W. J. ZENG - F.A.Q.

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Replies to some questions that I am frequently asked.

Q: Quantum computing is getting a lot of buzz and money. Has any quantum computer so far performed a computation? That is, taken some inputs and given a meaningful (and useful) output or response?
The short answer is yes. Quantum computers have performed meaningful computations, and sometimes these computations outperform classical computers, showing an advantage. However, those cases where there are advantage are so far for relatively limited and esoteric benchmarks. I would recommend this recent overview blogpost from the Google Quantum team with some examples comparing noisy quantum processors to classical computations.

An example of this is in random circuit sampling [research paper] [overview article]. I'd hesitate to say this was a useful calcuation, though it may have application to generating certifiable random numbers. I co-wrote a short blog post about what the supremacy calculation does that covers the main idea using some short Python snippets. When asking about milestones for quantum computer performance, clear terminology is important. I wrote a short, useful taxonomy in this post. When it comes to performing a valuable computation, I've done some work on the required resources for derivative pricing [7] and there are there are other examples in chemistry [8] [9] and prime factorization [10]. Our whitepaper (esp. Figure 2) summarizes lots of the current literature around resource estimates for different applications. We're aiming to have a live version of these captured at metriq.info/progress. Note that there is much left to discover in applying quantum computers. I am optimistic that these resource requirements will continue to decrease.

Q: When will quantum computers break RSA?
Current estimates indicate that we need scaled up, error corrected quantum computers. We need numbers of qubits and error rates that are orders of magnitude better than those available today. For more details, I recommend this DEFCON talk from Craig Gidney. It discusses rough cost estimates for factoring and is accessible to general programmers.

Q: What is a good summary of the state of the art in useful quantum computing?
I'd recommend the summary in our whitepaper (esp. the resource estimates in Figure 2) and this review "Quantum computing at the quantum advantage threshold: a down-to-business review". For some more example applications you can see this survey of quantum computer applications at Los Alamos National Lab.

Q: Can quantum computers speed up optimization? There have been some recent interesting benchmarks for heuristic improvements on near term devices for certain problems. E.g. JPM running on Quantinuum and studying the LABS problem. Read more in this overview whitepaper: Quantum Optimization: Potential, Challenges, and the Path Forward

Q: What is quantum entanglement?
Check out this excellent accessible post from Chris Ferrie.

Q: Does anyone make art with quantum computers / quantum tech?
There's been more then a few surprising creative uses already. There are folks looking at generating music [1] [2] [3]. There is also a growing field of quantum gaming. James Wooten has an excellent summary history of quantum computer games (with mention of a mobile game that Jo and I made at the one of the worlds first quantum computer hackathons). There's also a board game, mobile game, and quantum chess.

Q: How can I get into quantum computing? Do I need a PhD?
There's lots of ways! Quantum technology is a new field and growing fast. Fortunately, the amount of available educational material is growing rapidly. You can find many links to educational materials here. A comprehensive book list for a self study "quantum native" course is available here. There are also great open source textbooks/codebooks for quantum programming that are a great way to learn interactively.

qubit.guide:introductory online course for quantum computing
Qiskit Textbook
Pennylane Codebook
Learn Quantum Computing with Python and Q# textbook [actual physical textbook]

I'll break down other specific suggestions based on current career stage:

High school: Check out these resources from Unitary Fund. My advice is to work on projects in addition to taking courses. That way you develop a portfolio that shows your developing capabilities. Open source software projects are great places to start since they have a low barrier to entry. See if you can close some issues on open source projects.
Undergrad [Quantum Engineering]: Look for internships/research labs where you can be a builder and not just a course taker. See above for open open source project suggestions. It is no longer the case that you have to get a PhD in order to contribute to the cutting edge in quantum tech. Particularly the engineering disciplines of quantum software engineering, control systems engineering, electrical and mech eng, don't require a PhD. Sometimes you'll get to the cutting edge faster by joining a startup or industrial lab rather than an academic one. A list of companies in the space is hosted here.
Undergrad [Research]: That said, there are some areas of quantum technology that are more research than engineering. They include developing new quantum algorithms, error-correction schemes, or qubit/gate types. In these fields, a PhD can be very helpful. A PhD teaches you how to do independent original research whose outputs are publications. In the open research areas of quantum computing, these skills are critical and a PhD is often the best way to get them. A PhD does come with high opportunity costs in time and money. If you are unsure, then taking a couple of years to do engineering in industry around quantum tech can give you a better sense of whether you really need research skills. It will also help you determine what group you'd like to work with in a PhD. Here are a few example undergraduate programs that offer research experience: [IQC Waterloo] [LANL] [UMD]
Incoming PhD: When considering where to do a PhD, candidates often consider first the school, then the department, and then the advisor. This is precisely the opposite order of importance. Your PhD supervisor does not only teach you skills, but they also act as your career guide and primary mentor for years. It is worth taking the time to determine your supervisor as an individual if you can.
Current PhD: Many companies and research labs offer internships during the summer. These are a great way to expand into new subfields and to experience different working cultures. If you are interested in working at one of Quantonation's portfolio companies or at Unitary Fund then let me know. You can reach me @wjzeng on twitter.

Q: Are there good references or overviews for learning quantum algorithms?
For a general course on quantum algorithms I'd recommend these lecture notes from Ronald de Wolf and these recent lecture notes on quantum algorithms for scientific computing by Lin Lin. For specifically noisy intermediate scale quantum computing algorithms, which are typically more heuristic and less about computational complexity, there is a recent review article.

Q: You are based in NY. Are there quantum tech organizations in the state?
Yes! Here's is a map.