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Tunable Source of Quantum-Correlated Photons with Integrated Pump Rejection in a Silicon CMOS Platform

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Tunable Source of Quantum-Correlated Photons with Integrated...

Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Fargas Cabanillas, Josep M. Kramnik, Danielius Ramesh, Anirudh Gentry, Cale M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

ISBN: (纸本)9781557528209

A wavelength-tunable, silicon photon-pair source based on spontaneous four-wave mixing, integrated with a pump rejection filter in a single, flip-chip packaged CMOS chip, is demonstrated with a coincidence-to-accidentals ratio of 9.1 with no off-chip pump filtering. Frontiers in Optics / Laser science © Optica Publishing Group 2021.

关键词： CMOS integrated circuits

All-optical measurement-device-free feedforward enabling ultra-fast quantum informationprocessing

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arXiv 2024年

作者： Yamashima, Taichi Kashiwazaki, Takahiro Suzuki, Takumi Nehra, Rajveer Nakamura, Tomohiro Inoue, Asuka Umeki, Takeshi Takase, Kan Asavanant, Warit Endo, Mamoru Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Atsugi Kanagawa243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Wako Saitama351-0198 Japan

Utilizing feedforward to perform adaptive quantum operations on one entangled state according to the measurement result of the other state enables measurement-based quantum information processing (QIP). Until now, the bandwidth of feedforward in optical QIP has been limited to around 100 MHz by measurement with electronics. A potential alternative is the utilization of an optical parametric amplifier (OPA). This optical device eliminates the need for electronic measuring devices and enables all-optical broadband feedforward. In this paper, we demonstrate a variable squeezing gate with an operation bandwidth of 1.3 THz by all-optical measurement-device-free feedforward. We utilize a periodically poled lithium niobate waveguide as a broadband OPA and perform continuous phase locking in our optical system. Experimental results demonstrate that our all-optical QIP operates at a THz clock frequency, representing a major step toward the realization of an ultra-fast quantum computer. © 2024, CC BY.

关键词： Parametric amplifiers

Cauchy-schwarz regularized autoencoder

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arXiv 2021年

作者： Tran, Linh Pantic, Maja Deisenroth, Marc Peter The Department of Computing Imperial College London United Kingdom The Department of Computing Imperial College London United Kingdom Facebook AI Applied Research United Kingdom The Department of Computer Science University College London United Kingdom

Recent work in unsupervised learning has focused on efficient inference and learning in latent variables models. Training these models by maximizing the evidence (marginal likelihood) is typically intractable. Thus, a common approximation is to maximize the Evidence Lower BOund (ELBO) instead. Variational autoencoders (VAE) are a powerful and widely-used class of generative models that optimize the ELBO efficiently for large datasets. However, the VAE’s default Gaussian choice for the prior imposes a strong constraint on its ability to represent the true posterior, thereby degrading overall performance. A Gaussian mixture model (GMM) would be a richer prior, but cannot be handled efficiently within the VAE framework because of the intractibility of the Kullback-Leibler divergence for GMMs. We deviate from the common VAE framework in favor of one with an analytical solution for Gaussian mixture prior. To perform efficient inference for GMM priors, we introduce a new constrained objective based on the Cauchy-Schwarz divergence, which can be computed analytically for GMMs. This new objective allows us to incorporate richer, multi-modal priors into the autoencoding framework. We provide empirical studies on a range of datasets and show that our objective improves upon variational auto-encoding models in density estimation, unsupervised clustering, semi-supervised learning, and face analysis. © 2021, CC BY-NC-SA.

关键词： Constrained optimization

Multi-class granular approximation by means of disjoint and adjacent fuzzy granules

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arXiv 2022年

作者： Palangetić, Marko Cornelis, Chris Greco, Salvatore Slowiński, Roman Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Economics and Business University of Catania Catania Italy University of Portsmouth Portsmouth United Kingdom Institute of Computing Science Poznań University of Technology Poznań Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland

In granular computing, fuzzy sets can be approximated by granularly representable sets that are as close as possible to the original fuzzy set w.r.t. a given closeness measure. Such sets are called granular approximations. In this article, we introduce the concepts of disjoint and adjacent granules and we examine how the new definitions affect the granular approximations. First, we show that the new concepts are important for binary classification problems since they help to keep decision regions separated (disjoint granules) and at the same time to cover as much as possible of the attribute space (adjacent granules). Later, we consider granular approximations for multi-class classification problems leading to the definition of a multi-class granular approximation. Finally, we show how to efficiently calculate multi-class granular approximations for Lukasiewicz fuzzy connectives. We also provide graphical illustrations for a better understanding of the introduced concepts. Copyright © 2022, The Authors. All rights reserved.

