Braiding of non-Abelian anyons using pairwise interactions

The common approach to topological quantum computation is to implement quantum gates by adiabatically moving non-Abelian anyons around each other. Here we present an alternative perspective based on the possibility of realizing the exchange (braiding) operators of anyons by adiabatically varying pairwise interactions between them rather than their positions. We analyze a system composed by four anyons whose couplings define a T-junction and we show that the braiding operator of two of them can be obtained through a particular adiabatic cycle in the space of the coupling parameters. We also discuss how to couple this scheme with anyonic chains in order to recover the topological protection.Comment: 8 pages, 7 figures. Errors corrected, clarifications and comments adde

Topological blockade and measurement of topological charge

The fractionally charged quasiparticles appearing in the 5/2 fractional quantum Hall plateau are predicted to have an extra non-local degree of freedom, known as topological charge. We show how this topological charge can block the tunnelling of these particles, and how such 'topological blockade' can be used to readout their topological charge. We argue that the short time scale required for this measurement is favorable for the detection of the non-Abelian anyonic statistics of the quasiparticles. We also show how topological blockade can be used to measure braiding statistics, and to couple a topological qubit with a conventional one.Comment: Published version: one additional paragraph (on the 331 state); Figs. 1 and 4 modified; Ref. 46 adde

Reaching the quantum Hall regime with rotating Rydberg-dressed atoms

Despite the striking progress in the field of quantum gases, one of their much anticipated applications-the simulation of quantum Hall states-remains elusive: all experimental approaches so far have failed in reaching a sufficiently small ratio between atom and vortex densities. In this paper we consider rotating Rydberg-dressed atoms in magnetic traps: these gases offer strong and tunable nonlocal repulsive interactions and very low densities; hence they provide an exceptional platform to reach the quantum Hall regime. Based on the Lindemann criterion and the analysis of the interplay of the length scales of the system, we show that there exists an optimal value of the dressing parameters that minimizes the ratio between the filling factor of the system and its critical value to enter the Hall regime, thus making it possible to reach this strongly correlated phase for more than 1000 atoms under realistic conditions

Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms

We propose Laelaps, an energy-efficient and fast learning algorithm with no false alarms for epileptic seizure detection from long-term intracranial electroencephalography (iEEG) signals. Laelaps uses end-to-end binary operations by exploiting symbolic dynamics and brain-inspired hyperdimensional computing. Laelaps's results surpass those yielded by state-of-the-art (SoA) methods [1], [2], [3], including deep learning, on a new very large dataset containing 116 seizures of 18 drug-resistant epilepsy patients in 2656 hours of recordings - each patient implanted with 24 to 128 iEEG electrodes. Laelaps trains 18 patient-specific models by using only 24 seizures: 12 models are trained with one seizure per patient, the others with two seizures. The trained models detect 79 out of 92 unseen seizures without any false alarms across all the patients as a big step forward in practical seizure detection. Importantly, a simple implementation of Laelaps on the Nvidia Tegra X2 embedded device achieves 1.7 7-3.9 7 faster execution and 1.4 7-2.9 7 lower energy consumption compared to the best result from the SoA methods. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch

Enhancing structural health monitoring with vehicle identification and tracking

Traffic load monitoring and structural health monitoring (SHM) have been gaining increasing attention over the last decade. However, most of the current installations treat the two monitoring types as separated problems, thereby using dedicated installed sensors, such as smart cameras for traffic load or accelerometers for Structural Health Monitoring (SHM). This paper presents a new framework aimed at leveraging the data collected by a SHM system for a second use, namely, monitoring vehicles passing on the structure being monitored (a viaduct). Our framework first processes the raw three-axial acceleration signals through a series of transformations and extracts its energy. Then, an anomaly detection algorithm is used to detect peaks from 90 installed sensors, and a linear regression together with a simple threshold filters out false detection by estimating the speed of the vehicles. Initial results in conditions of moderate traffic load are promising, demonstrating the detection of vehicles and realistic characterization of their speed. Moreover, a k-means clustering analysis distinguishes two groups of peaks with statistically different features such as amplitude and damping duration that could be likely associated with heavy vehicles and cars, respectively

Adversarially-Trained Tiny Autoencoders for Near-Sensor Continuous Structural Health Monitoring

Structural Health Monitoring (SHM) systems are increasingly employed in many civil structures such as buildings, tunnels and viaducts. Typical installations consist of sensors that gather information and send it to a central computing unit, which then periodically analyzes the incoming data and produces an assessment of the structure conditions. To avoid the transmission of a huge amount of raw data and reduce latency in the detection of structural anomalies, recent works focus on moving computation on the sensor nodes. This work shows that a small autoencoder, which fits the tiny 2 MB memory of a typical microcontroller used for SHM sensor nodes can achieve very competitive accuracy in detecting structural anomalies as well as vehicle passage on bridges by leveraging adversarial training based on generative adversarial networks (GANs). We improve accuracy over state-of-the-art algorithms in two use-cases on real-standing buildings: i) predicting anomalies on a bridge (+7.4%) and ii) detecting vehicles on a viaduct (2.30 x )

Work-in-Progress: DORY: Lightweight Memory Hierarchy Management for Deep NN Inference on IoT Endnodes

IoT endnodes often couple a small and fast L1 scratchpad memory with higher-capacity but lower bandwidth and speed L2 background memory. The absence of a coherent hardware cache hierarchy saves energy but comes at the cost of labor-intensive explicit memory management, complicating the deployment of algorithms with large data memory footprint, such as Deep Neural Network (DNN) inference. In this work, we present DORY, a lightweight software-cache dedicated to DNN Deployment Oriented to memoRY. DORY leverages static data tiling and DMA-based double buffering to hide the complexity of manual L1-L2 memory traffic management. DORY enables storage of activations and weights in L2 with less than 4% performance overhead with respect to direct execution in L1. We show that a 142 kB DNN achieving 79.9% on CIFAR-10 runs 3.2x faster compared to its execution directly from L2 memory while consuming 1.9x less energy

Work-in-progress: Dory: Lightweight memory hierarchy management for deep NN inference on iot endnodes

IoT endnodes often couple a small and fast L1 scratchpad memory with higher-capacity but lower bandwidth and speed L2 background memory. The absence of a coherent hardware cache hierarchy saves energy but comes at the cost of labor-intensive explicit memory management, complicating the deployment of algorithms with large data memory footprint, such as Deep Neural Network (DNN) inference. In this work, we present DORY, a lightweight software-cache dedicated to DNN Deployment Oriented to memoRY. DORY leverages static data tiling and DMA-based double buffering to hide the complexity of manual L1-L2 memory traffic management. DORY enables storage of activations and weights in L2 with less than 4% performance overhead with respect to direct execution in L1. We show that a 142 kB DNN achieving 79.9% on CIFAR-10 runs 3.2 7 faster compared to its execution directly from L2 memory while consuming 1.9 7 less energy