Simulating Entanglement beyond Quantum Steering

While quantum entanglement is a highly non-classical feature, certain entangled states cannot realize the nonlocal effect of quantum steering. In this work, we quantify the resource content of such states in terms of how much shared randomness is needed to simulate their dynamical behavior. We rigorously show that the shared randomness cost is unbounded even for some two-qubit unsteerable states. Moreover, the simulation cost for entangled states is always strictly greater than that of any separable state. Our work utilizes the equivalence between steering and measurement incompatibility, and it connects both to the zonotope approximation problem of Banach space theory.Comment: We change the focus of our paper from the compatible complexity of measurement to the simulation cost of an entangled but unsteerable quantum state, few interesting property of such a class of state emerge from these discussion

Concatenation of the Gottesman-Kitaev-Preskill code with the XZZX surface code

Bosonic codes provide an alternative option for quantum error correction. An important category of bosonic codes called the Gottesman-Kitaev-Preskill (GKP) code has aroused much interest recently. Theoretically, the error correction ability of GKP code is limited since it can only correct small shift errors in position and momentum quadratures. A natural approach to promote the GKP error correction for large-scale, fault-tolerant quantum computation is concatenating encoded GKP states with a stabilizer code. The performance of the XZZX surface-GKP code, i.e., the single-mode GKP code concatenated with the XZZX surface code is investigated in this paper under two different noise models. Firstly, in the code-capacity noise model, the asymmetric rectangular GKP code with parameter $\lambda$ is introduced. Using the minimum weight perfect matching decoder combined with the continuous-variable GKP information, the optimal threshold of the XZZX-surface GKP code reaches $\sigma\approx0.67$ when $\lambda=2.1$, compared with the threshold $\sigma\approx0.60$ of the standard surface-GKP code. Secondly, we analyze the shift errors of two-qubit gates in the actual implementation and build the full circuit-level noise model. By setting the appropriate bias parameters, the logical error rate is reduced by several times in some cases. These results indicate the XZZX surface-GKP codes are more suitable for asymmetric concatenation under the general noise models. We also estimate the overhead of the XZZX-surface GKP code which uses about 291 GKP states with the noise parameter 18.5 dB ($\kappa/g \approx 0.71\%$) to encode a logical qubit with the error rate $2.53\times10^{-7}$, compared with the qubit-based surface code using 3041 qubits to achieve almost the same logical error rate.Comment: 17 pages, 10 figure

DFL: High-Performance Blockchain-Based Federated Learning

Many researchers are trying to replace the aggregation server in federated learning with a blockchain system to achieve better privacy, robustness and scalability. In this case, clients will upload their updated models to the blockchain ledger, and use a smart contract on the blockchain system to perform model averaging. However, running machine learning applications on the blockchain is almost impossible because a blockchain system, which usually takes over half minute to generate a block, is extremely slow and unable to support machine learning applications. This paper proposes a completely new public blockchain architecture called DFL, which is specially optimized for distributed federated machine learning. This architecture inherits most traditional blockchain merits and achieves extremely high performance with low resource consumption by waiving global consensus. To characterize the performance and robustness of our architecture, we implement the architecture as a prototype and test it on a physical four-node network. To test more nodes and more complex situations, we build a simulator to simulate the network. The LeNet results indicate our system can reach over 90% accuracy for non-I.I.D. datasets even while facing model poisoning attacks, with the blockchain consuming less than 5% of hardware resources.Comment: 11 pages, 17 figure

Hybrid Design of Multiplicative Watermarking for Defense Against Malicious Parameter Identification

Watermarking is a promising active diagnosis technique for detection of highly sophisticated attacks, but is vulnerable to malicious agents that use eavesdropped data to identify and then remove or replicate the watermark. In this work, we propose a hybrid multiplicative watermarking (HMWM) scheme, where the watermark parameters are periodically updated, following the dynamics of the unobservable states of specifically designed piecewise affine (PWA) hybrid systems. We provide a theoretical analysis of the effects of this scheme on the closed-loop performance, and prove that stability properties are preserved. Additionally, we show that the proposed approach makes it difficult for an eavesdropper to reconstruct the watermarking parameters, both in terms of the associated computational complexity and from a systems theoretic perspective.Comment: 8 pages, first submission to the 62nd IEEE Conference on Decision and Contro

Optimization of Combined Casing Treatment Structure Applied in a Transonic Axial Compressor Based on Surrogate Model

For modern high load compressors, an excellent stability-enhancing capability by casing treatment (CT) is desirable. However, it is very time consuming to accomplish effective CT design. In this study, a new combined CT structure composed of axial skewed slots and end-wall injection, was proposed to be installed in transonic axial compressors to improve the overall performance. Considering the high computation cost for CFD simulation of the flow field in transonic compressor, a Gaussian Process Regression (GPR) surrogate model combined with Latin hypercube sampling, was utilized to predict compressor performance. For optimization process, a multi-objective evolutionary algorithm (NSGA-Ⅱ) was adopted to obtain the Pareto-optimal front. The main geometric parameters of the slot and the mass-flow rate of injection were selected as design parameters, with the peak efficiency and pressure ratio being two objectives. The results indicated that the surrogate model works well in capturing the key features of the concerning target and accelerating the optimization process. The optimal scheme of the combined CT was found able to increase stall margin (SM) by 19.5% with low efficiency penalty, showing a better performance than the reference combined casing treatment (CCT) scheme. What’s more, the analysis results of entropy generation showed that the superior effect of optimized scheme (OPT) can be attributed to the improvement of exchange flow in slots and the decreased loss in the whole passage

Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling

On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy

The influence of constitutive law choice used to characterise atherosclerotic tissue material properties on computing stress values in human carotid plaques.

Calculating high stress concentration within carotid atherosclerotic plaques has been shown to be complementary to anatomical features in assessing vulnerability. Reliability of stress calculation may depend on the constitutive laws/strain energy density functions (SEDFs) used to characterize tissue material properties. Different SEDFs, including neo-Hookean, one-/two-term Ogden, Yeoh, 5-parameter Mooney-Rivlin, Demiray and modified Mooney-Rivlin, have been used to describe atherosclerotic tissue behavior. However, the capacity of SEDFs to fit experimental data and the difference in the stress calculation remains unexplored. In this study, seven SEDFs were used to fit the stress-stretch data points of media, fibrous cap, lipid and intraplaque hemorrhage/thrombus obtained from 21 human carotid plaques. Semi-analytic solution, 2D structure-only and 3D fully coupled fluid-structure interaction (FSI) analyses were used to quantify stress using different SEDFs and the related material stability examined. Results show that, except for neo-Hookean, all other six SEDFs fitted the experimental points well, with vessel stress distribution in the circumferential and radial directions being similar. 2D structural-only analysis was successful for all seven SEDFs, but 3D FSI were only possible with neo-Hookean, Demiray and modified Mooney-Rivlin models. Stresses calculated using Demiray and modified Mooney-Rivlin models were nearly identical. Further analyses indicated that the energy contours of one-/two-term Ogden and 5-parameter Mooney-Rivlin models were not strictly convex and the material stability indictors under homogeneous deformations were not always positive. In conclusion, considering the capacity in characterizing material properties and stabilities, Demiray and modified Mooney-Rivlin SEDF appear practical choices for mechanical analyses to predict the critical mechanical conditions within carotid atherosclerotic plaques.This research is supported by BHF PG/11/74/29100, HRUK RG2638/14/16, the NIHR Cambridge Biomedical Research Centre, and National Natural Science Foundation of China (81170291).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jbiomech.2015.09.02