Quantum inspired approach for denoising with application to medical imaging.

Background noise in many fields such as medical imaging poses significant challenges for accurate diagnosis, prompting the development of denoising algorithms. Traditional methodologies, however, often struggle to address the complexities of noisy environments in high dimensional imaging systems. This paper introduces a novel quantum-inspired approach for image denoising, drawing upon principles of quantum and condensed matter physics.

Quantum inspired approach for denoising with application to medical imaging.

Background noise in many fields such as medical imaging poses significant challenges for accurate diagnosis, prompting the development of denoising algorithms. Traditional methodologies, however, often struggle to address the complexities of noisy environments in high dimensional imaging systems. This paper introduces a novel quantum-inspired approach for image denoising, drawing upon principles of quantum and condensed matter physics.

Chaos-Assisted Long-Range Tunneling for Quantum Simulation.

We present an extension of the chaos-assisted tunneling mechanism to spatially periodic lattice systems. We demonstrate that driving such lattice systems in an intermediate regime of modulation maps them onto tight-binding Hamiltonians with chaos-induced long-range hoppings t_{n}∝1/n between sites at a distance n. We provide a numerical demonstration of the robustness of the results and derive an analytical prediction for the hopping term law.

Accuracy of neural networks for the simulation of chaotic dynamics: Precision of training data vs precision of the algorithm.

We explore the influence of precision of the data and the algorithm for the simulation of chaotic dynamics by neural network techniques. For this purpose, we simulate the Lorenz system with different precisions using three different neural network techniques adapted to time series, namely, reservoir computing [using Echo State Network (ESN)], long short-term memory, and temporal convolutional network, for both short- and long-time predictions, and assess their efficiency and accuracy. Our results show that the ESN network is better at predicting accurately the dynamics of the system, and that in all cases, the precision of the algorithm is more important than the precision of the training data for the accuracy of the predictions.

Chaos-assisted tunneling resonances in a synthetic Floquet superlattice.

The field of quantum simulation, which aims at using a tunable quantum system to simulate another, has been developing fast in the past years as an alternative to the all-purpose quantum computer. So far, most efforts in this domain have been directed to either fully regular or fully chaotic systems. Here, we focus on the intermediate regime, where regular orbits are surrounded by a large sea of chaotic trajectories.