Device-independent parallel self-testing of two singlets

Device-independent self-testing is the possibility of certifying the quantum state and the measurements, up to local isometries, using only the statistics observed by querying uncharacterized local devices. In this paper, we study parallel self-testing of two maximally entangled pairs of qubits: in particular, the local tensor product structure is not assumed but derived. We prove two criteria that achieve the desired result: a double use of the Clauser-Horne-Shimony-Holt inequality and the $3\times 3$ Magic Square game. This demonstrate that the magic square game can only be perfectly won by measureing a two-singlets state. The tolerance to noise is well within reach of state-of-the-art experiments.Comment: 9 pages, 2 figure

SELF-TESTING: WALKING ON THE BOUNDARY OF THE QUANTUM SET

Ph.DDOCTOR OF PHILOSOPH

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor QDs are a candidate system for building quantum computers. The present-day tuning techniques for bringing the QD devices into a desirable configuration suitable for quantum computing that rely on heuristics do not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. We show that the learner's accuracy in recognizing the state of a device is ~96.5 % in both current- and charge-sensor-based training. We also introduce a tool that enables other researchers to use this approach for further research: QFlow lite - a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.Comment: 18 pages, 6 figures, 3 table

Geometry of the set of quantum correlations

It is well known that correlations predicted by quantum mechanics cannot be explained by any classical (local-realistic) theory. The relative strength of quantum and classical correlations is usually studied in the context of Bell inequalities, but this tells us little about the geometry of the quantum set of correlations. In other words, we do not have good intuition about what the quantum set actually looks like. In this paper we study the geometry of the quantum set using standard tools from convex geometry. We find explicit examples of rather counter-intuitive features in the simplest non-trivial Bell scenario (two parties, two inputs and two outputs) and illustrate them using 2-dimensional slice plots. We also show that even more complex features appear in Bell scenarios with more inputs or more parties. Finally, we discuss the limitations that the geometry of the quantum set imposes on the task of self-testing.Comment: 11 + 8 pages, 6 figures, v2: added an argument relating self-testing and extremality, v3: typos corrected, results unchanged, published versio

PMP22 exon 4 deletion causes ER retention of PMP22 and a gain-of-function allele in CMT1E

OBJECTIVE: To determine whether predicted fork stalling and template switching (FoSTeS) during mitosis deletes exon 4 in peripheral myelin protein 22 KD (PMP22) and causes gain‐of‐function mutation associated with peripheral neuropathy in a family with Charcot–Marie–Tooth disease type 1E. METHODS: Two siblings previously reported to have genomic rearrangements predicted to involve exon 4 of PMP22 were evaluated clinically and by electrophysiology. Skin biopsies from the proband were studied by RT‐PCR to determine the effects of the exon 4 rearrangements on exon 4 mRNA expression in myelinating Schwann cells. Transient transfection studies with wild‐type and mutant PMP22 were performed in Cos7 and RT4 cells to determine the fate of the resultant mutant protein. RESULTS: Both affected siblings had a sensorimotor dysmyelinating neuropathy with severely slow nerve conduction velocities (<10 m/sec). RT‐PCR studies of Schwann cell RNA from one of the siblings demonstrated a complete in‐frame deletion of PMP22 exon 4 (PMP22Δ4). Transfection studies demonstrated that PMP22Δ4 protein is retained within the endoplasmic reticulum and not transported to the plasma membrane. CONCLUSIONS: Our results confirm that that FoSTeS‐mediated genomic rearrangement produced a deletion of exon 4 of PMP22, resulting in expression of both PMP22 mRNA and protein lacking this sequence. In addition, we provide experimental evidence for endoplasmic reticulum retention of the mutant protein suggesting a gain‐of‐function mutational mechanism consistent with the observed CMT1E in this family. PMP22Δ4 is another example of a mutated myelin protein that is misfolded and contributes to the pathogenesis of the neuropathy

The measurement and modeling of a P2P streaming video service

Most of the work on grid technology in the video area has been generally restricted to aspects of resource scheduling and replica management. The traffic of such a service has a lot of characteristics in common with that of the traditional video service. However the architecture and user behavior in grid networks are quite different from those of the traditional Internet. Considering the potential of grid networks and video sharing services, measuring and analyzing P2P IPTV traffic are important and fundamental works in the field of grid networks. This paper investigates the features of PPLive, the most popular streaming service in China and based on P2P technology. Through monitoring and analyzing PPLive traffic streams, the characteristics of P2P streaming services have been studied. The analyses are carried out in respect of bearing protocols, geographical distribution and the self-similarity properties of the traffic. A streaming service traffic model has been created and verified with the simulation. The simulation results indicate that the proposed streaming service traffic model complies well with the real IPTV streaming service. It can also function as a step towards studying video-sharing services on grids