17,384 research outputs found
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Over the past decade, machine learning techniques have revolutionized how
research is done, from designing new materials and predicting their properties
to assisting drug discovery to advancing cybersecurity. Recently, we added to
this list by showing how a machine learning algorithm (a so-called learner)
combined with an optimization routine can assist experimental efforts in the
realm of tuning semiconductor quantum dot (QD) devices. Among other
applications, semiconductor QDs are a candidate system for building quantum
computers. The present-day tuning techniques for bringing the QD devices into a
desirable configuration suitable for quantum computing that rely on heuristics
do not scale with the increasing size of the quantum dot arrays required for
even near-term quantum computing demonstrations. Establishing a reliable
protocol for tuning that does not rely on the gross-scale heuristics developed
by experimentalists is thus of great importance. To implement the machine
learning-based approach, we constructed a dataset of simulated QD device
characteristics, such as the conductance and the charge sensor response versus
the applied electrostatic gate voltages. Here, we describe the methodology for
generating the dataset, as well as its validation in training convolutional
neural networks. We show that the learner's accuracy in recognizing the state
of a device is ~96.5 % in both current- and charge-sensor-based training. We
also introduce a tool that enables other researchers to use this approach for
further research: QFlow lite - a Python-based mini-software suite that uses the
dataset to train neural networks to recognize the state of a device and
differentiate between states in experimental data. This work gives the
definitive reference for the new dataset that will help enable researchers to
use it in their experiments or to develop new machine learning approaches and
concepts.Comment: 18 pages, 6 figures, 3 table
Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques
(AI) from the research community and industry to tackle "real world" problems,
requires High Performance Computing (HPC) resources to efficiently compute and
scale complex algorithms across thousands of nodes. Unfortunately, typical data
scientists are not familiar with the unique requirements and characteristics of
HPC environments. They usually develop their applications with high-level
scripting languages or frameworks such as TensorFlow and the installation
process often requires connection to external systems to download open source
software during the build. HPC environments, on the other hand, are often based
on closed source applications that incorporate parallel and distributed
computing API's such as MPI and OpenMP, while users have restricted
administrator privileges, and face security restrictions such as not allowing
access to external systems. In this paper we discuss the issues associated with
the deployment of AI frameworks in a secure HPC environment and how we
successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing
Conferenc
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy
intermediate-scale quantum computers for practical applications. Expanding the
portfolio of such techniques, we propose a quantum circuit learning algorithm
that can be used to assist the characterization of quantum devices and to train
shallow circuits for generative tasks. The procedure leverages quantum hardware
capabilities to its fullest extent by using native gates and their qubit
connectivity. We demonstrate that our approach can learn an optimal preparation
of the Greenberger-Horne-Zeilinger states, also known as "cat states". We
further demonstrate that our approach can efficiently prepare approximate
representations of coherent thermal states, wave functions that encode
Boltzmann probabilities in their amplitudes. Finally, complementing proposals
to characterize the power or usefulness of near-term quantum devices, such as
IBM's quantum volume, we provide a new hardware-independent metric called the
qBAS score. It is based on the performance yield in a specific sampling task on
one of the canonical machine learning data sets known as Bars and Stripes. We
show how entanglement is a key ingredient in encoding the patterns of this data
set; an ideal benchmark for testing hardware starting at four qubits and up. We
provide experimental results and evaluation of this metric to probe the trade
off between several architectural circuit designs and circuit depths on an
ion-trap quantum computer.Comment: 16 pages, 9 figures. Minor revisions. As published in npj Quantum
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