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

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning

Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devices has enabled daily monitoring. However, due to the need for professional expertise in the ECGs interpretation, general public access has once again been restricted, prompting the need for the development of advanced diagnostic algorithms. Classic rule-based algorithms are now completely outperformed by deep learning based methods. But the advancement of smart diagnostic algorithms is hampered by issues like small dataset, inconsistent data labeling, inefficient use of local and global ECG information, memory and inference time consuming deployment of multiple models, and lack of information transfer between tasks. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout, with the help of the development of low-resolution high-level semantic information, by capitalizing on both local morphological information and global rhythm information. From the perspective of effective data leverage and inter-task knowledge transfer, we develop a parameter isolation based ECG continual learning (ECG-CL) approach. We evaluated our model's performance on four open-access datasets by designing segmentation-to-classification for cross-domain incremental learning, minority-to-majority class for category incremental learning, and small-to-large sample for task incremental learning. Our approach is shown to successfully extract informative morphological and rhythmic features from ECG segmentation, leading to higher quality classification results. From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.Comment: 10 page