Ballistic one-dimensional holes with strong g-factor anisotropy in germanium

We report experimental evidence of ballistic hole transport in one-dimensional quantum wires gate-defined in a strained SiGe/Ge/SiGe quantum well. At zero magnetic field, we observe conductance plateaus at integer multiples of 2e2/h. At finite magnetic field, the splitting of these plateaus by Zeeman effect reveals largely anisotropic g-factors with absolute values below 1 in the quantum-well plane, and exceeding 10 out-of-plane. This g-factor anisotropy is consistent with a heavy-hole character of the propagating valence-band states, which is in line with a predominant confinement in the growth direction. Remarkably, we observe quantized ballistic conductance in device channels up to 600 nm long. These findings mark an important step toward the realization of novel devices for applications in quantum spintronics

Probing quantum devices with radio-frequency reflectometry

Many important phenomena in quantum devices are dynamic, meaning that they cannot be studied using time-averaged measurements alone. Experiments that measure such transient effects are collectively known as fast readout. One of the most useful techniques in fast electrical readout is radio-frequency reflectometry, which can measure changes in impedance (both resistive and reactive) even when their duration is extremely short, down to a microsecond or less. Examples of reflectometry experiments, some of which have been realized and others so far only proposed, include projective measurements of qubits and Majorana devices for quantum computing, real-time measurements of mechanical motion, and detection of non-equilibrium temperature fluctuations. However, all of these experiments must overcome the central challenge of fast readout: the large mismatch between the typical impedance of quantum devices (set by the resistance quantum) and of transmission lines (set by the impedance of free space). Here, we review the physical principles of radio-frequency reflectometry and its close cousins, measurements of radio-frequency transmission and emission. We explain how to optimize the speed and sensitivity of a radio-frequency measurement and how to incorporate new tools, such as superconducting circuit elements and quantum-limited amplifiers into advanced radio-frequency experiments. Our aim is threefold: to introduce the readers to the technique, to review the advances to date, and to motivate new experiments in fast quantum device dynamics. Our intended audience includes experimentalists in the field of quantum electronics who want to implement radio-frequency experiments or improve them, together with physicists in related fields who want to understand how the most important radio-frequency measurements work

Modeling Sustainability Reporting with Ternary Attractor Neural Networks

International Conference on Mining Intelligence and Knowledge Exploration. Cluj-Napoca, Romania, December 20–22, 2018This work models the Corporate Sustainability General Reporting Initiative (GRI) using a ternary attractor network. A dataset of years evolution of the GRI reports for a world-wide set of companies was compiled from a recent work and adapted to match the pattern coding for a ternary attractor network. We compare the performance of the network with a classical binary attractor network. Two types of criteria were used for encoding the ternary network, i.e., a simple and weighted threshold, and the performance retrieval was better for the latter, highlighting the importance of the real patterns’ transformation to the three-state coding. The network exceeds the retrieval performance of the binary network for the chosen correlated patterns (GRI). Finally, the ternary network was proved to be robust to retrieve the GRI patterns with initial noise.This work has been supported by Spanish grants MINECO (http://www.mineco.gob.es/) TIN2014-54580-R, TIN2017-84452-R, and by UAMSantander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02

Quantum device fine-tuning using unsupervised embedding learning

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min

Report of the Scientific Council Meeting 01 -15 June 2017

Council met at the Sobey Building, Saint Mary’s University, Halifax, NS, Canada, during 01 – 15 June 2017, to consider the various matters in its Agenda. Representatives attended from Canada, Denmark (in respect of Faroe Islands and Greenland), the European Union (France, Germany (via WebEx), Portugal, Spain, the United Kingdom and the European Commission), Japan, the Russian Federation and the United States of America. Observers from the Ecology Action Centre and Dalhousie University were also present. The Executive Secretary, Scientific Council Coordinator and other members of the Secretariat were in attendance. The Executive Committee met prior to the opening session of the Council to discuss the provisional agenda and plan of work. The Council was called to order at 1000 hours on 01 June 2017. The provisional agenda was adopted with modification. The Scientific Council Coordinator was appointed the rapporteur. The Council was informed that the meeting was quorate and authorization had been received by the Executive Secretary for proxy votes from the European Union, Denmark (in respect of Faroe Islands and Greenland), Iceland, Japan, Republic of Korea, and Norway. The opening session was adjourned at 1200 hours on 01 June 2017. Several sessions were held throughout the course of the meeting to deal with specific items on the agenda. The Council considered adopted the STACFEN report on 8 June 2017, and the STACPUB, STACFIS and STACREC reports on 15 June 2017. The concluding session was called to order at 0830 hours on 15 June 2017. The Council considered and adopted the report the Scientific Council Report of this meeting of 01 -15 June 2017. The Chair received approval to leave the report in draft form for about two weeks to allow for minor editing and proof-reading on the usual strict understanding there would be no substantive changes. The meeting was adjourned at 1430 hours on 15 June 2017. The Reports of the Standing Committees as adopted by the Council are appended as follows: Appendix I - Report of the Standing Committee on Fisheries Environment (STACFEN), Appendix II - Report of Standing Committee on Publications (STACPUB), Appendix III - Report of Standing Committee on Research Coordination (STACREC), and Appendix IV - Report of Standing Committee on Fisheries Science (STACFIS). The Agenda, List of Research (SCR) and Summary (SCS) Documents, and List of Representatives, Advisers and Experts, are given in Appendix V-VII. The Council’s considerations on the Standing Committee Reports, and other matters addressed by the Council follow in Sections II-XV

