Quantum discriminator for binary classification

Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces-this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in [Formula: see text] time, and inferencing is performed on a universal quantum computer in [Formula: see text] time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit, and outputs the predicted label. We analyze its performance on the Iris and Bars and Stripes data sets, and show that it can attain 99% accuracy in simulation.

Encoding integers and rationals on neuromorphic computers using virtual neuron

Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network-based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the μ-recursive functions, which are the building blocks of general-purpose computation.

Feasibility of wearable technology for 'real-world' gait analysis in children with Prader-Willi and Angelman syndromes

BACKGROUND

Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are neurodevelopmental disorders in need of innovative 'real-world' outcome measures to evaluate treatment effects. Instrumented gait analysis (IGA) using wearable technology offers a potentially feasible solution to measure "real-world' neurological and motor dysfunction in these groups.

METHODS

Children (50% female; 6-16 years) diagnosed with PWS (n = 9) and AS (n = 5) completed 'real-world' IGA assessments using the Physilog®5 wearable. PWS participants completed a laboratory assessment and a 'real-world' long walk. The AS group completed 'real-world' caregiver-assisted assessments. Mean and variability results for stride time, cadence, stance percentage (%) and stride length were extracted and compared across three different data reduction protocols.

RESULTS

The wearables approach was found to be feasible, with all participants able to complete at least one assessment. This study also demonstrated significant agreement, using Lin's concordance correlation coefficient (CCC), between laboratory and 'real-world' assessments in the PWS group for mean stride length, mean stance % and stance % CV (n = 7, CCC: 0.782-0.847, P = 0.011-0.009).

CONCLUSION

'Real-world' gait analysis using the Physilog®5 wearable was feasible to efficiently assess neurological and motor dysfunction in children affected with PWS and AS.

Opportunities for neuromorphic computing algorithms and applications

Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.

Adiabatic quantum linear regression

A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${2.8 \times }$$\end{document}2.8× speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric. The quantum approach used the D-Wave 2000Q adiabatic quantum computer, whereas the classical approach used a desktop workstation with an 8-core Intel i9 processor. As such, the results obtained in this work must be interpreted within the context of the specific hardware and software implementations of these machines.