64 citations · 105 across the 9 of their papers we have counts for
9 papers
Learned Diffractive Optics for Quantum-Optimal Inference
Matthew J. Filipovich, Alexander Duplinskii, A. I. Lvovsky
Quantum mechanics sets the ultimate bounds on photon-limited sensing, yet practical measurements attaining these bounds are known only in special cases. This is particularly the ca…
Diffractive neural networks for mode-sorting with flexible detection regions
Kaden Bearne, Alexander Duplinskiy, Matthew J. Filipovich +1
Mode-sorting is a procedure that decomposes a light field into a basis of transverse modes, directing each mode into a separate spatial location, allowing the constituent mode inte…
TorchOptics: An open-source Python library for differentiable Fourier optics simulations
Matthew J. Filipovich, A. I. Lvovsky
TorchOptics is an open-source Python library for differentiable Fourier optics simulations, developed using PyTorch to enable GPU-accelerated tensor computations and automatic diff…
Neural Quantum States and Peaked Molecular Wave Functions: Curse or Blessing?
Aleksei Malyshev, Markus Schmitt, A. I. Lvovsky
The field of neural quantum states has recently experienced a tremendous progress, making them a competitive tool of computational quantum many-body physics. However, their largest…
Tsang's resolution enhancement method for imaging with focused illumination
Alexander Duplinskiy, Jernej Frank, Kaden Bearne +1
A widely tested approach to overcoming the diffraction limit in microscopy without disturbing the sample relies on substituting widefield sample illumination with a structured ligh…
Autoregressive Neural Quantum States with Quantum Number Symmetries
Aleksei Malyshev, Juan Miguel Arrazola, A. I. Lvovsky
Neural quantum states have established themselves as a powerful and versatile family of ansatzes for variational Monte Carlo simulations of quantum many-body systems. Of particular…
Role of Spatial Coherence in Diffractive Optical Neural Networks
Matthew J. Filipovich, Aleksei Malyshev, A. I. Lvovsky
Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks,…
Passive superresolution imaging of incoherent objects
Jernej Frank, Alexander Duplinskiy, Kaden Bearne +1
We investigate Hermite Gaussian Imaging (HGI) -- a novel passive super-resolution technique -- for complex 2D incoherent objects in the sub-Rayleigh regime. The method consists of…
Super-resolution linear optical imaging in the far field
A. A. Pushkina, G. Maltese, J. I. Costa-Filho +2
The resolution of optical imaging devices is ultimately limited by the diffraction of light. To circumvent this limit, modern super-resolution microscopy techniques employ active i…