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arXiv 2023年

作者： da Silva, Maria Heloísa Fraga de Jesus, Gleydson Fernandes Cruz, Clebson Grupo de Informação Quântica e Física Estatística Centro de Ciências Exatas e das Tecnologias Universidade Federal do Oeste da Bahia-Campus Reitor Edgard Santos Rua Bertioga 892 Morada Nobre I Bahia Barreiras47810-059 Brazil Latin American Quantum Computing Center High Performance Computing Center SENAI CIMATEC Av. Orlando Gomes 1845 Piatã Bahia Salvador41650-010 Brazil

quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In particular, there is a discrepant lack of trained and skilled workforce to implement new quantum software on the recent quantum hardware. In this scenario, this work presents a didactic implementation of the quantum search algorithm in Atos quantum Assembly Language (AQASM). In addition, we present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers, even those with little scientific computing experience, can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for teaching quantum computing and building, implementing, and simulating quantum hardware. Copyright © 2023, The Authors. All rights reserved.

关键词： quantum noise

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Pseudorandom Density Matrices

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PRX quantum 2025年第2期6卷 020322-020322页

作者： Nikhil Bansal Wai-Keong Mok Kishor Bharti Dax Enshan Koh Tobias Haug Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (ASTAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Republic of Singapore Department of Physical Sciences Indian Institute of Science Education and Research (IISER) Mohali Sector 81 SAS Nagar Mohali Punjab 140306 India Institute for Quantum Information and Matter California Institute of Technology Pasadena California 91125 USA Quantum Innovation Centre (Q.InC) Agency for Science Technology and Research (ASTAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Republic of Singapore Centre for Quantum Engineering Research and Education TCG CREST Sector V Salt Lake Kolkata 700091 India Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Singapore Quantum Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates

Pseudorandom states (PRSs) are state ensembles that cannot be efficiently distinguished from Haar-random states. However, the definition of PRSs has been limited to pure states and lacks robustness against noise. In this work, we introduce pseudorandom density matrices (PRDMs), ensembles of n-qubit states that are computationally indistinguishable from the generalized Hilbert-Schmidt ensemble (GHSE), which is constructed from (n+m)-qubit Haar random states with m qubits traced out. For m=0, PRDMs are equivalent to PRSs, whereas for m=ω(log n), PRDMs are computationally indistinguishable from the maximally mixed state. PRDMs with m=ω(log n) are robust to unital noise channels and separated in terms of security from PRS. PRDMs can disguise valuable quantum resources as trivial states. In particular, we construct pseudoresource state ensembles, which possess near-maximal entanglement, magic and coherence, but are computationally indistinguishable from resource-free states. PRDMs exhibit a pseudoresource gap of Θ(n) vs 0, surpassing previously found gaps. We also render EFI pairs, a fundamental cryptographic primitive, robust to strong mixed unitary noise. Our work has major implications on quantum resource theory. We show that entanglement, magic, and coherence cannot be efficiently tested, and that black-box resource distillation requires a superpolynomial number of copies. We also establish lower bounds on the purity needed for efficient testing and black-box distillation. Finally, we introduce memoryless PRSs, a noise-robust notion of PRS, which are indistinguishable to Haar random states for efficient algorithms without quantum memory, as well as noise-robust quantum money. Our work provides a comprehensive framework of pseudorandomness for mixed states, which yields powerful quantum cryptographic primitives and fundamental bounds on quantum resource theories.

关键词： quantum entanglement

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quantum Machine Learning for Credit Scoring

arXiv

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arXiv 2023年

作者： Schetakis, Nikolaos Aghamalyan, Davit Boguslavsky, Michael Rees, Agnieszka Raktomalala, Marc Griffin, Paul Alma-Sistemi Srl Guidonia00012 Italy Quantum Innovation Pc Chania73100 Greece School of Computing and Information Systems Singapore Management University 81 Victoria street Singapore188065 Singapore Materials Science and Chemistry Institute of High Performance Computing 1 Fusionpolis Way 16-16 Connecis Singapore138632 Singapore Tradeteq Ltd London United Kingdom Sim Kee Boon Institute for Financial Economics Singapore Management University 50 Stamford Road Singapore178899 Singapore

In this paper we explore the use of quantum machine learning (QML) applied to credit scoring for small and medium-sized enterprises (SME). A quantum/classical hybrid approach has been used with several models, activation functions, epochs and other parameters. Results are shown from the best model, using two quantum classifiers and a classical neural network, applied to data for companies in Singapore. We observe significantly more efficient training for the quantum models over the classical models with the quantum model trained for 350 epochs compared to 3500 epochs for comparable prediction performance. Surprisingly, a degradation in the accuracy was observed as the number of qubits was increased beyond 12 qubits and also with the addition of extra classifier blocks in the quantum model. Practical issues for executing on simulators and real quantum computers are also explored. Overall, we see great promise in this first in-depth exploration of the use of hybrid QML in credit scoring. © 2023, CC BY.

