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Topic Review
Quantum Stream Cipher
Quantum cryptography includes quantum key distribution (QKD) and quantum stream cipher, but the researchers point out that the latter is expected as the core technology of next-generation communication systems. Various ideas have been proposed for QKD quantum cryptography, but most of them use a single-photon or similar signal. Then, although such technologies are applicable to special situations, these methods still have several difficulties to provide functions that surpass conventional technologies for social systems in the real environment. Thus, the quantum stream cipher has come to be expected as one promising countermeasure, which artificially creates quantum properties using special modulation techniques based on the macroscopic coherent state. In addition, it has the possibility to provide superior security performance than one-time pad cipher.
  • 903
  • 25 May 2022
Topic Review
Quantum Natural Language Processing
Quantum Natural Language Processing (QNLP) is a hybrid field that combines aspects derived from Quantum Computing (QC) with tasks of Natural Language Processing (NLP).
  • 718
  • 14 Jun 2022
Topic Review
Quantum Machine Learning for Security Assessment in IoMT
Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways.
  • 322
  • 13 Oct 2023
Topic Review
Quantum Machine Learning
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed.
  • 373
  • 01 Jun 2023
Topic Review
Quantum Generative Adversarial Networks
Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate-scale quantum era, quantum mechanics finds its use in various fields of life. Following this trend, researchers seek to augment machine learning in a quantum way. The generative adversarial network (GAN), an important machine learning invention that excellently solves generative tasks, has also been extended with quantum versions. Since the first publication of a quantum GAN (QuGAN) in 2018, many QuGAN proposals have been suggested. A QuGAN may have a fully quantum or a hybrid quantum–classical architecture, which may need additional data processing in the quantum–classical interface. Similarly to classical GANs, QuGANs are trained using a loss function in the form of max likelihood, Wasserstein distance, or total variation. The gradients of the loss function can be calculated by applying the parameter-shift method or a linear combination of unitaries in order to update the parameters of the networks. 
  • 694
  • 15 Mar 2023
Topic Review
Quantum Finite Automata
In quantum computing, quantum finite automata (QFA) or quantum state machines are a quantum analog of probabilistic automata or a Markov decision process. They provide a mathematical abstraction of real-world quantum computers. Several types of automata may be defined, including measure-once and measure-many automata. Quantum finite automata can also be understood as the quantization of subshifts of finite type, or as a quantization of Markov chains. QFAs are, in turn, special cases of geometric finite automata or topological finite automata. The automata work by receiving a finite-length string [math]\displaystyle{ \sigma=(\sigma_0,\sigma_1,\cdots,\sigma_k) }[/math] of letters [math]\displaystyle{ \sigma_i }[/math] from a finite alphabet [math]\displaystyle{ \Sigma }[/math], and assigning to each such string a probability [math]\displaystyle{ \operatorname{Pr}(\sigma) }[/math] indicating the probability of the automaton being in an accept state; that is, indicating whether the automaton accepted or rejected the string. The languages accepted by QFAs are not the regular languages of deterministic finite automata, nor are they the stochastic languages of probabilistic finite automata. Study of these quantum languages remains an active area of research.
  • 238
  • 20 Oct 2022
Topic Review
Quantum Computing Supremacy in the Internet of Things
The Internet of Things (IoT) strongly influences the world economy; this emphasizes the importance of securing all four aspects of the IoT model: sensors, networks, cloud, and applications. Considering the significant value of public-key cryptography threats on IoT system confidentiality, it is vital to secure it. One of the potential candidates to assist in securing public key cryptography in IoT is quantum computing. Although the notion of IoT and quantum computing convergence is not new, it has been referenced in various works of literature and covered by many scholars. Quantum computing eliminates most of the challenges in IoT.
  • 1.4K
  • 16 Nov 2022
Topic Review
Quantum Computer Hardware
Quantum computers are not only faster than conventional classical computers, but they also have a different framework for solving problems due to the laws of quantum mechanics such as superposition and entanglement.
  • 123
  • 22 Sep 2023
Topic Review
Quantum AI Integration for Industry Transformation
The fusion of quantum computing and artificial intelligence (AI) heralds a transformative era for Industry 4.0, offering unprecedented capabilities and challenges
  • 555
  • 11 Jun 2024
Topic Review
Quantization Methods of  Defense against Membership Inference Attacks
Machine learning deployment on edge devices has faced challenges such as computational costs and privacy issues. Membership inference attack (MIA) refers to the attack where the adversary aims to infer whether a data sample belongs to the training set. In other words, user data privacy might be compromised by MIA from a well-trained model. Therefore, it is vital to have defense mechanisms in place to protect training data, especially in privacy-sensitive applications such as healthcare. 
  • 221
  • 19 Sep 2023
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