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Compact data encoding for data re-uploading quantum classifier

QUANTUM INFORMATION PROCESSING 2022年第3期21卷 1-14页

作者： Fan, Liangliang Situ, Haozhen South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China

In the realm of quantum machine learning, different genres of quantum classifiers have been designed to classify classical data. Recently, a quantum classifier that features re-uploading the sample to be classified many times along the quantum circuit was proposed. data re-uploading allows circumventing the limitations established by the no-cloning theorem. This quantum classifier has great potential in NISQ-era, because it requires very few qubits due to the special data encoding scheme it used. Previous work showed that even a single-qubit could constitute effective classifiers for problems with up to 4 dimensions. In this work, we focus our attention on the data encoding scheme of this quantum classifier, we propose an alternative way to encode the input sample in order to reduce by half the number of learnable parameters of the quantum circuit and simplify the computation, so the training time can be greatly shortened. Numerical results show that the new data encoding method achieves higher accuracy for high-dimensional data while using less parameters.

关键词： Quantum machine learning Classification data encoding

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The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2023年第5期38卷 362-400页

作者： Kashif, Muhammad Al-Kuwari, Saif Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Div Informat & Comp Technol Doha Qatar

Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs.

关键词： Quantum machine learning entanglement data encoding quantum neural networks trainability >

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Effective reversible data hiding scheme for interpolated images using an improved data encoding strategy

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SOFT COMPUTING 2024年第4期28卷 3491-3508页

作者： Xiong, Xiangguang Li, Zhi Chen, Yi Fan, Mengting Guizhou Normal Univ Sch Big Data & Comp Sci Guiyang 550025 Peoples R China Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Peoples R China

In 2021, Lu et al. proposed a reversible data hiding (RDH) scheme for interpolated images based on a multilayer centerfolding strategy with a high embedding rate and image quality. However, their data encoding method and the chosen block size are not optimal, leaving the watermarked image quality and the embedding rate to be improved. To further reduce the distortion caused by embedded data, we propose an improved RDH scheme for interpolated images using an improved data encoding method. First, an improved two-layer data encoding method is proposed, which encodes the original data R by comparing the amplitude of R and its complement and the center folding strategy to form a new encoding pair (R, F). Thereafter, the neighbor mean interpolation method proposed by Jung and Yoo is used to generate an interpolated image divided into mxm non-overlapping blocks. Additionally, the interpolated pixels in each block are pre-processed to solve the overflow/underflow problem. Finally, the encoded data R and F are embedded into the pre-processed interpolated pixels to achieve blind extraction. We theoretically prove that the image quality obtained with a block size of 2 x 2 is superior to that of many other blocks. The results of extensive experiments on the BossBase, BOWS-2, NCID, and SIPI databases confirm that the proposed scheme outperforms several state-of-the-art schemes, with the average PSNR value improved by similar to 7.58 dB when 3-bit data are embedded into each interpolated pixel. These results indicate that our proposed scheme is effective.

关键词： Reversible data hiding Interpolation technology data encoding

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data Checking and Econometric Software Development: A Technique of Traceability by Fictive data encoding

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COMPUTATIONAL ECONOMICS 2015年第2期46卷 325-357页

作者： Buda, Rodolphe Univ Western Paris Nanterre La Def GAMA Team F-92000 Nanterre France

In this paper, we have exposed the data checking problem in the context of the econometric software development. We have based our presentation on the development of our SIMUL multidimensional econometric software. We have shown that this problem is particularly important when one develops multi-dimensional econometric software. Then, we have briefly recalled the principle of the main arithmetical and managerial techniques available to deal with this problem. Finally, we have presented the data checking procedures we have conceived based on a fictive data encoding technique.

关键词： data bank data location checking data encoding Econometric software

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Secure Textual data Deduplication Scheme Based on data encoding and Compression 10

Secure Textual Data Deduplication Scheme Based on Data Encod...