关键词： Fuzzy sets

Fuzzy granular approximation classifier

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arXiv 2022年

作者： Palangetić, Marko Cornelis, Chris Greco, Salvatore Slowinski, Roman Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Economics and Business University of Catania Catania Italy University of Portsmouth Portsmouth United Kingdom Institute of Computing Science Poznan University of Technology Poznan Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland

In this article, a new Fuzzy Granular Approximation Classifier (FGAC) is introduced. The classifier is based on the previously introduced concept of the granular approximation and its multi-class classification case. The classifier is instance-based and its biggest advantage is its local transparency i.e., the ability to explain every individual prediction it makes. We first develop the FGAC for the binary classification case and the multi-class classification case and we discuss its variation that includes the Ordered Weighted Average (OWA) operators. Those variations of the FGAC are then empirically compared with other locally transparent ML methods. At the end, we discuss the transparency of the FGAC and its advantage over other locally transparent methods. We conclude that while the FGAC has similar predictive performance to other locally transparent ML models, its transparency can be superior in certain cases. Copyright © 2022, The Authors. All rights reserved.

关键词： Classifiers

The minimality of the Georges-Kelmans graph

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arXiv 2021年

作者： Brinkmann, Gunnar Goedgebeur, Jan McKay, Brendan D. Department of Applied Mathematics Computer Science & Statistics Ghent University Ghent9000 Belgium Computer Science Department University of Mons Mons7000 Belgium Department of Computer Science KU Leuven Campus Kulak Kortrijk8500 Belgium Department of Computing Australian National University CanberraACT2601 Australia

In 1971, Tutte wrote in an article that it is tempting to conjecture that every 3-connected bipartite cubic graph is hamiltonian. Motivated by this remark, Horton constructed a counterexample on 96 vertices. In a sequence of articles by different authors several smaller counterexamples were presented. The smallest of these graphs is a graph on 50 vertices which was discovered independently by Georges and Kelmans. In this article we show that there is no smaller counterexample. As all non-hamiltonian 3-connected bipartite cubic graphs in the literature have cyclic 4-cuts-even if they have girth 6-it is natural to ask whether this is a necessary prerequisite. In this article we answer this question in the negative and give a construction of an infinite family of non-hamiltonian cyclically 5-connected bipartite cubic graphs. In 1969 Barnette gave a weaker version of the conjecture stating that 3-connected planar bipartite cubic graphs are hamiltonian. We show that Barnette's conjecture is true up to at least 90 vertices. We also report that a search of small non-hamiltonian 3-connected bipartite cubic graphs did not find any with genus less than 4. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamiltonians

Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry

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arXiv 2022年

作者： Sbahi, Faris M. Martinez, Antonio J. Patel, Sahil Saberi, Dmitri Yoo, Jae Hyeon Roeder, Geoffrey Verdon, Guillaume X Mountain View Mountain ViewCA United States Google Brain Mountain ViewCA United States Department of Computer Science UC Berkeley Berkeley United States Department of Mathematics Stanford University Stanford United States Institute for Quantum Computing University of Waterloo ON Canada Department of Applied Mathematics University of Waterloo ON Canada Department of Computer Science Princeton University PrincetonNJ United States

The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are relevant to many important problems in physics and chemistry. In the low temperature regime, algorithms for these tasks often suffer from intractabilities, for example from poor sample- or time-complexity. With the aim of addressing such intractabilities, we introduce a generalization of quantum natural gradient descent to parameterized mixed states, as well as provide a robust first-order approximating algorithm, Quantum-Probabilistic Mirror Descent. We prove data sample efficiency for the dual tasks using tools from information geometry and quantum metrology, thus generalizing the seminal result of classical Fisher efficiency to a variational quantum algorithm for the first time. Our approaches extend previously sample-efficient techniques to allow for flexibility in model choice, including to spectrally-decomposed models like Quantum Hamiltonian-Based Models, which may circumvent intractable time complexities. Our first-order algorithm is derived using a novel quantum generalization of the classical mirror descent duality. Both results require a special choice of metric, namely, the Bogoliubov-Kubo-Mori metric. To test our proposed algorithms numerically, we compare their performance to existing baselines on the task of quantum Gibbs sampling for the transverse field Ising model. Finally, we propose an initialization strategy leveraging geometric locality for the modelling of sequences of states such as those arising from quantum-stochastic processes. We demonstrate its effectiveness empirically for both real and imaginary time evolution while defining a broader class of potential applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Mirrors

Robusta: Robust AutoML for Feature Selection via Reinforcement Learning

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arXiv 2021年

作者： Wang, Xiaoyang Li, Bo Zhang, Yibo Kailkhura, Bhavya Nahrstedt, Klara Department of Computer Science University of Illinois at Urbana-Champaign United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Several AutoML approaches have been proposed to automate the machine learning (ML) process, such as searching for the ML model architectures and hyper-parameters. However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial attacks. As ML systems are increasingly being used in a variety of mission-critical applications, improving the robustness of ML systems has become of utmost importance. In this paper, we propose the first robust AutoML framework, Robusta–based on reinforcement learning (RL)–to perform feature selection, aiming to select features that lead to both accurate and robust ML systems. We show that a variation of the 0-1 robust loss can be directly optimized via an RL-based combinatorial search in the feature selection scenario. In addition, we employ heuristics to accelerate the search procedure based on feature scoring metrics, which are mutual information scores, tree-based classifiers feature importance scores, F scores, and Integrated Gradient (IG) scores, as well as their combinations. We conduct extensive experiments and show that the proposed framework is able to improve the model robustness by up to 22% while maintaining competitive accuracy on benign samples compared with other feature selection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

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A BCLC Staging System for Hepatocellular Carcinoma using Swi...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging