90 research outputs found
Quantitative Robustness Analysis of Quantum Programs (Extended Version)
Quantum computation is a topic of significant recent interest, with practical
advances coming from both research and industry. A major challenge in quantum
programming is dealing with errors (quantum noise) during execution. Because
quantum resources (e.g., qubits) are scarce, classical error correction
techniques applied at the level of the architecture are currently
cost-prohibitive. But while this reality means that quantum programs are almost
certain to have errors, there as yet exists no principled means to reason about
erroneous behavior. This paper attempts to fill this gap by developing a
semantics for erroneous quantum while-programs, as well as a logic for
reasoning about them. This logic permits proving a property we have identified,
called -robustness, which characterizes possible "distance" between
an ideal program and an erroneous one. We have proved the logic sound, and
showed its utility on several case studies, notably: (1) analyzing the
robustness of noisy versions of the quantum Bernoulli factory (QBF) and quantum
walk (QW); (2) demonstrating the (in)effectiveness of different error
correction schemes on single-qubit errors; and (3) analyzing the robustness of
a fault-tolerant version of QBF.Comment: 34 pages, LaTeX; v2: fixed typo
On the stability of impulsive functional differential equations with infinite delays
In this paper, the stability problem of impulsive functional differential equations with infinite delays is considered. By using Lyapunov functions and the Razumikhin technique, some new theorems on the uniform stability and uniform asymptotic stability are obtained. The obtained results are milder and more general than several recent works. Two examples are given to demonstrate the advantages of the results
On exploiting social relationship and personal background for content discovery in P2P networks
International audienceContent discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384, 494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features-Knowledge and Similarity-and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node's friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods
On exploiting social relationship and personal background for content discovery in P2P networks
Content discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384,494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features – Knowledge and Similarity – and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node’s friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods.This work has been funded by the European Union under the project eCOUSIN (EU-FP7-318398) and the project SITAC (ITEA2-11020). It also has been partially funded by the Spanish Government through the MINEC eeCONTENT project (TEC2011-29688-C02-02)
Quantum algorithm for estimating volumes of convex bodies
Estimating the volume of a convex body is a central problem in convex
geometry and can be viewed as a continuous version of counting. We present a
quantum algorithm that estimates the volume of an -dimensional convex body
within multiplicative error using
queries to a membership oracle and
additional arithmetic operations. For
comparison, the best known classical algorithm uses
queries and
additional arithmetic operations. To the
best of our knowledge, this is the first quantum speedup for volume estimation.
Our algorithm is based on a refined framework for speeding up simulated
annealing algorithms that might be of independent interest. This framework
applies in the setting of "Chebyshev cooling", where the solution is expressed
as a telescoping product of ratios, each having bounded variance. We develop
several novel techniques when implementing our framework, including a theory of
continuous-space quantum walks with rigorous bounds on discretization error. To
complement our quantum algorithms, we also prove that volume estimation
requires quantum membership queries, which rules
out the possibility of exponential quantum speedup in and shows optimality
of our algorithm in up to poly-logarithmic factors.Comment: 61 pages, 8 figures. v2: Quantum query complexity improved to
and number of additional arithmetic
operations improved to . v3: Improved
Section 4.3.3 on nondestructive mean estimation and Section 6 on quantum
lower bounds; various minor change
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
We present a strong object detector with encoder-decoder pretraining and
finetuning. Our method, called Group DETR v2, is built upon a vision
transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant
DINO~\cite{zhang2022dino}, and an efficient DETR training method Group
DETR~\cite{chen2022group}. The training process consists of self-supervised
pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the
detector on Object365, and finally finetuning it on COCO. Group DETR v2
achieves mAP on COCO test-dev, and establishes a new SoTA on
the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-cocoComment: Tech report, 3 pages. We establishes a new SoTA (64.5 mAP) on the
COCO test-de
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