Microwave-based controlled quantum dynamics in trapped ions

The key research aim of the present thesis is the building of a universal set of quantum gates for a long wavelength trapped-ion quantum information processor. It is desired to realise quantum computation using microwave and radio wave sources in a linear ion trap, where a static magnetic field gradient has been added to enhance motional and atomic state coupling. Furthermore, the qubit is constructed with the intrinsic use of superposition states generated with the help of constant microwave fields in the background: dressed states. This technique is essential for the shielding of the quantum operations against the unavoidable effects of magnetic noise. After reviewing the preliminary results and discussing briefly an auxiliary experimental technique intrinsic to the set-up, we introduce the magnetic gradient coupling and the dressed state scheme. We then proceed to illustrate how single and multi-qubit gates can be realised within such a system. Theoretical arguments are supplemented by numerical simulation and sources of experimental noise are taken into account.Open Acces

Universal Set of Gates for Microwave Dressed-State Quantum Computing

We propose a set of techniques that enable universal quantum computing to be carried out using dressed states. This applies in particular to the effort of realising quantum computation in trapped ions using long-wavelength radiation, where coupling enhancement is achieved by means of static magnetic-field gradient. We show how the presence of dressing fields enables the construction of robust single and multi-qubit gates despite the unavoidable presence of magnetic noise, an approach that can be generalised to provide shielding in any analogous quantum system that relies on the coupling of electronic degrees of freedom via bosonic modes

Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201

Cosmic microwave background anomalies viewed via Gumbel Statistics

We describe and discuss the application of Gumbel statistics, which model extreme events, to WMAP 5-year measurements of the cosmic microwave background. We find that temperature extrema of the CMB are well modelled by the Gumbel formalism and describe tests for Gaussianity that the approach can provide. Comparison to simulations reveals Gumbel statistics to have only weak discriminatory power for the conventional statistic: $f_{NL}<1000$, though it may probe other regimes of non-Gaussianity. Tests based on hemispheric cuts reveal interesting alignment with other reported CMB anomalies. The approach has the advantage of model independence and may find further utility with smaller scale data.Comment: 5 pages, 8 figures, accepted for publication in MNRAS. This version: added reference

Passive mobile sensing and psychological traits for large scale mood prediction

Experience sampling has long been the established method to sample people’s mood in order to assess their mental state. Smartphones have started to be used as experience sampling tools for mental health state as they accompany individuals during their day and can therefore gather in-the-moment data. However, the granularity of the data needs to be traded off with the level of interruption these tools introduce on users’ activities. As a consequence the data collected with this technique is often sparse. This has been obviated by the use of passive sensing in addition to mood reports, however this adds additional noise. In this paper we show that psychological traits collected through one-off questionnaires combined with passively collected sensing data (movement from the accelerometer and noise levels from the microphone) can be used to detect individuals whose general mood deviates from the common relaxed characteristic of the general population. By using the reported mood as a classification target we show how to design models that depend only on passive sensors and one-off questionnaires, without bothering users with tedious experience sampling. We validate our approach by using a large dataset of mood reports and passive sensing data collected in the wild with tens of thousands of participants, finding that the combination of these modalities has the best classification performance, and that passive sensing yields a +5% boost in accuracy. We also show that sensor data collected for the duration of a week performs better than when only using data collected for single days for this task. We discuss feature extraction techniques and appropriate classifiers for this kind of multimodal data, as well as overfitting shortcomings of using deep learning to handle static and dynamic features. We believe these findings have significant implications for mobile health applications that can benefit from the correct modeling of passive sensing along with extra user metadata.This work was partially funded by the Embiricos Trust Scholarship of Jesus College, Cambridge and the EPSRC Doctoral Training Partnership (grant reference EP/N509620/1)

Passive mobile sensing and psychological traits for large scale mood prediction

Experience sampling has long been the established method to sample people's mood in order to assess their mental state. Smartphones start to be used as experience sampling tools for mental health state as they accompany individuals during their day and can therefore gather in-the-moment data. However, the granularity of the data needs to be traded off with the level of interruption these tools introduce. As a consequence the data collected with this technique is often sparse. This has been obviated by the use of passive sensing in addition to mood reports, however, this adds additional noise.In this paper we show that psychological traits collected through one-off questionnaires combined with passively collected sensing data (movement from the accelerometer and noise levels from the microphone) can be used to detect individuals whose general mood deviates from the common relaxed characteristic of the general population. By using the reported mood as a classification target we show how to design models that depend only on passive sensors and one-off questionnaires, without bothering users with tedious experience sampling. We validate our approach by using a large dataset of mood reports and passive sensing data collected in the wild with tens of thousands of participants, finding that the combination of these modalities achieves the best classification performance, and that passive sensing yields a +5% boost in accuracy. We also show that sensor data collected for a week performs better than single days for this task. We discuss feature extraction techniques and appropriate classifiers for this kind of multimodal data, as well as overfitting shortcomings of using deep learning to handle static and dynamic features. We believe these findings have significant implications for mobile health applications that can benefit from the correct modeling of passive sensing along with extra user metadata.<br/