Machine Learning meets Quantum Physics

Conclusions and where do we stand

Lots of experimental analysis coming, see for example Unsupervised Learning for Identifying Events in Active Target Experiments by Solli et al. as well references and examples in Report from the A.I. For Nuclear Physics Workshop by Bedaque et al..
Extension of the work of G. Carleo and M. Troyer, Science 355, Issue 6325, pp. 602-606 (2017) gives excellent results for two-electron systems as well as good agreement with standard VMC calculations from \( N=2 \) to more than \( N=100 \) electrons.
Promising results. Next step is to use trial wave function in final Green's function Monte Carlo calculations.
Minimization problem can be tricky.
Can we use ML to find out which correlations are relevant and thereby diminish the dimensionality problem in say CC or SRG theories?
Quantum Boltzmann Machines (QBMs) to devise better quantum gates for quantum computations and many more exciting topics
QBMs can be utilized to train models for data from experiments with quantum systems. They might be employed for combinatorial optimization. Classical BMs have been investigated in this context and developing and analyzing quantum algorithms for combinatorial optimization is an active area of research.
All in all, there are many possible applications which still have to be explored with many more exciting research avenues. See for example Maria Schuld et al on evaluating analytic gradients on quantum hardware.