coutinho, B., & Magano, D. (2026). Network-based prediction of drug combinations with quantum annealing. Quantum Machine Intelligence, 8, 70. https://doi.org/10.1007/s42484-026-00411-7
把 network medicine 的 Complementary Exposure 原則轉成 QUBO，以 控制選取 2–3 種藥物的 cardinality constraint，並明確採用 =10(|z|,|s|)，非常適合討論「objective scale 如何決定 penalty」。Springer2. Safe & Synergistic Drug Combination
Feedback-based quantum control for safe and synergistic drug combination design. (2026). Journal of Computational Science, 100, 102965. https://doi.org/10.1016/j.jocs.2026.102965
將有害 drug–drug interactions 當 penalty、synergistic interactions 當 reward 建成 Ising/QUBO，並以 Drugs.com 與 SYNERGxDB 驗證，是很新穎的「安全性 vs. 療效」multi-objective QUBO。ScienceDirect3. Pickup-and-Delivery with Time Windows
Curuliuc, C. Ș., & Leon, F. (2026). QUBO formulation of the pickup and delivery problem with time windows for quantum annealing. Applied Sciences, 16(4), 1690.
把 visit、flow、capacity、time window、precedence 等 constraint 分別配置 j，還提出依 violation feedback 自動調整 penalty weight 的機制。MDPI4. Commercial AGV Warehouse Routing
Quantum annealing-based route optimization for commercial AGV operating systems in large-scale logistics warehouses. (2025). Scientific Reports.
針對真實大型物流倉儲 AGV routing 建立 QUBO，並明確討論 penalty 太小導致 infeasibility、太大導致 coefficient scaling/noise sensitivity 的實務問題。Nature5. Drone Delivery Packing
Testing quantum and simulated annealers on the drone delivery packing problem. (2026). Quantum Machine Intelligence.
針對 drone delivery packing 建立 standard slack-variable 與較省變數的 proxy QUBO，並把 penalty-weight parameterization、embedding 與 chain strength 納入 QA/SA 實驗比較。Springer6. Chance-Constrained Unit Commitment
Ribes Marzá, D., & González Grandón, T. (2026). Quantum annealing for joint chance-constrained unit commitment. Journal of Mathematics in Industry, 16, 3.
把 stochastic/chance-constrained Unit Commitment 轉成 QUBO，並依 annealer samples 的 constraint feasibility ratio 自動更新各 i，是非常值得研究的 adaptive multi-penalty 方法。Springer7. Hybrid Unit Commitment
Ling, J., Zhang, Q., Geng, G., & Jiang, Q. (2025). Hybrid quantum annealing decomposition framework for unit commitment. Electric Power Systems Research, 238, 111121. https://doi.org/10.1016/j.epsr.2024.111121
以 decomposition + variable reduction 控制 Unit Commitment QUBO 規模，適合討論 energy-system constraints、penalty encoding 與目前 QA hardware size limitation。ScienceDirect8. Power-System Optimization
EVA-centric QUBO optimization for active and reactive power coordination in DLMP-driven distribution systems. (2026). Scientific Reports.
把配電系統 active/reactive power coordination 建成 multi-penalty QUBO，同時考慮 penalty sufficiency、numerical stability 與 adaptive update，是相當完整的 power-grid 應用。9. Dynamic Spectrum Allocation
Kurebayashi, Y., Yamashita, Y., & Tobe, Y. (2025). Optimization of penalty weights in quantum annealing for dynamic spectrum allocation. In 2025 Asia-Pacific Conference on Communications (APCC). https://doi.org/10.34385/proc.97.T-2-2-1
把無線通訊 Dynamic Spectrum Allocation 建成 QUBO，同時推導 penalty-weight lower bound 並以 iterative QA 減少 tuning overhead，是「精彩應用 + penalty 方法」結合得最直接的論文之一。IEICE10. Portfolio Optimization
Morapakula, S., et al. (2026). End-to-end portfolio optimization with hybrid quantum annealing. Advanced Quantum Technologies. https://doi.org/10.1002/qute.202500753
將資產 selection/budget constraint 轉成 quadratic penalty，從 QUBO coefficient scale 初始化 b 再逐步增加直到 feasibility，適合金融應用與 penalty tuning 同時報告。The Advanced Portfolio11. Squared-Hinge SVM Training
Yang, Z., Zhou, J., Yang, X., Ma, H., et al. (2026). Training squared-hinge support vector machines by an explicit QUBO–Ising construction with quantum-annealing execution. Scientific Reports. https://doi.org/10.1038/s41598-026-53864-y
把 continuous SVM training 完整編譯成 QUBO，對 constraint penalty 給出 exactness 論證及 penalty schedule {t}，而且真的在 D-Wave Advantage 執行。Nature12. Multi-Knapsack
Güney, E., Ehrenthal, J., & Hanne, T. (2025). QUBO formulations and characterization of penalty parameters for the multi-knapsack problem. IEEE Access, 13, 47086–47098.
