Quantum and Classical in Adiabatic Computation

Adiabatic transport provides a powerful way to manipulate quantum states. By preparing a system in a readily initialised state and then slowly changing its Hamiltonian, one may achieve quantum states that would otherwise be inaccessible. Moreover, a judicious choice of final Hamiltonian whose groundstate encodes the solution to a problem allows adiabatic transport to be used for universal quantum computation. However, the dephasing effects of the environment limit the quantum correlations that an open system can support and degrade the power of such adiabatic computation. We quantify this effect by allowing the system to evolve over a restricted set of quantum states, providing a link between physically inspired classical optimisation algorithms and quantum adiabatic optimisation. This new perspective allows us to develop benchmarks to bound the quantum correlations harnessed by an adiabatic computation. We apply these to the D-Wave Vesuvius machine with revealing - though inconclusive - results

Construction of an instrument for the measurement of educational philosophy

Thesis (M.Ed.)--Boston Universit

A tradeoff in simultaneous quantum-limited phase and loss estimation in interferometry

Interferometry with quantum light is known to provide enhanced precision for estimating a single phase. However, depending on the parameters involved, the quantum limit for the simultaneous estimation of multiple parameters may not attainable, leading to trade-offs in the attainable precisions. Here we study the simultaneous estimation of two parameters related to optical interferometry: phase and loss, using a fixed number of photons. We derive a trade-off in the estimation of these two parameters which shows that, in contrast to single-parameter estimation, it is impossible to design a strategy saturating the quantum Cramer-Rao bound for loss and phase estimation in a single setup simultaneously. We design optimal quantum states with a fixed number of photons achieving the best possible simultaneous precisions. Our results reveal general features about concurrently estimating Hamiltonian and dissipative parameters, and has implications for sophisticated sensing scenarios such as quantum imaging.Comment: 9 pages, 6 figure

Travelling waves in a drifting flux lattice

Starting from the time-dependent Ginzburg-Landau (TDGL) equations for a type II superconductor, we derive the equations of motion for the displacement field of a moving vortex lattice without inertia or pinning. We show that it is linearly stable and, surprisingly, that it supports wavelike long-wavelength excitations arising not from inertia or elasticity but from the strain-dependent mobility of the moving lattice. It should be possible to image these waves, whose speeds are a few \mu m/s, using fast scanning tunnelling microscopy.Comment: 4 pages, revtex, 2 .eps figures imbedded in paper, title shortened, minor textual change

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac

Interstitial lung disease incidence and mortality in the United Kingdom and the European Union: an observational study, 2001-2017

Objective: To compare the trends in age-standardised incidence and mortality from interstitial lung diseases (ILD) in the United Kingdom (UK) and the European Union (EU). Design: Observational study using data obtained from the Global Burden of Disease Study. Setting and Participants: Residents of the UK and of the twenty-seven EU countries. Main outcome measures: ILD age-standardised incidence rates per 100 000 (ASIR), age-standardised death rates per 100 000 (ASDR), and mortality-to-incidence ratio (MIRs) are presented for males and females separately for each country, for the years 2001–2017. Trends were analysed using Joinpoint regression analysis. Results: For men, in 2017, the median incidence of ILD was 7.22 (IQR 5.57–8.96) per 100 000 population. For women, in 2017, the median incidence of ILD was 4.34 (IQR 3.36–6.29) per 100 000 population. For men, in 2017, the median ASDR attributed to ILD was 2.04 (IQR 1.13–2.71) per 100 000 population. For women, the median ASDR in 2017 for ILD was 1.02 (0.68–1.37) per 100 000 population. There was an overall increase in ASDR during the observation period with a median change of +20.42% (IQR 5.44–31.40) for men and an increase of +15.44% (IQR −1.01–31.52) for women. Despite increases in mortality over the entire observation period, there were decreasing mortality trends in the majority of countries at the end of the observation period (75% for men and 86% for women). Conclusion: Over the past two decades, there have been increases in the incidence and mortality of interstitial lung diseases in Europe. The most recent trends, however, demonstrate decreases in mortality from ILD in the majority of European countries for both men and women. These data support the ongoing improvements in the diagnosis and management of ILD

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709