Recent years have witnessed the rapid development of various applications based on quantum information science. Among the many physical platforms, spin defect in diamond, namely nitrogen-vacancy (NV) centers, has attracted a lot of attention thanks to its good controllability and long coherence time even at room temperature. We will give a brief introduction to this platform and explore its potential in different quantum fields.
Changhao Li is currently a Ph.D. candidate at the Massachusetts Institute of Technology. He got his bachelor’s degree in physics at Xi’an Jiaotong University in 2017. He has been working on developing various quantum applications based on spin defects in diamonds, including quantum simulators and novel quantum sensors that can detect biological or chemical signals.
Basics of the nitrogen-vacancy (NV) center platform
NV center and nuclear spins as qubit systems
Typical system characterization methods, including coherence time measurement, dynamical decoupling, and charge state measurement.
How to develop various quantum sensors such as COVID19 sensor based on NV centers
Recent experimental progres
2022年8月6日（星期六） 9 PM （美国东部时间） 6 PM （美国太平洋时间）
2022年8月7日（星期日） 9 AM （中国北京时间）
Topic: FinQ Workshop 22: Spin defects in diamond for quantum applications Time: Aug 6, 2022 21:00 Eastern Time (US and Canada)
Meeting ID: 878 6322 1171 Passcode: 763465 One tap mobile +13126266799,,87863221171#,,,,*763465# US (Chicago) +16469313860,,87863221171#,,,,*763465# US
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When: Saturday 2022-08-06 ⋅ 9pm – 10pm (Eastern Time – New York)
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
Case study a real industry operational optimization problem;
Understand and implement QUBO and quantum annealer;
Participation in quantum coding development;
Drafting professional proof-of-concept reports for quantum technology.
Prerequisites: (should take <2 hours to learn all these)
Basic Python: Numpy. (Knowledge of Qiskit recommended.)
Basic linear algebra: Matrix multiplication, trace, partial trace etc.
Meeting ID: 840 818 9361 Passcode: Finq One tap mobile +16465588656,,8408189361#,,,,416271# US (New York) +13126266799,,8408189361#,,,,416271# US (Chicago)
Dial by your location +1 646 558 8656 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US (Washington DC) +1 253 215 8782 US (Tacoma) +1 346 248 7799 US (Houston) +1 669 900 9128 US (San Jose) Meeting ID: 840 818 9361 Passcode: 416271 Find your local number: https://us02web.zoom.us/u/kbjOWHSit1