Multi-Knapsack 本身就是經典資源配置應用，論文又進一步對 constraint penalty 做 algebraic characterization 與 sensitivity experiments。
- (2026/10/27) Week 8.
Midterm Project (No Class Today)
Using the QUBO solver to solve a large scale COP (TBA). Deadline: (2026/11/02 23:59)
- VVVVVVVVVVVVVVVVV
以下內容尚待調整 VVVVVVVVVVVVVVVVVV
- (9/16) Week 3. (QBookCh2.zip)QUBO
建模、PyQUBO程式設計與量子位元疊加態程式設計(2025-0916-PyQUBO.zip)
(Quantum_Course_Slides(Week3).zip)
Homework 2: (A) Max-Cut Prob. PyQUBO programming for the G22 dataset and
(Select two from Ex2.1-Ex2.5) Deadline: 9/22 23:59 - (9/23) Week 4. Quantum-inspired
Annealers (2025-GPUA.zip),
metaheuristic algorithms (metaheuristics.pptx)
as well as Quantum Gates, Quantum Entanglement and
Quantum Teleportation (QBookCh3.zip)(QBookCh4.zip)(qexp.pptx)
Homework 3: (A) Write a report to introduce ES, SA, DE, TCA, VNS, TS, or GSA (bonus: with an example using a program to solve an NP-hard problem). (TA will announce the problem assignment soon.) (B) Ex4.1 to Ex4.5. Deadline: 9/29 23:59
- (9/30) Week 5. Paper
Oral Reports (1/3)
(1) Introduction to Fujitsu Digital Annealer with Examples. (Fujitsu_DA_Constraints.docx)(TA: 林橋毅)
(2) de Queiroz, T. A., Iori, M., Locatelli, A., & Parizy, M. (2025, July).
Solving the Cubic Knapsack Problem using the Quantum-Inspired Digital Annealer Technology. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 890-897).
(3) Codognet, P., Diaz, D., & Abreu, S. (2022, July). Quantum and digital annealing for the quadratic assignment problem. In 2022 IEEE International Conference on Quantum Software (QSW) (pp. 1-8). IEEE.
- (10/7) Week 6. Paper
Oral Reports (2/3)
(4) Jehn-Ruey Jiang, and Chun-Wei Chu, "Classifying and Benchmarking Quantum Annealing Algorithms Based on Quadratic Unconstrained Binary Optimization for Solving NP-hard Problems," IEEE Access, vol. 11, pp. 104165-104178, 2023.
Jehn-Ruey Jiang, Yu-Chen Shu, and Qiao-Yi Lin, "Benchmarks and Recommendations for Quantum, Digital, and GPU Annealers in Combinatorial Optimization," IEEE Access, vol. 12, pp. 125014-125031, 2024.
(5) Zeng, Q. G., Cui, X. P., Liu, B., Wang, Y., Mosharev, P., & Yung, M. H. (2024). Performance of quantum annealing inspired algorithms for combinatorial optimization problems. Communications Physics, 7(1), 249.
(6) Lee, H., & Jun, K. (2025). Range dependent Hamiltonian algorithms for numerical QUBO formulation. Scientific Reports, 15(1), 8819. - (10/14) Week 7. Paper
Oral Reports (3/3)
(7) Nakano, K., Takafuji, D., Ito, Y., Yazane, T., Yano, J., Ozaki, S., ... & Mori, R. (2023, May). Diverse adaptive bulk search: a framework for solving QUBO problems on multiple GPUs. In 2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 314-325). IEEE.
(8) Andreou, A., Mavromoustakis, C. X., Markakis, E., Bourdena, A., & Mastorakis, G. (2025). Sustainable AI With Quantum-Inspired Optimization: Enabling End-to-End Automation in Cloud-Edge Computing. IEEE Access.
(9) Kaseb, Z., Moller, M., Vergara, P. P., & Palensky, P. (2024). Power flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach. Scientific Reports, 14(1), 23216.
- (10/21) Week 8.
Midterm Project (No Class Today)
Using the QUBO solver to solve a large scale 0/1 knapsack problem instance based on the slack-variable-range-search problem decomposition mechanism (ref: 2025-ECICE-QUBO-Decomposition(1008).zip). The scoring will be based on how close you solution approaches the best known solution and how well you describe your program. (TA will announce the problem instance via EE-Class soon.) (Due: 10/27 23:59) - (10/28) Week
9. Background knowledge for gate-based quantum
algorithms and Grover's algorithm and its variants (QBookCh4.zip)(QBookCh5.zip)
Homework: Ex4.1, Ex4.3~Ex4.5, Ex5.1, Ex5.3 (Deadline: 11/3 23:59)
- (11/4)
Week 10. Grover's algorithm and its variants (1/2).
