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

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

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

Phytochemicals as novel agents for the induction of browning in white adipose tissue

Obesity and its associated metabolic syndrome continue to be a health epidemic in westernized societies and is catching up in the developing world. Despite such increases, little headway has been made to reverse adverse weight gain in the global population. Few medical options exist for the treatment of obesity which points to the necessity for exploration of anti-obesity therapies including pharmaceutical and nutraceutical compounds. Defects in brown adipose tissue, a major energy dissipating organ, has been identified in the obese and is hypothesized to contribute to the overall metabolic deficit observed in obesity. Not surprisingly, considerable attention has been placed on the discovery of methods to activate brown adipose tissue. A variety of plant-derived, natural compounds have shown promise to regulate brown adipose tissue activity and enhance the lipolytic and catabolic potential of white adipose tissue. Through activation of the sympathetic nervous system, thyroid hormone signaling, and transcriptional regulation of metabolism, natural compounds such as capsaicin and resveratrol may provide a relatively safe and effective option to upregulate energy expenditure. Through utilizing the energy dissipating potential of such nutraceutical compounds, the possibility exists to provide a therapeutic solution to correct the energy imbalance that underlines obesity

Epigenetic modulators as therapeutic targets in prostate cancer

Prostate cancer is one of the most common non-cutaneous malignancies among men worldwide. Epigenetic aberrations, including changes in DNA methylation patterns and/or histone modifications, are key drivers of prostate carcinogenesis. These epigenetic defects might be due to deregulated function and/or expression of the epigenetic machinery, affecting the expression of several important genes. Remarkably, epigenetic modifications are reversible and numerous compounds that target the epigenetic enzymes and regulatory proteins were reported to be effective in cancer growth control. In fact, some of these drugs are already being tested in clinical trials. This review discusses the most important epigenetic alterations in prostate cancer, highlighting the role of epigenetic modulating compounds in pre-clinical and clinical trials as potential therapeutic agents for prostate cancer management.info:eu-repo/semantics/publishedVersio