A busy January

There's been a lot going on here...

Machine Learning & Physics

Unsupervised learning of quantum many-body scars using intrinsic dimension - Now available on arXiv! We applied manifold learning techniques to identify scar states in the PXP model The take-home message: manifold learning techniques are a powerful alternative to more popular deep learning methods, especially in physics problems where you might not have access to enough training data for deep learning to work well.

Identifying topology of leaky photonic lattices with machine learning - Just published in Nanophotonics! We apply various machine learning methods to distinguish different topological phases in a photonic lattice, assuming one only has access to intensity measurements. This can serve as an alternative to full state tomography or phase retrieval methods, but one needs to be careful when training the models on ideal / pristine systems and then applying them to disordered systems. The journal also published a press release on WeChat!

Quantum Computing

Computing electronic correlation energies using linear depth quantum circuits - Finally published in Quantum Science & Technology, after more than a year and a half working through the peer review system. We use perturbation theory to determine electronic correlation energies in small molecular systems (hydrogen, lithium hydride, etc.) using a large set of shallow circuits, giving an alternative to existing methods which require deeper circuits infeasible for current quantum processors. We also tested the algorithm on cloud quantum processors, observing the detrimental impacts of noise. It would be interesting to run this again now to see how much (or how little) the performance from the different cloud providers has improved!

Landscape approximation of low-energy solutions to binary optimization problems - Published in Physical Review A. We present a method to obtain approximate solutions to binary optimization problems using the localization landscape, a function which is able to place bounds on the regions of Anderson localized eigenstates in disordered media without solving the underlying eigenvalue problem. We lay out the conditions required for these bounds to hold, outline how a quadratic unconstrained binary optimization problem can be transformed to fit these conditions, and provide details on how the quantum state representing the landscape function can be produced and sampled using techniques developed for near-term quantum devices.

 

On a related note, I was interested to see this month a new arXiv preprint in which the localization landscape was used to engineer multifractal resonances in SiN membranes!

Photonic Flatband Resonances

Photonic Flatband Resonances in Multiple Light Scattering - Published in Physical Review Letters. We reveal that flatbands can emerge as collective resonances in fine-tuned arrays of Mie-resonant nanoparticles, leading to giant values of the Purcell factor for dipolar emitters. The article was also highlighted with a Synopsis in Physics Magazine!