Quantum Computers: A Review on How Quantum Computing Can Boom AI

Chauhan, Vibhor; Negi, Sunder; Jain, Dhyanendra; Singh, Prashant; Sagar, Anil Kumar; Sharma, Anupam

doi:10.1109/icacite53722.2022.9823619

Cited by 13 publications

(3 citation statements)

References 5 publications

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“…When all nodes have the same initial state probability, they evolve simultaneously over the entire network, and REQF simulates the global evolution by the probability distribution matrix M t of quantum walk to represent the global role relevance of nodes and generate the global role representation z global . The formulated Grover operator [22] is shown in Eq. ( 5).…”

Section: Global Role Informationmentioning

confidence: 99%

Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

Zhou,

Li,

Qian

et al. 2023

Computers, Materials &Amp; Continua

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Role-based network embedding aims to embed role-similar nodes into a similar embedding space, which is widely used in graph mining tasks such as role classification and detection. Roles are sets of nodes in graph networks with similar structural patterns and functions. However, the rolesimilar nodes may be far away or even disconnected from each other. Meanwhile, the neighborhood node features and noise also affect the result of the role-based network embedding, which are also challenges of current network embedding work. In this paper, we propose a Role-based network Embedding via Quantum walk with weighted Features fusion (REQF), which simultaneously considers the influence of global and local role information, node features, and noise. Firstly, we capture the global role information of nodes via quantum walk based on its superposition property which emphasizes the local role information via biased quantum walk. Secondly, we utilize the quantum walk weighted characteristic function to extract and fuse features of nodes and their neighborhood by different distributions which contain role information implicitly. Finally, we leverage the Variational Auto-Encoder (VAE) to reduce the effect of noise. We conduct extensive experiments on seven real-world datasets, and the results show that REQF is more effective at capturing role information in the network, which outperforms the best baseline by up to 14.6% in role classification, and 23% in role detection on average.

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Section: Global Role Informationmentioning

confidence: 99%

Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

Zhou,

Li,

Qian

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The term AI was first used in the summer of 1956 at the Dartmouth Conference by John McCarthy, an American computer scientist [84]. The concept of AI is now widely known and has triggered the entry of many researchers into research, alternating between boom and bust periods [85]. Initially, the first AI boom began in the 1950s and lasted until the 1960s, when "inference" and "exploration" were the main research areas, and it became a boom because it became possible to present solutions to specific problems.…”

Section: Technological Application In Bigdata and Deep Learningmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

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“…More quantum circuits of AES were designed to reduce the number of qubits by improving the quantum circuits of S-box or round function [13,14,[16][17][18][19][20][21][22]. Because qubits are very important resource limited by current quantum computers [23]. So far Li et al proposed the method in Ref.…”

Section: Introductionmentioning

confidence: 99%

Quantum implementaion of SM4 block cipher with less qubits

Luo

et al. 2023

Preprint

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SM4 cryptographic algorithm is a block cipher algorithm issued by China's state cryptographic administration and has become an international standard. we implement the quantum circuits of SM4 Block cipher by optimizing the number of qubits and the value of depth-times-width. When the S-box is implemented, four kind of improved quantum circuits of S-box are presented for different phases in SM4 based on composite field arithmetic. When optimizing the number of qubits, we implement the quantum circuit of SM4 by connecting the quantum subcircuits in series. The implemented quantum circuit of SM4 only uses 260 qubits, which is the least number of qubits used not only in implementing the SM4 quantum circuit, but also in implementing the block cipher algorithms with 8-bit S-box, 128-bit plaintext and 128-bit secret key. When optimizing the value of depth-times-width, we achieve it through parallel implementation. The trade-off quantum circuit uses a total of 288 quantum bits and the Toffoli depth is 1716. The depth-times-width is 494208, which is less than the existing best value 825792.

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Quantum Computers: A Review on How Quantum Computing Can Boom AI

Cited by 13 publications

References 5 publications

Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

Applications of Deep Learning for Drug Discovery Systems with BigData

Quantum implementaion of SM4 block cipher with less qubits

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