### Description

There will be food and drinks during the poster session.

We study the effect of adding intra-layer connections in restricted Boltzmann machines (RBM), in the hidden layer, in the visible layer, or in both layers at the same time. The improvement obtained with these new connections is evaluated with the negative log-likelihood in the MNIST dataset. We have also implemented different ways to calculate the connection updates, some more precise (and...

Finding the closest separable state to a given target state is a notoriously difficult task, even more difficult than deciding whether a state is entangled or separable. To tackle this task, we parametrize separable states with a neural network and train it to minimize the distance to a given target state, with respect to a differentiable distance, such as the trace distance or Hilbert-Schmidt...

We show that a Support Vector Machine with a quantum kernel provides an accurate prediction of the phase transition in quantum many-body models, even when trained far from the critical point.

The surging popularity of machine learning techniques has prompted their application to the study of physical properties, in particular to the detection of phase transitions. Recently, SVMs have been...

In supervised Machine Learning (ML) or Deep Learning (DL) projects, a model is trained, validated and tested by selecting the optimal preprocessing parameters, hyperparameters, and model architecture. The model’s performance is then optimized based on the preferred performance metric, such as accuracy or F1-score. In most cases, the number and distribution of the input data is kept fixed....

Quantum logic gates are the building blocks of quantum circuits and algorithms, where the generation of entanglement is essential to perform quantum computations. The amount of entanglement that a unitary quantum gate can produce from product states can be quantified by the so-called entangling power, which is a function of the gate’s unitary or Choi matrix representation. In this work, I...

Experiments with ultra-short laser pulses applied to a single atom invoke highly non-linear phenomena. Thus, they are strongly sensitive to the laser pulse parameters such as intensity, carrier envelope phase (CEP), frequency, polarization, and the number of cycles. Several techniques of retrieving pulse parameters have been developed, with state-of-the-art precision and accuracy achieved...

The state space of a quantum-mechanical system grows exponentially in the number of its classical degrees of freedom. Thus, efficient approximations are crucial for extracting physical information from this vast space. In the variational approach, computations are performed on trial states determined by a tractable number of parameters. Neural quantum states (NQS) provide a large family of...

In this paper, we present a model for detecting GaN pyramids in SEM images which relies on the strong use of data augmentation, due to the complexity of microscopic structures. A procedure has been developed to generate synthetic images for training the algorithm owing to this fact real images are hard to be prepared and labeled. In the next stage, YOLO algorithm has been employed for the...

Variational methods aim to approximate the quantum states of interest efficiently. Recently, artificial neural networks are being used as the variational ansatz to represent the wave function. These variational states are known as neural network quantum states (NQSs). The success of these NQSs in finding the ground states of spin systems has motivated researchers to explore their capabilities...

Data in machine learning scenarios is typically scattered over a large amount of files. This comes with a number of undesired side effects. First, operating systems are not designed for storing thousands of files in a flat file system. As a result, a simple scan of a directory does not terminate anymore in the worst case. Implicitly called operations like user name resolution and sorting...

Recently proposed spintronic devices use magnetic skyrmions as bits of information. The reliable detection of those chiral magnetic objects is an indispensable requirement. Yet, the high mobility of magnetic skyrmions leads to their stochastic motion at finite temperatures, which hinders the precise measurement of the topological numbers.

Here, we demonstrate the successful training of...

X-ray free-electron lasers (XFELs) provide a powerful tool to probe atomic and molecular dynamics with both exceptional temporal and spatial resolution. For a quantitative comparison between experimental results and their simulated theoretical counterpart, however, a precise characterisation of the X-ray pulse profile is essential. Generally, the pulse profile provides a non-uniform photon...