State-of-the-art quantum computers cannot run arbitrarily long quantum algorithms since their decoherence time is limited. The quality of the results unavoidably decays as the execution time increases. This work introduces a method to reduce the depth requirements of a circuit to be executed in a quantum device. The method consists in splitting the circuit in several stages to be applied...

Quantum machine learning has emerged as a promising utilization of near-term quantum computation devices. However, algorithmic classes such as variational quantum algorithms have been shown to suffer from barren plateaus due to vanishing gradients in their parameters spaces. We present an approach to quantum algorithm optimization that is based on trainable Fourier coefficients of Hamiltonian...

The analysis of the absorption and emission of electromagnetic radiation is a powerful method for exploring the quantum world of atoms and molecules. The ability to use well-defined laser pulses provides an opportunity to study the underlying structure and mechanisms of these microscopic systems with a very high resolution. A large number of techniques developed up to date for the...

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols. Here, we present a novel framework for efficiently controlling...

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols. Here, we present a novel framework for efficiently controlling...

The estimation of decoherence timescales is important not only as a key performance indicator for quantum technology, but also to measure physical quantities through the change they induce in the relaxation of quantum sensors. Typically, decoherence times are estimated by fitting a signal acquired while sweeping the time delay between qubit preparation and detection on a pre-determined range....

Quantum dots must be tuned to a specific charge regime before being used for qubit operation. This calibration procedure requires measuring the stability diagram and finding the proper gate voltages to confine one electron in the dot. Currently, this operation is performed manually, which is time-consuming and therefore not desirable for a large-scale quantum system. To overcome this...

In this talk, I will present the Anomalous Diffusion (AnDi) challenge, a community driven event aimed at pushing our understanding of diffusion phenomena. Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a...

We propose a machine learning method to characterize heterogeneous diffusion processes at a single-step level. The machine learning model takes a trajectory of arbitrary length as input and outputs the prediction of a property of interest, such as the diffusion coefficient or the anomalous exponent, at every time step. This way, changes in the diffusive properties along the trajectory emerge...

Machine learning can enable and accelerate the design of new molecules and materials in multiple ways, e.g. by learning from large amounts of (simulated or experimental) data to predict molecular or materials properties faster, or even by interfacing machine learning algorithms for autonomous decision-making directly with automated high-throughput experiments. This talk will give a brief...

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...

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...

Unveiling the microscopic origins of quantum many-body phases dominated by the interplay of spin and charge degrees of freedom constitutes one of the central challenge in modern strongly correlated many-body physics. When holes hop through a background of insulating spins, they displace their positions, which in turn induces effective frustration in the magnetic background. However, the...

We develop a variational approach to simulating the dynamics of open quantum and classical many-body systems using artificial neural networks. The parameters of a compressed representation of a probability distribution are adapted dynamically according to the Lindblad master equation or Fokker Planck equation, respectively, by employing a time-dependent variational principle. We illustrate our...

How much does it cost to generate a target quantum state from another reference state?

This is a rather general question that has been discussed in quantum information for obvious reasons. In quantum computation it is desirable to obtain the result with the minimum set of gates. This number is, roughly speaking, the cost and it is called

Complexity. In this talk, I will introduce different...

Being able to efficiently represent mixed quantum states is essential in order to describe the effects of dissipation, such as those arising in Open Quantum Systems, or in order to represent the noisy outcome of a circuit executed on a present-day Quantum Computer.

The challenges in the description of such objects arise from the exponential growth of the Hilbert space and from the need to...

The application of Machine Learning techniques in many field of theoretical physics has been very successful in the last years, leading to great improvement over existing standard methods.

In this work, we demonstrate how deploying deep generative machine learning models for estimating thermodynamic observables in lattice field theory

is a promising route for addressing many drawbacks...

Quantum scrambling is the process by which quantum information is spread within the degrees of freedom of many-body quantum systems. As such, understanding what are the features of a quantum system that maximise this information spreading has become a recent topic of interest of crucial importance. Graph theory provides a natural mathematical framework to encode the interactions of a quantum...

Scanning tunneling microscopy (STM) is an important tool to image surfaces at atomic scale, that allows to acquire significant amounts of data in comparably short time. Therefore, for example to examine large ensembles of molecules in STM images can be a difficult and time-consuming task. We present a method to recognize chirality within experimentally observed self-assembled molecular...

Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values.

Modeling this experimental situation and the resulting conductance histograms is challenging for theoretical methods, as computations need...

The computational technology of highly expressive parametric neural-network-functions has allowed machine learning to make a major foray into disciplines of natural sciences. The neural network functions may be effectively “fitted” to a loss function, given in the form of a variational principle or virial theorem, to provide solutions to quantum mechanical problems. Recently, a few deep neural...

Neural networks are powerful feature extractors - but which features do they extract from their data? And how does the structure of the training data shape the representations they learn? We investigate these questions by introducing several synthetic data models, each of which accounts for a salient feature of modern data sets: low intrinsic dimension of images [1], symmetries and...

We investigate the potential of tensor network based machine learning meth- ods to scale to large image and text data sets. For that, we study how the mutual information between a subregion and its complement scales with the subsystem size $L$, similarly to how it is done in quantum many-body physics. We find that for text, the mutual information scales as a power law $L^ν$ with a close to...

The last few decades have seen significant advancements in materials research tools, allowing scientists to rapidly synthesis and characterize large numbers of samples - a major step toward high-throughput materials discovery. Autonomous research systems take the next step, placing synthesis and characterization under control of machine learning. For such systems, machine learning controls...

Fascination in topological materials originates from their remarkable response properties and exotic quasiparticles which can be utilized in quantum technologies. In particular, large-scale efforts are currently focused on realizing topological superconductors and their Majorana excitations. However, determining the topological nature of superconductors with current experimental probes is an...

Since many concepts in theoretical physics are well known to scientists in the form of equations, it is possible to identify such concepts in non-conventional applications of neural networks to physics. In this talk, we examine what is learned by artificial neural networks, especially siamese networks in various physical domains. These networks intrinsically learn physical concepts like...

Veracity (uncertainty of data quality) and variety (heterogeneity of form and meaning of data) are two of the 4V challenges of Big Data. Both are issues for the FAIRness of materials-science results, concerning in particular, the interoperability, i.e., the “I” in FAIR. I will address what may enable us to use heterogenous data for machine learning, e.g. data from different sources or...

Inverse design problems in photonics typically operate in very high dimensional parameter spaces which are notoriously difficult to navigate to find local or global optima. Even worse, from practice it is known that different devices can have comparable performance leading to multimodal device distributions. This often confuses optimization routines causing oscillations and failure to...