Related papers: Machine learning-enhanced optical tweezers for def…
Quantum computing in atom tweezers requires high-fidelity implementations of quantum operations. Here, we demonstrate the optimal implementation of the transition $|0\rangle \rightarrow |1\rangle$ of two levels, serving as a qubit, of an…
Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent…
Retrieving the phase of a complex-valued field from the measurements of its amplitude is a crucial problem with a wide range of applications in microscopy and ultracold atomic physics. In particular, obtaining an accurate and efficient…
In the classical context, it is well known that, sometimes, if the search does not find its target, it is better to start the process anew again, known as resetting. The quantum counterpart of resetting also indicates speeding up the…
Optical tweezers, which are powerful tools for trapping and manipulating particles, have been widely used in many areas. However, their potential wells are typically symmetrical, which limit their capability of optical trapping and…
Emulsions are ubiquitous in everyday life and find applications in various industries. Optical tweezers (OTs) have emerged as the preferred method for studying emulsion dynamics. In this review, we first introduce the theory of optical…
We recently demonstrated that strings of trapped atoms inside a standing wave optical dipole trap can be rearranged using optical tweezers [Y. Miroshnychenko et al., Nature, in press (2006)]. This technique allows us to actively set the…
Single-shot images are the standard readout of experiments with ultracold atoms -- the tarnished looking glass into their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here, we…
Quantum computing promises to solve previously intractable problems, with neutral atoms emerging as a promising technology. Zoned neutral atom architectures allow for immense parallelism and higher coherence times by shielding idling atoms…
Quantum computing is an advancing area of research in which computer hardware and algorithms are developed to take advantage of quantum mechanical phenomena. In recent studies, quantum algorithms have shown promise in solving linear systems…
Optical tweezers is a very well-established technique that has developed into a standard tool for trapping and manipulating micron and submicron particles with great success in the last decades. Although the nature of light enforces…
Quantum algorithms have the potential to provide exponential speedups over some of the best known classical algorithms. These speedups may enable quantum devices to solve currently intractable problems such as those in the fields of…
The development of self-propelled particles at the micro- and the nanoscale has sparked a huge potential for future applications in active matter physics, microsurgery, and targeted drug delivery. However, while the latter applications…
Reduced order models are computationally inexpensive approximations that capture the important dynamical characteristics of large, high-fidelity computer models of physical systems. This paper applies machine learning techniques to improve…
This paper summarizes our recent progress towards using single rubidium atoms trapped in an optical tweezer to encode quantum information. We demonstrate single qubit rotations on this system and measure the coherence of the qubit. We move…
Optical processors, built with "optical neurons", can efficiently perform high-dimensional linear operations at the speed of light. Thus they are a promising avenue to accelerate large-scale linear computations. With the current advances in…
Optical tweezers have become essential tools for dynamically manipulating objects, ranging from microspheres or biological molecules to neutral atoms. In this study, we demonstrate the creation of tweezer arrays using a generative neural…
Instability and slowness are two main problems in deep reinforcement learning. Even if proximal policy optimization (PPO) is the state of the art, it still suffers from these two problems. We introduce an improved algorithm based on…
There is a trend in research towards more automation using smart systems powered by artificial intelligence. While experiments are often challenging to automate, they can greatly benefit from automation by reducing labor and increasing…
The task of atom rearrangement has emerged in the last decade as a fundamental building block for the development of neutral atom-based quantum processors. However, despite many recent efforts to develop algorithms with favorable asymptotic…