1,098 research outputs found
Quantum device fine-tuning using unsupervised embedding learning
Quantum devices with a large number of gate electrodes allow for precise
control of device parameters. This capability is hard to fully exploit due to
the complex dependence of these parameters on applied gate voltages. We
experimentally demonstrate an algorithm capable of fine-tuning several device
parameters at once. The algorithm acquires a measurement and assigns it a score
using a variational auto-encoder. Gate voltage settings are set to optimise
this score in real-time in an unsupervised fashion. We report fine-tuning times
of a double quantum dot device within approximately 40 min
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
Mainstream machine-learning techniques such as deep learning and
probabilistic programming rely heavily on sampling from generally intractable
probability distributions. There is increasing interest in the potential
advantages of using quantum computing technologies as sampling engines to speed
up these tasks or to make them more effective. However, some pressing
challenges in state-of-the-art quantum annealers have to be overcome before we
can assess their actual performance. The sparse connectivity, resulting from
the local interaction between quantum bits in physical hardware
implementations, is considered the most severe limitation to the quality of
constructing powerful generative unsupervised machine-learning models. Here we
use embedding techniques to add redundancy to data sets, allowing us to
increase the modeling capacity of quantum annealers. We illustrate our findings
by training hardware-embedded graphical models on a binarized data set of
handwritten digits and two synthetic data sets in experiments with up to 940
quantum bits. Our model can be trained in quantum hardware without full
knowledge of the effective parameters specifying the corresponding quantum
Gibbs-like distribution; therefore, this approach avoids the need to infer the
effective temperature at each iteration, speeding up learning; it also
mitigates the effect of noise in the control parameters, making it robust to
deviations from the reference Gibbs distribution. Our approach demonstrates the
feasibility of using quantum annealers for implementing generative models, and
it provides a suitable framework for benchmarking these quantum technologies on
machine-learning-related tasks.Comment: 17 pages, 8 figures. Minor further revisions. As published in Phys.
Rev.
Evaluation of synthetic and experimental training data in supervised machine learning applied to charge state detection of quantum dots
Automated tuning of gate-defined quantum dots is a requirement for large
scale semiconductor based qubit initialisation. An essential step of these
tuning procedures is charge state detection based on charge stability diagrams.
Using supervised machine learning to perform this task requires a large dataset
for models to train on. In order to avoid hand labelling experimental data,
synthetic data has been explored as an alternative. While providing a
significant increase in the size of the training dataset compared to using
experimental data, using synthetic data means that classifiers are trained on
data sourced from a different distribution than the experimental data that is
part of the tuning process. Here we evaluate the prediction accuracy of a range
of machine learning models trained on simulated and experimental data and their
ability to generalise to experimental charge stability diagrams in two
dimensional electron gas and nanowire devices. We find that classifiers perform
best on either purely experimental or a combination of synthetic and
experimental training data, and that adding common experimental noise
signatures to the synthetic data does not dramatically improve the
classification accuracy. These results suggest that experimental training data
as well as realistic quantum dot simulations and noise models are essential in
charge state detection using supervised machine learning
A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing
The ever-increasing number of materials science articles makes it hard to
infer chemistry-structure-property relations from published literature. We used
natural language processing (NLP) methods to automatically extract material
property data from the abstracts of polymer literature. As a component of our
pipeline, we trained MaterialsBERT, a language model, using 2.4 million
materials science abstracts, which outperforms other baseline models in three
out of five named entity recognition datasets when used as the encoder for
text. Using this pipeline, we obtained ~300,000 material property records from
~130,000 abstracts in 60 hours. The extracted data was analyzed for a diverse
range of applications such as fuel cells, supercapacitors, and polymer solar
cells to recover non-trivial insights. The data extracted through our pipeline
is made available through a web platform at https://polymerscholar.org which
can be used to locate material property data recorded in abstracts
conveniently. This work demonstrates the feasibility of an automatic pipeline
that starts from published literature and ends with a complete set of extracted
material property information
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