Observing strongly-coupled Mie polaritons using water droplets

Mie theory, the analytical solution for electromagnetic wave scattering off a spherical particle, provides a powerful approach for understanding scattering spectra in terms of different multipole resonances. While the assumption of spherical symmetry is often merely an approximation, Mie theory can nevertheless give useful insights in more realistic settings such as resonances of cylindrical high refractive index nanopillars.

One setting where spherical scatterers arise quite naturally is in liquids with high surface tension, which promotes the formation of spherical droplets. Remarkably, for the case of water droplets with radii of a few microns, the Mie resonances coincide with the infrared stretching and bending vibrational resonances of the H2O molecule! This leads to strong coupling between electromagnetic and vibrational degrees of freedom leading to the formation of polaritons, as reported in recent work published in Physical Review Letters: Self-Hybridized Vibrational-Mie Polaritons in Water Droplets.

Observing the key signature of strong coupling - Rabi splitting between upper and lower polariton resonances (corresponding to electromagnetic and vibrational oscillations being in or out of phase) - using water droplets is complicated by the non-uniform droplet sizes. Thus, the measured scattering spectrum involved not just a few resonances at specific frequencies, but a distribution of different resonance frequencies dependent on the particles' sizes.

To overcome this, the authors of the study also measured the scattering spectra of droplets of heavy water, where the vibrational modes become red-shifted due to the increased mass of the deuterium atoms. The authors observed that the absorption peaks associated with the strong coupling between vibrational and electromagnetic resonances are also red-shifted.

In addition to applications to the spectra of water droplets in the atmosphere, it will be interesting to explore similar strong coupling phenomena in other high surface tension liquids and applications to polariton chemistry, whereby strong coupling between electromagnetic and molecular degrees of freedom shows promise as a means of controlling rates of chemical reactions.

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!