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IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

作者： Miri, Ali Rashid, Fatema Ryerson Univ Comp Sci Toronto ON Canada

ISBN: (纸本)9781728125305

As the need for storage has grown exponentially in recent years, cloud storage has been providing a solution to this need by providing users expanded capacity and access. Providing adequate security and privacy, and lowering storage costs are some of the key challenges facing this solution. A common practice used by cloud service providers (CSPs)-data deduplication - identifies identical copies of users' data, and removing all, but one copy to lower required storage overhead. However, this can result in serious privacy concerns. In this paper, we formulate a new secure deduplication scheme for textual data. Our proposed method uses data encoding and compression techniques that not only result in reduce storage space required, but also in saving in required transmission bandwidth. The security of the data against the semi-honest CSP and malicious users is ensured by using Burrows Wheel Transform encoding scheme. The encoded data is further compressed to gain effective savings in terms of storage and reduced size of the data. data encoding and data compression techniques are combined together to realize secure and efficient data deduplication. Through our scheme, the CSP will not only achieve huge storage space savings through data compression and data deduplication, but can also provide the users a satisfactory level of security for their data in the cloud.

关键词： Burrows Wheel Transform encoding data Compression data Deduplication Client-Cide Deduplication Cross User Deduplication Semi-Honest CSP Big data data encoding Bzip2 Reversible encoding

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Quantum Circuit Complexity of Genomic data encoding 5

Quantum Circuit Complexity of Genomic Data Encoding

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2024 International Conference on Quantum Computing and Engineering

作者： Ye, Orson Strelchuk, Sergii Univ Cambridge Dept Appl Math & Theoret Phys Wilberforce Rd Cambridge CB3 0WA England

ISBN: (纸本)9798331541378

data encoding is a key step in almost all applications of quantum computation, but one that is not very well understood in general. Extensively researched in quantum machine learning, it is pivotal to exploiting the speedups offered by quantum algorithms. Failure to efficiently represent data risks weakening or nullifying prospective quantum advantages. While naive methods, such as basis encoding, will almost always work, they are extremely inefficient in terms of qubit use, rendering them infeasible for near-term applications. We focus on data encoding for genomics, a deeply data-driven field with computationally intensive algorithms throughout, from the initial DNA sequencing to pangenome assembly and more. We first examine the compressibility of genomic data from an information-theoretic perspective and how compression affects the quantum state preparation complexity. Here, there are two opposing forces at work: compression reduces data size but at the cost of the beneficial structure within the data. We review some standard approaches to data encoding in the context of genomics data from considering both the preparation complexity in terms of gate counts, qubit use, and depth, as well as the usefulness of the resulting states. We also present some new data encoding methods designed specifically for genomics that have a low-depth preparation circuits while still maintaining the essential features the algorithms require.

关键词： Quantum Applications Quantum Circuit Complexity data encoding Genomics State Preparation Quantum Information

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Expanding data encoding Patterns For Quantum Algorithms 18

Expanding Data Encoding Patterns For Quantum Algorithms

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18th IEEE International Conference on Software Architecture (ICSA)

作者： Weigold, Manuela Barzen, Johanna Leymann, Frank Salm, Marie Univ Stuttgart Univ Str 38 Stuttgart Germany

ISBN: (纸本)9781665439107

As quantum computers are based on the laws of quantum mechanics, they are capable of solving certain problems faster than their classical counterparts. However, often algorithms which a theoretical speed-up assume that data can be loaded efficiently. In general, the runtime complexity of the loading routine depends on (i) the data encoding that defines how the data is represented and (ii) the data itself. In some eases, loading the data requires at least exponential tune which destroys a potential speed-up. And especially for the first generation of devices that are currently available, the resources (qubits and operations) needed to encode the data are limited. Therefore, understanding the consequences of a particular data encoding is crucial. To capture knowledge about different encodings, we present two data encoding patterns that extend our previous collection of encoding patterns [1].

关键词： Quantum Computing data encoding Patterns Pattern Primitives

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Efficient LiDAR point cloud data encoding for scalable data management within the Hadoop eco-system

Efficient LiDAR point cloud data encoding for scalable data ...