关键词： Machine learning

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Deep supervised multimodal semantic autoencoder for cross-modal retrieval

Deep supervised multimodal semantic autoencoder for cross-mo...

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作者： Tian, Yu Yang, Wenjing Liu, Qingsong Yang, Qiong Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology ChangshaHunan410073 China Naval Aeronautical University Yantai China

Cross-modal retrieval aims to do flexible retrieval among different modals, whose main issue is how to measure the semantic similarities among multimodal data. Though many existing methods have been proposed to enable cross-modal retrieval, they rarely consider the preservation of content information among multimodal data. In this paper, we present a three-stage cross-modal retrieval method, named MMCA-CMR. To reduce the discrepancy among multimodal data, we first attempt to embed multimodal data into a common representation space. We then combine the feature vectors with the content information into the semantic-aware feature vectors. We finally obtain the feature-aware and content-aware projections via multimodal semantic autoencoders. With semantic deep autoencoders, MMCA-CMR promotes a more reliable cross-modal retrieval by learning feature vectors from different modalities and content information simultaneously. Extensive experiments demonstrate that the proposed method is valid in cross-modal retrieval, which significantly outperforms state-of-the-art on four widely-used benchmark datasets. © 2020 John Wiley & Sons, Ltd.

关键词： Semantics

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Nonequilibrium dynamics of the localization-delocalization transition in the non-Hermitian Aubry-André model

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Physical Review B 2022年第1期106卷 014204-014204页

作者： Liang-Jun Zhai Guang-Yao Huang Shuai Yin Department of Physics Nanjing University Nanjing 210093 China The School of Mathematics and Physics Jiangsu University of Technology Changzhou 213001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China School of Physics Sun Yat-Sen University Guangzhou 510275 China

In this paper, we investigate the driven dynamics of the localization transition in the non-Hermitian Aubry-André model with the periodic boundary condition. Depending on the strength of the quasiperiodic potential λ, this model undergoes a localization-delocalization phase transition. We find that the localization length ξ satisfies ξ∼ɛ−ν with ɛ being the distance from the critical point and ν=1 being a universal critical exponent independent of the non-Hermitian parameter. In addition, from the finite-size scaling of the energy gap between the ground state and the first excited state, we determine the dynamic exponent z as z=2. The critical exponent of the inverse participation ratio for the nth eigenstate is also determined as s=0.1197. By changing ɛ linearly to cross the critical point, we find that the driven dynamics can be described by the Kibble-Zurek scaling (KZS). Moreover, we show that the KZS with the same set of the exponents can be generalized to the localization phase transitions in the excited states.

关键词： Anderson localization Localization Scaling methods

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Stably protected gapless edge states without Wannier obstruction

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Physical Review B 2023年第7期107卷 075126-075126页

作者： Yun-Chung Chen Yu-Ping Lin Ying-Jer Kao Department of Physics and Center for Theoretical Physics National Taiwan University Taipei Taiwan 10607 Republic of China Department of Physics University of Colorado Boulder Colorado 80309 USA Center for Quantum Science and Technology National Taiwan University Taipei 10607 Taiwan Republic of China National Center for High-Performance Computing Hsinchu City 30076 Taiwan Republic of China

Bulk-boundary correspondence serves as an important feature of topological insulators, including Chern insulators and Z2 topological insulators. These topological insulators have spectral flow in the Wilson-loop spectrum, and they encounter Wannier obstruction. Recent studies show that the bulk topological features may not imply the existence of protected gapless boundary states. Here we address the opposite question: Does the existence of stably protected gapless edge states necessarily imply Wannier obstruction or Wilson-loop winding? We provide an example in which stably protected gapless edge states arise without the aforementioned bulk topological features. This trivialized topological insulator belongs to a new class of systems with non-δ-like Wannier functions. Interestingly, the gapless edge states are not protected by crystalline symmetry; instead, the protection originates from mirror antisymmetry, a combination of chiral and mirror symmetries. Although the protected gapless edge states cannot be captured by bulk topological features, they can be characterized by the spectral flow in the entanglement spectrum.

