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The paper is intended to be a survey of all the important aspects and results that have shaped the field of quantum computation and quantum information. The reader is first familiarized with those features and principles of quantum mechanics providing a more efficient and secure information processing. Their applications to the general theory of in...
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... was first conducted by Thomas Young in 1801 and demonstrated that light behaves like waves. In his experiment, Young projected light onto a screen through a barrier pierced with two closely spaced slits (see figure 1). What he observed on the screen was an interference pattern, the hallmark of waves. ...
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... is known is that BPP # BQP (that is, quantum computers can efficiently solve all the problems that are tractable for a PTM) and BQP # PSPACE (there are no problems outside of PSPACE which quantum computers can solve efficiently) [26]. Consequently, from P # BPP # BQP # PSPACE we can see that BQP lies somewhere between P and PSPACE (see figure 10). Thus, we know for sure that BQP contains all of P and BPP, but whether it also contains some problems in PSPACE that are not in NP, for example, remains an open question. ...
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... speed-up. The important change in the attitude of quantum complexity theorists relative to the speed-up gained by quantum computers when dealing with Figure 10. Relationships between quantum and classical complexity classes. ...
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... and Vaidman [78] describe how a Mach -Zehnder interferometer can be used to perform an interaction-free measurement in the dramatic context of detecting an ultrasensitive bomb. The Mach -Zehnder interferometer (depicted in figure 11) is an optical device composed of beam splitters, mirrors and photon detectors carefully placed to bring about quantum interference when a photon travels through the apparatus. Thus, when a photon enters the first beam splitter horizontally, it will always emerge from the horizontal port of the second beam splitter, provided the two arms of the interferometer have equal lengths. ...
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The technologies are rapidly developing, but some of them present in the computers, as for instance their processing capacity, are reaching their physical limits. It is up to quantum computation offer solutions to these limitations and issues that may arise. In the field of information security, encryption is of paramount importance, being then the...
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... [1][2][3] The three principles of quantum physics: parallelism, interference and entanglement, have greatly improved the computing speed of quantum computer compared with classical computers. [4] However, qubits are extremely vulnerable to environmental impact, resulting in bit flipping or phase flipping errors. Due to the decoherence and gate infidelity, near-term quantum computer can only implement shallow quantum circuit depth, which is named as noisy intermediate-scale quantum (NISQ). ...
We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network (HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation (INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST (Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds 98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.
... It involves the use of quantum mechanical properties to enable computational tasks. Quantum computing exploits the unique properties of quantum communication to transmit information securely, preventing unauthorized access [42,45]. The exploration of quantum computing began in the 1980s when researchers examined the quantum mechanical framework of Turing machines [15]. ...
... Nevertheless, arguably the most successful branch of quantum computation is quantum cryptography, with the promise of unconditionally secure communication protocols [6]. Quantum security protocols have been designed for key distribution [7], delayed secure decisions, and zero knowledge protocols. ...
Quantum computation offers unique properties that cannot be paralleled by conventional computers. In particular, reading qubits may change their state and thus signal the presence of an intruder. This paper develops a proof-of-concept for a quantum honeypot that allows the detection of intruders on reading. The idea is to place quantum sentinels within all resources offered within the honeypot. Additional to classical honeypots, honeypots with quantum sentinels can trace the reading activity of the intruder within any resource. Sentinels can be set to be either visible and accessible to the intruder or hidden and unknown to intruders. Catching the intruder using quantum sentinels has a low theoretical probability per sentinel, but the probability can be increased arbitrarily higher by adding more sentinels. The main contributions of this paper are that the monitoring of the intruder can be carried out at the level of the information unit, such as the bit, and quantum monitoring activity is fully hidden from the intruder. Practical experiments, as performed in this research, show that the error rate of quantum computers has to be considerably reduced before implementations of this concept are feasible.
... Computations executed by following the laws of quantum mechanics are generally called quantum computing. Quantum computing offers the manipulation of information with the help of quantum algorithms/circuits [6,7]. ...
... The quantum computing model has become a hot topic in recent years, and it was first proposed by the American physicist Feynman in 1982 (Feynman 1982). The famous Moore's Law states that computer performance will double every 2-3 years (Nagy and Akl 2006). However, Moore's Law cannot hold forever with the electronic components cannot shrink indefinitely. ...
