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It's been a while that quantum computing is looked as the next step in computational science. I somewhat always tought we were decade aways from it's happening but it appears I was wrong: ibm-quantum-computing-cloud (well, I am still not sure this is a large scale quantum computer or an efficient quantum computer replication; edit: changed the link; it is a simulator)

Quantum mechanics and quantitative finance were already linked by some powerfull tools (ex. Random matrix theory (RMT) in finance) or shady reasonments (Quantum Mechanics and Economics... What) but I did not really considered quantum mechanics as the next step of QF. For a long time papers I could find on the topic were either quantum mechanics oriented (see Path_integral_formulation and links) or 'poor' in term of quantitative finance (see Quantum_finance and links).

Now, I can find more interesting papers (currently at work, will post the relevant paper later) and I am somewhat convinced that quantum computing could be a huge step ahead in both optimization and simulation and so in quantitative finance.

I am now looking for some good sources (keep in mind the aim is 3, I don't necessarly need to go trough all quantum mechanics again) on

  1. quantum mechanics
  2. quantum computing
  3. quantum computing applied to quantitative finance
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    $\begingroup$ I don't see anything useful coming out of QC in next 10 years. There's a lot of hype, but every time I look at their stuff closer there's nothing of utility. $\endgroup$
    – Aksakal almost surely binary
    Commented Apr 30, 2017 at 14:22

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Try Quantum for Quants, which has contributions from people working actively in quantum computing, and some small scale examples solved on the D-Wave Systems Quantum Annealer.

The picture below is from an article on Finding Arbitrage Opportunities using a Quantum Annealer (the link is on the Q4Q main page). The annealer can solve certain graph theory problems very quickly, including those with multiple constraints on traversing the edges, so that calculations with multi-legged instruments are easier to manage.

enter image description here

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For #1 and #2 I really enjoyed this Edx course from UC Berkeley:

Quantum Mechanics and Quantum Computation

And for #3 you seem to have the sources already :)

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See here list of articles and sources I have been interested in. They concern Monte Carlo method, derivative pricing and TSP.

And here is a link to on-line course on basics of a quantum computing. I took the course and I could only recommend it.

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I have found one application of quantum computing in financial risk management: Financial Risk Management on a Neutral Atom Quantum Processor

It was run on a real machine and seems it will outperform standard approach by 2026. It is not specificly a quant finance algo but mostly a general purpose ML algo. Still, it seems interesting in the context of the question.

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There seems to be a general advancement on the use of quantum computing for ML, that then can be applied in finance. See: Financial Risk Management on a Neutral Atom Quantum Processor. While it is not strictly quantitative finance, it can impact all ML applications in the financial domain.

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