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We specify an algorithm to enumerate a minimum complete set of combinatorially non-isomorphic orthogonal arrays of given strength t, run-size N, and level-numbers of the factors. The algorithm is the first one handling general mixed-level and pure-level cases. Using an implementation in C, we generate most non-trivial series for t = 2, N ≤ 28, t = 3, N ≤ 64, and t = 4, N ≤ 168. The exceptions define limiting run-sizes for which the algorithm returns complete sets in a reasonable amount of time. q

Electrostatically defined quantum dot arrays offer a compelling platform for quantum computation and simulation. However, tuning up such arrays with existing techniques becomes impractical when going beyond a handful of quantum dots. Here, we present a method for systematically adding quantum dots to an array one dot at a time, in such a way that the number of electrons on previously formed dots is unaffected. The method allows individual control of the number of electrons on each of the dots, as well as of the interdot tunnel rates. We use this technique to tune up a linear array of eight GaAs quantum dots such that they are occupied by one electron each. This new method overcomes a critical bottleneck in scaling up quantum-dot based qubit registers. arXiv:1901.00426v1 [cond-mat.mes-hall]

We report the computer-automated tuning of gate-defined semiconductor double quantum dots in GaAs heterostructures. We benchmark the algorithm by creating three double quantum dots inside a linear array of four quantum dots. The algorithm sets the correct gate voltages for all the gates to tune the double quantum dots into the single-electron regime. The algorithm only requires (1) prior knowledge of the gate design and (2) the pinch-off value of the single gate T that is shared by all the quantum dots. This work significantly alleviates the user effort required to tune multiple quantum dot devices.

Semiconductor quantum dot arrays defined electrostatically in a 2D electron gas provide a scalable platform for quantum information processing and quantum simulations. For the operation of quantum dot arrays, appropriate voltages need to be applied to the gate electrodes that define the quantum dot potential landscape. Tuning the gate voltages has proven to be a time-consuming task, because of initial electrostatic disorder and capacitive cross-talk effects. Here, we report on the automated tuning of the inter-dot tunnel coupling in gate-defined semiconductor double quantum dots. The automation of the tuning of the inter-dot tunnel coupling is the next step forward in scalable and efficient control of larger quantum dot arrays. This work greatly reduces the effort of tuning semiconductor quantum dots for quantum information processing and quantum simulation.

Rest, "Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall," Proc. SPIE, Vol. 9253, (2014). http://dx.doi. org/10.1117/12.2066851 Copyright 2014 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only.Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. Automatic detection of suspicious behavior of pickpockets with trackbased features in a shopping mall ABSTRACTProactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snatch is a subtle action, and because collaboration is complex social behavior. We carried out an experiment with more than 20 validated pickpocket incidents. We used a top-down approach to translate expert knowledge in features and rules, and a bottom-up approach to learn discriminating patterns with a classifier. The classifier was used to separate the pickpockets from normal passers-by who are shopping in the mall. We performed a cross validation to train and evaluate our system. In this paper, we describe our method, identify the most valuable features, and analyze the results that were obtained in the experiment. We estimate the quality of these features and the performance of automatic detection of (collaborating) pickpockets. The results show that many of the pickpockets can be detected at a low false alarm rate.

The mission of QuTech is to bring quantum technology to industry and society by translating fundamental scientific research into applied research. To this end we are developing Quantum Inspire (QI), a full-stack quantum computer prototype for future co-development and collaborative R&D in quantum computing.A prerelease of this prototype system is already offering the public cloud-based access to QuTech technologies such as a programmable quantum computer simulator (with up to 31 qubits) and tutorials and user background knowledge on quantum information science (www.quantum-inspire.com). Access to a programmable CMOS-compatible Silicon spin qubit-based quantum processor will be provided in the next deployment phase. The first generation of QI's quantum processors consists of a double quantum dot hosted in an in-house grown SiGe/ 28 Si/SiGe heterostructure, and defined with a single layer of Al gates.Here we give an overview of important aspects of the QI full-stack. We illustrate QI's modular system architecture and we will touch on parts of the manufacturing and electrical characterization of its first generation two spin qubit quantum processor unit. We close with a section on QI's qubit calibration framework. The definition of a single qubit Pauli X gate is chosen as concrete example of the matching of an experiment to a component of the circuit model for quantum computation.

Abstract-Many threats in the real world can be related to activities in open sources on the internet. Early detection of threats based on internet information could assist in the prevention of incidents. However, the amount of data in social media, blogs and forums rapidly increases and it is time consuming for security services to monitor all these open sources. Therefore, it is important to have a system that automatically ranks messages based on their threat potential and thereby allows security operators to check these messages more efficiently. In this paper, we present a novel method for detecting threatening messages on Twitter based on trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content.