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies

Sensitive radio-frequency read-out of quantum dots using an ultra-low-noise SQUID amplifier

Fault-tolerant spin-based quantum computers will require fast and accurate qubit readout. This can be achieved using radio-frequency reflectometry given sufficient sensitivity to the change in quantum capacitance associated with the qubit states. Here, we demonstrate a 23-fold improvement in capacitance sensitivity by supplementing a cryogenic semiconductor amplifier with a SQUID preamplifier. The SQUID amplifier operates at a frequency near 200 MHz and achieves a noise temperature below 600 mK when integrated into a reflectometry circuit, which is within a factor 120 of the quantum limit. It enables a record sensitivity to capacitance of 0.07 aF/ \sqrt{Hz}. The setup is used to acquire charge stability diagrams of a gate-defined double quantum dot in a short time with a signal-to-noise ration of about 38 in 1 μs of integration time

Red Clump Morphology as Evidence Against a New Intervening Stellar Population as the Primary Source of Microlensing Toward the LMC

We examine the morphology of the color-magnitude diagram (CMD) for core helium-burning (red clump) stars to test the recent suggestion by Zaritsky & Lin (1997) that an extension of the red clump in the Large Magellanic Cloud (LMC) toward brighter magnitudes is due to an intervening population of stars that is responsible for a significant fraction of the observed microlensing toward the LMC. Using our own CCD photometry of several fields across the LMC, we confirm the presence of this additional red clump feature, but conclude that it is caused by stellar evolution rather than a foreground population. We do this by demonstrating that the feature (1) is present in all our LMC fields, (2) is in precise agreement with the location of the blue loops in the isochrones of intermediate age red clump stars with the metallicity and age of the LMC, (3) has a relative density consistent with stellar evolution and LMC star formation history, and (4) is present in the Hipparcos CMD for the solar neighborhood where an intervening population cannot be invoked. Assuming there is no systematic shift in the model isochrones, which fit the Hipparcos data in detail, a distance modulus of $\mu_{LMC} = 18.3$ provides the best fit to our dereddened CMD.Comment: 21 pages LaTex (aaspp4.sty), including three tables and 9 figures (1 is .ps, 8 are .gif). Accepted for publication by Astronomical Journal on March 16, 1998. One error corrected and major revisions now lead to an even stronger argument for the stellar evolutionary origin of features in the LMC color magnitude diagram, claimed by others to be an intervening stellar population and major source of microlensing optical depth toward the LM

No Language-Specific Activation during Linguistic Processing of Observed Actions

It has been suggested that cortical neural systems for language evolved from motor cortical systems, in particular from those fronto-parietal systems responding also to action observation. While previous studies have shown shared cortical systems for action--or action observation--and language, they did not address the question of whether linguistic processing of visual stimuli occurs only within a subset of fronto-parietal areas responding to action observation. If this is true, the hypothesis that language evolved from fronto-parietal systems matching action execution and action observation would be strongly reinforced.We used functional magnetic resonance imaging (fMRI) while subjects watched video stimuli of hand-object-interactions and control photo stimuli of the objects and performed linguistic (conceptual and phonological), and perceptual tasks. Since stimuli were identical for linguistic and perceptual tasks, differential activations had to be related to task demands. The results revealed that the linguistic tasks activated left inferior frontal areas that were subsets of a large bilateral fronto-parietal network activated during action perception. Not a single cortical area demonstrated exclusive--or even simply higher--activation for the linguistic tasks compared to the action perception task.These results show that linguistic tasks do not only share common neural representations but essentially activate a subset of the action observation network if identical stimuli are used. Our findings strongly support the evolutionary hypothesis that fronto-parietal systems matching action execution and observation were co-opted for language, a process known as exaptation