QC Workshop 16: Case Study Winner Presentations

去年年末,我们组织了FinQ第一期量子计算案例研究活动。此次活动共有来自中国、美国和欧洲等地区的23位朋友报名参加,他们中有在校学生、学术界的学者、以及工业界有经验的从业者。经过为期8周的分组研究实践活动,以及最终的成果展示演讲,我们评选出了两组做为这次活动的获奖队伍。他们分别是:

最佳实践奖 / Best Practices Award (排名不分先后):

  • Jack Song
  • 赵翔
  • 敖敖

最佳创新奖 / Best Innovation Award (排名不分先后):

  • Mao Lin
  • Ke
  • Yiwen

获奖的每一位队员都获得了由FinQ颁发的获奖证书以及亚马逊礼品卡。

为了让更多的朋友可以欣赏到他们的优秀作品,我们经过获奖队伍的协商,决定公开展示他们的获奖演讲。

演讲1:2月19日 21:00(美东)/ 2月20日 10:00 (北京)– 最佳实践奖获奖团队
演讲2:2月26日 21:00 (美东)/ 2月27日 10:00 (北京)– 最佳创新奖获奖团队

报名方式

扫码微信添加FinQ小助手加入FinQ微信群获取报名链接。

FinQ Case Study Series #1

Aircraft Loading Optimisation with QUBO

Recently, Airbus announced their winner to The “Airbus Quantum Computing Challenge” (AQCC): the team Machine Learning Reply (MLR). In the fifth challenge — “Aircraft Loading Optimisation”, they formulated the problem and its constraints into cost functions in the form of Quadratic Unconstrained Binary Optimization (QUBO) problems. These cost functions are compatible with quantum annealers, as well as other hybrid classical-quantum optimization algorithms such as Quantum Approximate Optimization Algorithm (QAOA). Then they benchmarked the model on different solvers to evaluate the performances and capabilities of current technologies. 

In our case study, we will reimplement MLR’s approach in Python. Then, we can think of ways to improve their method, such as adding additional constraints and features. 

We expect that most participants would finish their assignments within 8 weeks, with a 4-hour weekly commitment and one 1-hour meeting biweekly. We will host an orientation / kick-off on October 16, 2021 and the session will conclude on December 11, 2021.

Participants would work together in groups of 3 or less. In this session, we plan to have a maximum of 3 groups.

How to register?

Registration details will be shared in WeChat group. Please add FinQ’s official account to join our WeChat groups.

What we will learn

  1. Case study a real industry operational optimization problem; 
  2. Understand and implement QUBO and quantum annealer;
  3. Participation in quantum coding development; 
  4. Drafting professional proof-of-concept reports for quantum technology.

Prerequisites: (should take <2 hours to learn all these)

  1. Basic Python: Numpy. (Knowledge of Qiskit recommended.)
  2. Basic linear algebra: Matrix multiplication, trace, partial trace etc.
  3. Basic graph theory: Nodes, edges etc.

Starting points:

Timeline:

  • October 16, 2021: Orientation / kick-off
  • Week 1-2: Reading materials, Q&A
  • Week 3-4: Implementation & iteration
  • Week 5-6: Brainstorming & adding features
  • Week 7-8: Review & report composing
  • December 11, 2021: Final presentation