Related papers: Machine learning-enhanced optical tweezers for def…
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the…
Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved…
Learned denoisers play a fundamental role in various signal generation (e.g., diffusion models) and reconstruction (e.g., compressed sensing) architectures, whose success derives from their ability to leverage low-dimensional structure in…
Squeezing is a resource that enables precision enhancements in quantum metrology and can be used as a basis for the generation of entanglement by linear optics. While strong squeezing is challenging to generate in optical fields, here we…
Diffusion Probabilistic Models (DPMs) have achieved great success in image generation but suffer from high inference latency due to their iterative denoising nature. Motivated by the evolving feature dynamics across the denoising…
We study the optimal focusing of two-level atoms with a near resonant standing wave light, using both classical and quantum treatments of the problem. Operation of the focusing setup is considered as a nonlinear spatial squeezing of atoms…
We present a novel experimental setup in which magnetic and optical tweezers are combined for torque and force transduction onto single filamentous molecules in a transverse configuration to allow simultaneous mechanical measurement and…
Optical tweezers have become essential tools to manipulate atoms or molecules at a single particle level. However, using standard diffracted-limited optical systems, the transverse size of the trap is lower bounded by the optical…
New computational strategies, such as molecular docking, are emerging to speed up the drug discovery process. This method predicts the activity of molecules at the binding site of proteins, helping to select the ones that exhibit desirable…
Quantum processors based on neutral atoms trapped in arrays of optical tweezers have appealing properties, including relatively easy qubit number scaling and the ability to engineer arbitrary gate connectivity with atom movement. However,…
We propose a scheme for quantum logic with neutral atoms stored in an array of holographic dipole traps where the positions of the atoms can be rearranged by using holographic optical tweezers. In particular, this allows for the transport…
Optical tweezers are powerful tools based on focused laser beams. They are able to trap, manipulate and investigate a wide range of microscopic and nanoscopic particles in different media, such as liquids, air, and vacuum. Key applications…
Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-tuning is required for various down-stream tasks.…
High degree of adjustability enables the holographic tweezer array a versatile platform for creating an arbitrary geometrical atomic array. In holographic tweezer array experiments, an optical tweezer generated by a spatial light modulator…
The precise description of quantum nuclear fluctuations in atomistic modelling is possible by employing path integral techniques, which involve a considerable computational overhead due to the need of simulating multiple replicas of the…
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial…
Many important physical processes have dynamics that are too complex to completely model analytically. Optimisation of such processes often relies on intuition, trial-and-error, or the construction of empirical models. Machine learning…
High-order quantum nonlinearity is an important prerequisite for the advanced quantum technology leading to universal quantum processing with large information capacity of continuous variables. Levitated optomechanics, a field where motion…
Acousto-optic deflectors (AOD) enable spatiotemporal control of laser beams through diffraction at an ultrasonic grating that is controllable by radio-frequency (rf) waveforms. These devices are a widely used tool for high-bandwidth…
Optimal transport (OT) is a popular tool in machine learning to compare probability measures geometrically, but it comes with substantial computational burden. Linear programming algorithms for computing OT distances scale cubically in the…