The Goldilocks principle of learning unitaries by interlacing fixed operators with programmable phase shifters on a photonic chip.

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Programmable photonics provides a flexible platform that can be reconfigured to perform multiple tasks, thereby holding great promise for revolutionizing future optical networks and quantum computing systems. Over the past decade, there has been constant progress in developing several different architectures for realizing programmable photonic circuits that allow for realizing arbitrary discrete unitary operations with light.

Auto-calibrating universal programmable photonic circuits: hardware error-correction and defect resilience.

It is recently shown that discrete N × N linear unitary operators can be represented by interlacing N + 1 phase shift layers with a fixed intervening operator such as discrete fractional Fourier transform (DFrFT). Here, we show that introducing perturbations to the intervening operations does not compromise the universality of this architecture. Furthermore, we show that this architecture is resilient to defects in the phase shifters as long as no more than one faulty phase shifter is present in each layer.

Inverse design of omnidirectional coherent absorbers for optical power beaming applications.

An efficient photovoltaic power converter is a critical element in laser power beaming systems for maximizing the end-to-end power transfer efficiency while minimizing beam reflections from the receiver for safety considerations. We designed a multilayer absorber that can efficiently trap monochromatic light from broad incident angles. The proposed design is built on the concept of a one-way coherent absorber with inverse-designed aperiodic multilayer front- and back-reflectors that enable maximal optical absorption in a thin-film photovoltaic material for broad angles.

Perivascular spaces as a marker of psychological trauma in depression: A 7-Tesla MRI study.

Introduction: Emerging evidence in depression suggests that blood-brain barrier (BBB) breakdown and elevated inflammatory cytokines in states of persistent stress or trauma may contribute to the development of symptoms. Signal-to-noise ratio afforded by ultra-high field MRI may aid in the detection of maladaptations of the glymphatic system related to BBB integrity that may not be visualized at lower field strengths.

Methods: We investigated the link between glymphatic neuroanatomy via perivascular spaces (PVS) and trauma experience in patients with major depressive disorder (MDD) and in healthy controls using 7-Tesla MRI and a semi-automated segmentation algorithm.

Semi-automated Segmentation and Quantification of Perivascular Spaces at 7 Tesla in COVID-19.

While COVID-19 is primarily considered a respiratory disease, it has been shown to affect the central nervous system. Mounting evidence shows that COVID-19 is associated with neurological complications as well as effects thought to be related to neuroinflammatory processes. Due to the novelty of COVID-19, there is a need to better understand the possible long-term effects it may have on patients, particularly linkage to neuroinflammatory processes.

Hippocampal subfield-specific connectivity findings in major depressive disorder: A 7 Tesla diffusion MRI study.

Objective: Diffusion magnetic resonance imaging (dMRI) enables non-invasive characterization of white matter (WM) structures in vivo. Prior studies suggest that certain WM tracts may be affected in major depressive disorder (MDD), however, hippocampal subfield-specific dMRI measures have not yet been explored in MDD. We use 7 Tesla dMRI to investigate differences in hippocampal subfield connectivity of MDD patients.