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IEEE International Conference on Big data (Big data)

作者： Vo, Anh Vu Hewage, Chamin Nalinda Lokugam Russo, Gianmarco Chauhan, Neel Laefer, Debra F. Bertolotto, Michela Le-Khac, Nhien-An Oftendinger, Ulrich Univ Coll Dublin Sch Comp Sci Dublin Ireland Univ Salerno Dept Comp Sci Fisciano Italy NYU Ctr Urban Sci Progress New York NY USA Queens Univ Belfast Sch Nat & Built Environm Belfast Antrim North Ireland

ISBN: (纸本)9781728108582

This paper introduces a novel LiDAR point cloud data encoding solution that is compact, flexible, and fully supports distributed data storage within the Hadoop distributed computing environment. The proposed data encoding solution is developed based on Sequence File and Google Protocol Buffers. Sequence File is a generic splittable binary file format built in the Hadoop framework for storage of arbitrary binary data. The key challenge in adopting the Sequence File format for LiDAR data is in the strategy for effectively encoding the LiDAR data as binary sequences in a way that the data can be represented compactly, while allowing necessary mutation. For that purpose, a data encoding solution, based on Google Protocol Buffers (a language-neutral, cross-platform, extensible data serialisation framework) was developed and evaluated. Since neither of the underlying technologies is sufficient to completely and efficiently represent all necessary point formats for distributed computing, an innovative fusion of them was required to provide a viable data storage solution. This paper presents the details of such a data encoding implementation and rigorously evaluates the efficiency of the proposed data encoding solution. Benchmarking was done against a straightforward, naive text encoding implementation using a high-density aerial LiDAR scan of a portion of Dublin, Ireland. The results demonstrated a 6-times reduction in data volume, a 4-times reduction in database ingestion time, and up to a 5 times reduction in querying time.

关键词： LiDAR point cloud Big data Hadoop HBase Google Protocol Buffers spatial database data encoding distributed atabase distributed computing

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Understanding the effects of data encoding on quantum-classical convolutional neural networks 5

Understanding the effects of data encoding on quantum-classi...

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2024 International Conference on Quantum Computing and Engineering

作者： Monnet, Maureen Chaabani, Nermine Dragan, Theodora-Augustina Schachtner, Balthasar Lorenz, Jeanette Miriam Fraunhofer Inst Cognit Syst IKS Munich Germany LMU Univ Hosp Munich Germany

ISBN: (纸本)9798331541378

Quantum machine learning was recently applied to various applications and leads to results that are comparable or, in certain instances, superior to classical methods, in particular when few training data is available. These results warrant a more in-depth examination of when and why improvements can be observed. A key component of quantum-enhanced methods is the data encoding strategy used to embed the classical data into quantum states. However, a clear consensus on the selection of a fitting encoding strategy given a specific use-case has not yet been reached. This work investigates how the data encoding impacts the performance of a quantum-classical convolutional neural network (QCCNN) on two medical imaging datasets. In the pursuit of understanding why one encoding method outperforms another, two directions are explored. Potential correlations between the performance of the quantum-classical architecture and various quantum metrics are first examined. Next, the Fourier series decomposition of the quantum circuits is analyzed, as variational quantum circuits generate Fourier-type sums. We find that while quantum metrics offer limited insights into this problem, the Fourier coefficients analysis appears to provide better clues to understand the effects of data encoding on QCCNNs.

关键词： quantum machine learning data encoding quantum convolutional neural networks medical imaging

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data encoding Technique for Crosstalk Delay Reduction on Fault Tolerant data-Bus in DSM Technology

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Procedia Engineering 2012年 38卷 2967-2972页

作者： A. Sathish M.Madhavi Latha K.Lal kishor Department of ECE RGMCET University of J.N.T Hyderabad Andhra Pradesh51850 India Department of ECE University of J.N.T Hyderabad Andhra Pradesh518501 India

Propagation delay through Fault tolerant data buses, data buses and interconnects limits the performance and reliability of a highly integrated circuits. This delay is due to crosstalk occurring on the buses or interconnects. This crosstalk is due to switching activity occurring on the Fault tolerant and interconnects when data is transmitted. Hence reducing the crosstalk delay is one of the main design challenges in DSM and VDSM technology. Reduction of crosstalk delay increases the reliability and performance of the system. One of the favourable techniques to reduce the crosstalk delay on fault tolerant data bus is to reduce the switching activity. data encoding technique is the promising method to reduce the switching activity on the fault tolerant data bus. Hence an efficient Crosstalk delay reduction fault tolerant data bus encoding technique is proposed which can reduce the total crosstalk delay by around 36%, to 40% compared to unencoded data and 0.3% to 32% compared to others techniques for 12-bit, 21-bit, 38-bit and 71-bit data buses.

关键词： Crosstalk delay switching activity interconnects reliability Fault tolerant data bus data encoding

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