There is at least one clear area of application of ML in Q quant finance, it is the LSM algorithm invented by Longstaff, Schwartz and Carriere in the late 1990s for the valuation of callable exotics in the context of Monte-Carlo simulations, and widely adopted for more recent bank-wide risk calculations like CVA.
In order to estimate the continuation value on an exercise date, or a portfolio value on an exposure date, in the context of Monte-Carlo simulations, one would normally need extremely costly nested simulations. The widely adopted LSM algorithm resolves the problem in a particularly elegant manner:
1) Simulate a training set consisting of the simulated state variables of the model on the exercise/exposure date as inputs x, and (discounted) values of future cash-flows in the corresponding scenarios as labels y.
2) Use the simulated training set to train a regression/ANN/deep ANN to estimate f(x) = E[y|x], aka the value on the exercise/exposure date of the remaining cash-flows as a function of the state variables of the model on that date in this scenario.
3) Run Monte-Carlo simulations, applying the trained function f(x) to estimate the future value of the transaction(s) on exercise/exposure dates.
Now I kind of reformulated LSM in modern ML lingo. The original algorithm recommended to find f by linear regression or polynomial regression or more generally by linear regression over basis functions of the state variables.
We are now acutely aware that deep learning, a powerful generalization of linear regression, produces more accurate results much more effectively (when applied correctly). The correct, efficient estimation of future values in Monte-Carlo simulations is a crucial problem in modern finance, because it is at the heart of most regulated risk calculations: CVA, XVA, CCR, FRTB, PRIIPS, ... Recent advances of ML and DL naturally apply to resolve difficulties with LSM, particularly with many regression variables (high dimensional x)
You will find more on LSM and its application for CVA on my SSRN paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2966155 although the paper does not discuss exactly why and how ML/DL helps resolve LSM problems in high dimension, which is an active topic of current research.
I also posted a (more gentle) presentation on similar topics (and with the same limitations) here: https://www.slideshare.net/AntoineSavine/financial-cashflow-scripting-beyond-valuation
Finally, you may want to look at my lecture notes on back-propagation, and particularly the first part, which introduces (very basic) deep learning as an extension of linear regression and shows how DL naturally resolves problems in high dimension: https://github.com/asavine/CompFinance/blob/master/Intro2AADinMachineLearningAndFinance.pdf