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, but not yet ones that give useful advantage, i.e. ones where using classical computation is outperformed. Some examples are calculating the ground state energy of molecules [1] [2] [3], MAXCUT optimization [4], or classification tasks [5] [6]. While these do give accurate answers for non-trival problems by running on real quantum hardware, the instances sizes are still very small.

When asking about milestones for quantum computer performance, clear terminology is important. I wrote a short, useful taxonomy in this post. Recently, an example of quantum supremacy was demonstrated, which means that some computation was performed that would be intractable to perform classically and that - in the computational complexity sense - we believe it to be hard for a classical computer and easy for a quantum one. I'd hesitate to say that 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 it comes to performing a valuable computation, we recently released 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.".

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