147 research outputs found
Robust Online Hamiltonian Learning
In this work we combine two distinct machine learning methodologies,
sequential Monte Carlo and Bayesian experimental design, and apply them to the
problem of inferring the dynamical parameters of a quantum system. We design
the algorithm with practicality in mind by including parameters that control
trade-offs between the requirements on computational and experimental
resources. The algorithm can be implemented online (during experimental data
collection), avoiding the need for storage and post-processing. Most
importantly, our algorithm is capable of learning Hamiltonian parameters even
when the parameters change from experiment-to-experiment, and also when
additional noise processes are present and unknown. The algorithm also
numerically estimates the Cramer-Rao lower bound, certifying its own
performance.Comment: 24 pages, 12 figures; to appear in New Journal of Physic
Solving a Higgs optimization problem with quantum annealing for machine learning
The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics
A consensus on the classification of hydrocephalus: its utility in the assessment of abnormalities of cerebrospinal fluid dynamics
Achieving a quantum smart workforce
Interest in building dedicated Quantum Information Science and Engineering
(QISE) education programs has greatly expanded in recent years. These programs
are inherently convergent, complex, often resource intensive and likely require
collaboration with a broad variety of stakeholders. In order to address this
combination of challenges, we have captured ideas from many members in the
community. This manuscript not only addresses policy makers and funding
agencies (both public and private and from the regional to the international
level) but also contains needs identified by industry leaders and discusses the
difficulties inherent in creating an inclusive QISE curriculum. We report on
the status of eighteen post-secondary education programs in QISE and provide
guidance for building new programs. Lastly, we encourage the development of a
comprehensive strategic plan for quantum education and workforce development as
a means to make the most of the ongoing substantial investments being made in
QISE.Comment: 18 pages, 2 figures, 1 tabl
Quantum adiabatic machine learning
We develop an approach to machine learning and anomaly detection via quantum
adiabatic evolution. In the training phase we identify an optimal set of weak
classifiers, to form a single strong classifier. In the testing phase we
adiabatically evolve one or more strong classifiers on a superposition of
inputs in order to find certain anomalous elements in the classification space.
Both the training and testing phases are executed via quantum adiabatic
evolution. We apply and illustrate this approach in detail to the problem of
software verification and validation.Comment: 21 pages, 9 figure
Ventriculocisternostomia de Torkildsen no tratamento do hidrocefalo nĂŁo comunicante: resultados em 67 casos
Technical and Comparative Aspects of Brain Glycogen Metabolism.
It has been known for over 50 years that brain has significant glycogen stores, but the physiological function of this energy reserve remains uncertain. This uncertainty stems in part from several technical challenges inherent in the study of brain glycogen metabolism, and may also stem from some conceptual limitations. Factors presenting technical challenges include low glycogen content in brain, non-homogenous labeling of glycogen by radiotracers, rapid glycogenolysis during postmortem tissue handling, and effects of the stress response on brain glycogen turnover. Here, we briefly review aspects of glycogen structure and metabolism that bear on these technical challenges, and discuss ways these can be overcome. We also highlight physiological aspects of glycogen metabolism that limit the conditions under which glycogen metabolism can be useful or advantageous over glucose metabolism. Comparisons with glycogen metabolism in skeletal muscle provide an additional perspective on potential functions of glycogen in brain
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