arXiv subjects

Eliska Greplova

Publications and source records attributed to Eliska Greplova.

31 records · Page 2Linked to original sources

arXiv2022

Automated reconstruction of bound states in bilayer graphene quantum dots

Bilayer graphene is a nanomaterial that allows for well-defined, separated quantum states to be defined by electrostatic gating and, therefore, provides an attractive platform to construct tunable quantum dots. When a magnetic field perpendicular to the graphene layers is applied, the graphene valley degeneracy is lifted, and splitting of the energy levels of the dot is observed. Given the experimental ability to engineer this energy valley splitting, bilayer graphene quantum dots have a great potential for hosting robust qubits. Although bilayer graphene quantum dots have been recently realized in experiments, it is critically important to devise robust methods that can identify the observed quantum states from accessible measurement data. Here, we develop an efficient algorithm for extracting the model parameters needed to characterize the states of a bilayer graphene quantum dot completely. We introduce a Hamiltonian-guided random search method and demonstrate robust identification of quantum states on both simulated and experimental data.

cond-mat.mes-hall↗

arXiv2022

Topological Entanglement Stabilization in Superconducting Quantum Circuits

Topological properties of quantum systems are one of the most intriguing emerging phenomena in condensed matter physics. A crucial property of topological systems is the symmetry-protected robustness towards local noise. Experiments have demonstrated topological phases of matter in various quantum systems. However, using the robustness of such modes to stabilize quantum correlations is still a highly sought-after milestone. In this work, we put forward a concept of using topological modes to stabilize fully entangled quantum states, and we demonstrate the stability of the entanglement with respect to parameter fluctuations. Specifically, we see that entanglement remains stable against parameter fluctuations in the topologically non-trivial regime, while entanglement in the trivial regime is highly susceptible. We supplement our scheme with an experimentally realistic and detailed proposal based on coupled superconducting resonators and qubits. Our proposal sets a novel approach for generating long-lived quantum modes with robustness towards disorder in the circuit parameters via a bottom-up experimental approach relying on easy-to-engineer building blocks.

quant-ph↗

arXiv2022

Introduction to Machine Learning for the Sciences

This is an introductory machine-learning course specifically developed with STEM students in mind. Our goal is to provide the interested reader with the basics to employ machine learning in their own projects and to familiarize themself with the terminology as a foundation for further reading of the relevant literature. In these lecture notes, we discuss supervised, unsupervised, and reinforcement learning. The notes start with an exposition of machine learning methods without neural networks, such as principle component analysis, t-SNE, clustering, as well as linear regression and linear classifiers. We continue with an introduction to both basic and advanced neural-network structures such as dense feed-forward and conventional neural networks, recurrent neural networks, restricted Boltzmann machines, (variational) autoencoders, generative adversarial networks. Questions of interpretability are discussed for latent-space representations and using the examples of dreaming and adversarial attacks. The final section is dedicated to reinforcement learning, where we introduce basic notions of value functions and policy learning.

physics.comp-ph↗

arXiv2021

Correlation-Enhanced Neural Networks as Interpretable Variational Quantum States

Variational methods have proven to be excellent tools to approximate ground states of complex many body Hamiltonians. Generic tools like neural networks are extremely powerful, but their parameters are not necessarily physically motivated. Thus, an efficient parametrization of the wave-function can become challenging. In this letter we introduce a neural-network based variational ansatz that retains the flexibility of these generic methods while allowing for a tunability with respect to the relevant correlations governing the physics of the system. We illustrate the success of this approach on topological, long-range correlated and frustrated models. Additionally, we introduce compatible variational optimization methods for exploration of low-lying excited states without symmetries that preserve the interpretability of the ansatz.

cond-mat.str-el↗

arXiv2021

Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics

Large-scale quantum devices provide insights beyond the reach of classical simulations. However, for a reliable and verifiable quantum simulation, the building blocks of the quantum device require exquisite benchmarking. This benchmarking of large scale dynamical quantum systems represents a major challenge due to lack of efficient tools for their simulation. Here, we present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems. We illustrate our approach using a model for a forefront quantum simulation platform: ultracold atoms in optical lattices. Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary size quasi-1D bosonic system using an accessible amount of experimental measurements. We are able to significantly increase the previously known parameter precision.

quant-ph↗

arXiv2019

Automated tuning of double quantum dots into specific charge states using neural networks