关键词： Edge states Topological insulators Tight-binding model

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Variational Entanglement-Assisted quantum Process Tomography with Arbitrary Ancillary Qubits

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Physical Review Letters 2022年第13期129卷 133601-133601页

作者： Shichuan Xue Yizhi Wang Junwei Zhan Yaxuan Wang Ru Zeng Jiangfang Ding Weixu Shi Yong Liu Yingwen Liu Anqi Huang Guangyao Huang Chunlin Yu Dongyang Wang Xiang Fu Xiaogang Qiang Ping Xu Mingtang Deng Xuejun Yang Junjie Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China China Greatwall Research Institute China Greatwall Technology Group CO. LTD. Shenzhen 518057 China National Innovation Institute of Defense Technology AMS Beijing 100071 China

quantum process tomography is a pivotal technique in fully characterizing quantum dynamics. However, exponential scaling of the Hilbert space with the increasing system size extremely restrains its experimental implementations. Here, we put forward a more efficient, flexible, and error-mitigated method: variational entanglement-assisted quantum process tomography with arbitrary ancillary qubits. Numerically, we simulate up to eight-qubit quantum processes and show that this tomography with m ancillary qubits (0≤m≤n) alleviates the exponential costs on state preparation (from 4n to 2n−m), measurement settings (at least a 1 order of magnitude reduction), and data postprocessing (efficient and robust parameter optimization). Experimentally, we first demonstrate our method on a silicon photonic chip by rebuilding randomly generated one-qubit and two-qubit unitary quantum processes. Further using the error mitigation method, two-qubit quantum processes can be rebuilt with average gate fidelity enhanced from 92.38% to 95.56%. Our Letter provides an efficient and practical approach to process tomography on the noisy quantum computing platforms.

关键词： Integrated optics quantum entanglement quantum tomography Tomography Variational approach

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An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks

arXiv

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arXiv 2023年

作者： Tao, Ye Liu, Wanwei Song, Fu Liang, Zhen Wang, Ji Zhu, Hongxu College of Computer Science and Technology National University of Defense Technology China School of Information Science and Technology ShanghaiTech University China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Automotive Software Innovation Center China Institute for Quantum Information State Key Laboratory for High Performance Computing National University of Defense Technology China

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training. Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hongzhou Wu Yifan Lyu Xingyu Shen Xuechen Zhao Mengzhu Wang Xiang Zhang Zhigang Luo Parallel and Distributed Processing Lab National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Computer National University of Defense Technology Changsha China Institute for Quantum Information and the State Key Lab of High Performance Computing National University of Defense Technology Changsha China

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

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SubDLe: identification of substructures in cosmological simulations with deep learning

arXiv

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arXiv 2024年

作者： Esposito, Michela Borgani, Stefano Murante, Giuseppe Department of Physics Astronomy Section University of Trieste via G. Tiepolo 11 TriesteI-34131 Italy INAF Osservatorio Astronomico di Trieste via G. Tiepolo 11 TriesteI-34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 TriesteI-34014 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC Italian Research Center on High Performance Computing Big Data and Quantum Computing via Magnanelli 2 Casalecchio di Reno40033 Italy

Context. The identification of substructures within halos in cosmological hydrodynamical simulations is a fundamental step to identify the simulated counterparts of real objects, namely galaxies. For this reason, substructure finders play a crucial role in extracting relevant information from the simulation outputs. In general, they are based on physically-motivated definitions of substructures, performing multiple steps of particle-by-particle operations, and for this reason they are computationally expensive. Aims. The purpose of this work is to develop a fast algorithm to identify substructures, especially galaxies, in simulations. The final aim, besides a faster production of subhalo catalogues, is to provide an algorithm fast enough to be applied with a fine time-cadence during the evolution of the simulations. Having access to galaxy catalogues while the simulation is evolving is necessary indeed for sub-resolution models based on global properties of galaxies. Methods. In this context, Machine Learning methods offer a wide range of automated tools for fast analysis of large data sets. So, we chose to apply the architecture of a well known Fully Convolutional Network, the U-Net, to the identification of substructures within the mass density field of the simulation. We have developed SubDLe (Substructure identification with Deep Learning), an algorithm which combines a 3D generalization of U-Net and a Friends-of-Friends algorithm, and trained it to reproduce the identification of substructures performed by the SubFind algorithm in a set of zoom-in cosmological hydrodynamical simulations of galaxy clusters. For the feasibility study presented in this work, we have trained and tested SubDLe on galaxy clusters at z = 0, using a NVIDIA P100 GPU. We focused our tests on the version of the algorithm working on the identification of purely stellar substructures, stellar SubDLe. Results. Our stellar SubDLe proved very efficient in identifying most of the galaxies, 82 p

关键词： Numerical methods

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