Ab-initio simulations of multiple heavy quarks propagating in a Quark-Gluon Plasma are computationally difficult to perform due the large dimension space density matrices. This work develops machine learning algorithms overcome this difficulty by approximating exact quantum states with neural network parametrisations, specifically Neural Density Operators. As proof principle demonstration QCD-like theory, approach is applied solve Lindblad master equation 1+1d lattice Schwinger Model as an...

Mode-pairing quantum key distribution (MP-QKD) is an easy-to-implement scheme that transcends the Pirandola--Laurenza--Ottaviani--Banchi bound without using repeaters. In this paper, we present improvement of performance MP-QKD advantage distillation method. The simulation results demonstrate proposed extends transmission distance significantly with a channel loss exceeding 7.6 dB. Moreover, tolerates maximum bit error rate 8.9%, which nearly twice original MP-QKD. particular, as system...

With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text tasks. Since better memory would typically prompt generation~(we define this primal problem). The traditional approach for retrieval involves selecting that exhibits the highest similarity input. However, method is constrained by quality fixed corpus from which retrieved. In paper, exploring duality problem: also prompts we propose novel framework,...

The advantage distillation (AD) method has proven effective in improving the performance of quantum key distribution (QKD). In this paper we introduce AD into a recently proposed asynchronous measurement-device-independent (AMDI) QKD protocol, taking finite-key effects account. Simulation results show that significantly enhances AMDI QKD, e.g., extending transmission distance by 16 km with total pulse count $N=7.24\ifmmode\times\else\texttimes\fi{}{10}^{13}$, and enables previously unable to...

This paper presents a gate driver for GaN-based half-bridge structure operating in buck converter with input voltage >40 V or boost output >30 V. Two 500 pF on-chip capacitors are utilized to construct three-level drivers, providing near- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{{\text {DD}}}$ </tex-math></inline-formula> negative of the rectifier switch eliminate induced pulse on from high...

Abstract Optical computing often employs tailor-made hardware to implement specific algorithms, trading generality for improved performance in key aspects like speed and power efficiency. An important approach that is still missing its corresponding optical probabilistic computing, used e.g. solving difficult combinatorial optimization problems. In this study, we propose an experimentally viable photonic solve arbitrary Our method relies on the insight coherent Ising machines composed of...

ABSTRACT The strict human pathogen Neisseria gonorrhoeae (gonococcus [Gc]) infects an estimated 82 million individuals globally and is a World Health Organization-designated bacterial of public health importance due to escalating antimicrobial resistance. Gc vaccines have been hindered by Gc’s ability evade immune surveillance in part varying its major surface antigens like the type IV pilus. We developed quick precise method for measuring pilin antigenic variation (Av) frequency using...

The demand for electric vehicles has increased over the past few years. Wireless power transfer provides more flexibility than traditional plug-in charging technology. Charging couplers are critical components in wireless systems. thermal effect produced by magnetic coupler work will cause temperature of device to rise rapidly, affecting efficiency, power, operation reliability, and service life. This paper modeled analyzed each component's distribution characteristics behavior. Firstly,...

We introduce a quantum information theory-inspired method to improve the characterization of many-body Hamiltonians on near-term devices. design new class similarity transformations that, when applied as preprocessing step, can substantially simplify Hamiltonian for subsequent analysis hardware. By design, these be identified and efficiently using purely classical resources. In practice, allow us shorten requisite physical circuit-depths, overcoming constraints imposed by imperfect...

Over the past few years, we've witnessed an enormous interest in stock price movement prediction using AI techniques. In recent literature, auxiliary data has been used to improve accuracy, such as textual news. When predicting a particular stock, we assume that information from other stocks should also be utilized enhance performance. this paper, propose Causality-guided Multi-memory Interaction Network (CMIN), novel end-to-end deep neural network for which, first time, models...

Since diffusion models (DM) and the more recent Poisson flow generative (PFGM) are inspired by physical processes, it is reasonable to ask: Can processes offer additional new models? We show that answer yes. introduce a general family, Generative Models from Physical Processes (GenPhys), where we translate partial differential equations (PDEs) describing models. can be constructed s-generative PDEs (s for smooth). GenPhys subsume two existing (DM PFGM) even give rise families of models,...

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed energy-efficient stochastic photonic elements. Nevertheless, computing hardware can control these elements program probabilistic algorithms been limited. Here, we implement a computer consisting element - neuron (PPN). Our PPN is...

<title>Abstract</title> Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed energy-efficient stochastic photonic elements. Nevertheless, computing hardware can control these elements program probabilistic algorithms been limited. Here, we implement a computer consisting element – neuron...

Optical computing often employs tailor-made hardware to implement specific algorithms, trading generality for improved performance in key aspects like speed and power efficiency. An important approach that is still missing its corresponding optical probabilistic computing, used e.g. solving difficult combinatorial optimization problems. In this study, we propose an experimentally viable photonic solve arbitrary Our method relies on the insight coherent Ising machines composed of coupled...

Quantum-classical hybrid dynamics is crucial for accurately simulating complex systems where both quantum and classical behaviors need to be considered. However, coupling between degrees of freedom the exponential growth Hilbert space present significant challenges. Current machine learning approaches predicting such dynamics, while promising, remain unknown in their error bounds, sample complexity, generalizability. In this work, we establish a generic theoretical framework analyzing...

A generalized hydrodynamic fluctuation model is proposed to simplify the calculation of dynamic structure factor S(ω, k) non-ideal plasmas using fluctuation-dissipation theorem. In this model, kinetic and correlation effects are both included in coefficients, which considered as functions coupling strength (Γ) collision parameter (kλei), where λei electron-ion mean free path. particle-particle particle-mesh molecular dynamics simulation code also developed simulate factors, used benchmark...

Finite-volume pionless effective field theory provides an efficient framework for the extrapolation of nuclear spectra and matrix elements calculated at finite volume in lattice QCD to infinite volume, nuclei with larger atomic number. In this work, it is demonstrated how may be implemented via a set correlated Gaussian wave functions optimized using differentiable programming solution generalized eigenvalue problem. This approach shown significantly more than stochastic implementation...

Gauge Theory plays a crucial role in many areas science, including high energy physics, condensed matter physics and quantum information science. In simulations of lattice gauge theory, an important step is to construct wave function that obeys symmetry. this paper, we have developed equivariant neural network techniques for simulating continuous-variable theories the Hamiltonian formulation. We applied approach find ground state 2+1-dimensional theory with U(1) group using variational Monte...

Moir\'e engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties. We solve the many-body problem of interacting electrons confined to a moir\'e superlattice potential minimum (the atom) using 2D fermionic neural network. show that strong Coulomb interactions combination anisotropic lead striking ``Wigner molecule" charge density distributions observable scanning tunneling microscopy.