While quantum dots are at the forefront of quantum device technology, tuning multi-dot systems requires a lengthy experimental process as multiple parameters need to be accurately controlled. This process becomes increasingly time-consuming and difficult to perform manually as the devices become more complex and the number of tuning parameters grows. In this work, we present a crucial step towards automated tuning of quantum dot qubits. We introduce an algorithm driven by machine learning that uses a small number of coarse-grained measurements as its input and tunes the quantum dot system into a pre-selected charge state. We train and test our algorithm on a GaAs double quantum dot device and we consistently arrive at the desired state or its immediate neighborhood.

cond-mat.mes-hall↗

arXiv2019

Fully automated identification of 2D material samples

Thin nanomaterials are key constituents of modern quantum technologies and materials research. Identifying specimens of these materials with properties required for the development of state of the art quantum devices is usually a complex and lengthy human task. In this work we provide a neural-network driven solution that allows for accurate and efficient scanning, data-processing and sample identification of experimentally relevant two-dimensional materials. We show how to approach classification of imperfect imbalanced data sets using an iterative application of multiple noisy neural networks. We embed the trained classifier into a comprehensive solution for end-to-end automatized data processing and sample identification.

cond-mat.mes-hall↗

arXiv2019

Unsupervised identification of topological order using predictive models

Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior theoretical knowledge. While for phases characterized by a broken symmetry, the use of unsupervised methods has proven to be successful, topological phases without a local order parameter seem to be much harder to identify without supervision. Here, we use an unsupervised approach to identify topological phases and transitions out of them. We train artificial neural nets to relate configurational data or measurement outcomes to quantities like temperature or tuning parameters in the Hamiltonian. The accuracy of these predictive models can then serve as an indicator for phase transitions. We successfully illustrate this approach on both the classical Ising gauge theory as well as on the quantum ground state of a generalized toric code.

quant-ph↗

arXiv2019

Hamiltonian Learning for Quantum Error Correction

The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use-cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processing as well as for reliable quantum memories. Inferring the experimentally realized Hamiltonian through a scalable number of measurements constitutes the challenging task of Hamiltonian learning. In particular, assessing the quality of the implementation of topological codes is essential for quantum error correction. Here, we introduce a neural net based approach to this challenge. We capitalize on a family of exactly solvable models to train our algorithm and generalize to a broad class of experimentally relevant sources of errors. We discuss how our algorithm scales with system size and analyze its resilience towards various noise sources.

quant-ph↗

arXiv2017

Quantum parameter estimation with a neural network

We propose to use neural networks to estimate the rates of coherent and incoherent processes in quantum systems from continuous measurement records. In particular, we adapt an image recognition algorithm to recognize the patterns in experimental signals and link them to physical quantities. We demonstrate that the parameter estimation works unabatedly in the presence of detector imperfections which complicate or rule out Bayesian filter analyses.

quant-ph↗

arXiv2017

Conditioned spin and charge dynamics of a single electron quantum dot

In this article we describe the incoherent and coherent spin and charge dynamics of a single electron quantum dot. We use a stochastic master equation to model the state of the system, as inferred by an observer with access to only the measurement signal. Measurements obtained during an interval of time contribute, by a past quantum state analysis, to our knowledge about the system at any time $t$ within that interval. Such analysis permits precise estimation of physical parameters, and we propose and test a modification of the classical Baum-Welch parameter re-estimation method to systems driven by both coherent and incoherent processes.

quant-ph↗

arXiv2016

Quantum teleportation with continuous measurements

We propose a scheme for quantum teleportation between two qubits, coupled sequentially to a cavity field. An implementation of the scheme is analyzed with superconducting qubits and a transmission line resonator, where measurements are restricted to continuous probing of the field leaking from the resonator rather than instantaneous projective Bell state measurement. We show that the past quantum state formalism [S. Gammelmark et al, Phys. Rev. 111, 160401] can be successfully applied to estimate what would have been the most likely Bell measurement outcome conditioned on our continuous signal record. This information determines which local operation on the target qubit yields the optimal teleportation fidelity. Our results emphasize the significance of applying a detailed analysis of quantum measurements in feed-forward protocols in non-ideal leaky quantum systems.

quant-ph↗

arXiv2015

Correlation functions and conditioned quantum dynamics in photodetection theory

Correlations in photodetection signals from quantum light sources are conventionally calculated by application of the source master equation and the quantum regression theorem. In this article we show how the conditioned dynamics, associated with the quantum theory of measurements, allows calculations and offers interpretations of the behaviour of the same quantities. Our theory is illustrated for photon counting and field-amplitude measurements, and we show, in particular, how transient correlations between field-amplitude measurements and later photon counting events can be accounted for by a recently developed theory of past quantum states of a monitored quantum system.

quant-ph↗