(QBookCh6.zip)(2023-VTS-APWCS-0825(REV).pptx)
Paper: Jehn-Ruey Jiang, and Yu-Jie Wang, "Using a Simplified Quantum Counter to Implement Quantum Circuits Based on Grover’s Algorithm to Tackle the Exact Cover Problem," Mathematics, 13(1), 90, 2025. (SCI)(IF: 2.2); Mathematics (Q1).
Homework: Ex6.1-Ex6.4 (Deadline: 11/10 23:59)
- (11/11)
Week 11. Grover's algorithm and its
variants (2/2).
Paper: Jehn-Ruey Jiang, and Yu-Jie Wang, "Dicke State Quantum Search for Solving the Vertex Cover Problem," Mathematics, 13(18), 3005, 2025.
- (11/18)
Week 12.
Shor's algorithm and its variants.(QBookCh7.zip)
1. Hong, Y. Y., Rioflorido, C. L. P. P., & Zhang, W. (2024). Hybrid deep learning and quantum-inspired neural network for day-ahead spatiotemporal wind speed forecasting. Expert Systems with Applications, 241, 122645.
2. Hong, Y. Y., Lopez, D. J. D., & Wang, Y. Y. (2024). Solar irradiance forecasting using a hybrid quantum neural network: A comparison on gpu-based workflow development platforms. IEEE Access, 12, 145079-145094.
3. Liu, C. Y., Chen, S. Y. C., Chen, K. C., Huang, W. J., & Chang, Y. J. (2025, March). Programming variational quantum circuits with quantum-train agent. In 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC) (pp. 544-548). IEEE.
4. Liu, C. Y., Chen, S. Y. C., Chen, K. C., Huang, W. J., & Chang, Y. J. (2025). Federated quantum-train long short-term memory for gravitational wave signal. arXiv preprint arXiv:2503.16049.
5. Liu, C. Y., Chen, K. C., Chen, Y. C., Chen, S. Y. C., Huang, W. H., Huang, W. J., & Chang, Y. J. (2025). Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting. arXiv preprint arXiv:2505.09395.
6. Sie, M. F., Chang, Y. J., Lin, C. L., Chang, C. R., & Liao, S. W. (2025). Efficient Bitcoin address classification using quantum-inspired feature selection. Quantum Machine Intelligence, 7(2), 75.
7. Chang, Y. J., Wang, W. T., Liu, C. Y., Wang, Y. Y., & Chang, C. R. (2025). Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration. arXiv preprint arXiv:2504.13532.
- (11/25)
Week 13. Speeches by Prof. Ying-Yi Hong (洪穎怡) (中原大學
電機系講座教授、前副校長與研發長)
Topic 1. 當深度學習在 能源預測遇見量子計算
Topic 2. A Robust Quantum AI Model for Short-Term Wind Speed Forecasting
- (12/02)
Week 14. Speeches by Prof. Yen-Jui Chang (張晏瑞 ) (中原大學 量子資訊中心教授).
Topic: Application of Quantum Computing in Finance and Material Finding
- (12/09)
NCU Quantum Day. 9:00-17:30. (中
大量子日宣傳海報)
10:00-11:40 114年余紀忠講座-蔡兆申院士主講「走進未來：超導量子電腦如何改變世界」
14:00-16:00 成功大學 陳岳男教授 Simulating open system dynamics using cloud quantum computers
Reports:
1. 11/25日之演講至少繳交一份演講報告
2. 12/04日之演講至少繳交一份演講報告 (分上半場及下半場)
3. 12/09日之演講至少繳交二份演講報告(其中必 定包含陳岳男教授的演講報告)
4. 其他額外繳交的報告視為加分報告
報告繳交及補交期限: 2025/12/15 23:59
- (12/16) Week
16. Term Project (No Class Today)
Problem: Using Grover's algorithm to
solve the exact cover problem (ECP). You
can use the IBM Qiskit, Xanadu PennyLane
and/or NVIDIA CUDA-Q platforms to
implement the quantum circuits. More
platforms you use, more points you get.
(Due:
12/29 23:59)
The two datasets are as follows.
Dataset-1:
U = {0, 1, 2, 3, 4}
S = {A, B, C, D}
A = {0, 3}
B = {0, 1, 2}
C = {1, 2}
D = {4}
Solution is {A, C, D}, and M=1.
Dataset-2:
U= {0, 1, 2}
S = {A, B, C, D, E}
A = {2}
B = {0, 2}
C = {0}
D = {1}
E = {0, 1}
Solutions are {A, E}, {B, D}, {A, C, D}, and M=3.