Linear gray enhancement is a spatial domain image enhancement technique commonly used in classical computers, mainly including image negative, image contrast stretching, and piecewise linear gray transformation. In order to realize these three linear gray enhancement techniques in the quantum computers, this paper proposes three types of linear gray transformation schemes for quantum images based on the generalized model of novel enhanced quantum image representation (GNEQR), and the quantum circuits that realize these three transformation methods are constructed according to the schemes. The proposed circuits take advantage of efficient quantum arithmetic operations and parallel Controlled-NOT modules to factor classical transformations into basic unitary operators such as the Controlled-NOT gates and the Toffoli gates. The feasibility of the schemes is verified on IBM platform by using a small size quantum image. The complexity analysis shows the linear gray enhancement algorithm for quantum images is better than the classical algorithm in both spatial complexity and time complexity. Furthermore, the effectiveness of the proposed scheme is also demonstrated by a clarity evaluation method.
... Moore's Law states that computer performance will double every 2-3 years [2]. However, Moore's Law cannot hold forever with the electronic components cannot shrink indefinitely. ...
Linear gray enhancement is a spatial domain image enhancementtechnique commonly used in classical computers, mainly including image negative, image contrast stretching, and piecewise linear gray transformation. Inorder to realize these three linear gray enhancement techniques in the quantumcomputers, this paper proposes three types of linear gray transformationschemes for quantum images based on the generalized model of novel enhancedquantum image representation(GNEQR), and the quantum circuits that realizethese three transformation methods are constructed according to theschemes. The proposed circuits take advantage of efficient quantum arithmeticoperations and parallel Controlled-NOT modules to factor classical transformationsinto basic unitary operators such as the Controlled-NOT gates andthe Toffoli gates. The results show that the linear gray enhancement algorithmfor quantum images is better than the classical algorithm in both spatialcomplexity and time complexity.
... Pauli X, Y, and Z-gates perform amplitude and phase transformations. Superposition is attained by applying Hadamard gate on qubits whereas CNOT gate perform entanglement operations on qubits [17][18]. ...
Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.
... The behavior of a quantum computer is governed by the basic principles of quantum mechanics. Some physicists claim that quantum mechanics is the most complete and accurate description of our world (Nagy and Aki, 2006). A QC exploits the laws of quantum mechanics such as entanglement and superposition, which make it possible for QC to perform jobs such as factoring large semiprimes. ...
Computational technologies drive progress in industry, science, government, and society. While these technologies form the foundation for intelligent systems and enable scientific and business innovation, they are also the limiting factor for progress. Quantum computing promises to overcome these limitations with better and faster solutions for optimization, simulation, and machine learning problems. Quantum computing is broadly applicable to business problems in optimization, machine learning, and simulation, impacting all industries. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Therefore, it is instrumental for industry to seek an active role in this emergent ecosystem. In this chapter, the authors shall present the brief overview on various applications of Quantum Computing in Machine Learning.
... Several researchers have contributed to the successful growth and implementations of quantum computing [12], [13]. It began in 1980 when the a quantum mechanical framework of the Turing machine was examined [14]. ...
It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and connected people to connected intelligence. The main goal of 6G networks is to support massive connectivity for time-sensitive and computation-sensitive services in mission-critical applications. The creation of real-time optimization (RTO) enabled by the fast growing data analytic and machine learning will seize the opportunities for 6G wireless networks to support such immersive services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and tactile Internet. Recently, with the rapid development of quantum computers, quantum-inspired optimization and machine learning algorithms have been exploited as efficient solutions for future wireless networks. In this article, we provide a comprehensive view on the new concept of quantum-inspired RTO and its application to the optimal resource allocation for 6G wireless networks. Our main contributions are to introduce some of the initial research results and introduce the potentiality of quantum-inspired RTO on some 6G emerging technologies. Not only do we review the fundamental principles; we also explore the challenges and opportunities of this exciting research direction.
... Quantum physics also features entanglement of subsystems, which allows for feats like teleportation [2,3] that have no classical analogue. The laws of quantum physics have been exploited in various security schemes, such as Quantum Key Distribution (QKD) [4,5,6], quantum anti-counterfeiting [7], quantum Oblivious Transfer [8,9], authentication and encryption of quantum states [10,11,12], unclonable encryption [13], quantum authentication of PUFs [14,15], and quantum-secured imaging [16], to name a few. For a recent overview of quantum-cryptographic schemes we refer to [17]. ...
... Setup phase. Alice chooses ε 0 and sets according to (13). She chooses values r, λ, β, ν, ε and α, taking care that the following condition is met, ...
... Furthermore, for sources that produce i.i.d. symbols it is known that asymptotically the smooth Rényi entropy approaches the Shannon entropy, which has an advantageous effect on (13). For these reasons, we expect the overhead 0 − to be sub-linear in log |M|. ...
Storing data on an external server with information-theoretic security, while using a key shorter than the data itself, is impossible. As an alternative, we propose a scheme that achieves information-theoretically secure tamper evidence: The server is able to obtain information about the stored data, but not while staying undetected. Moreover, the client only needs to remember a key whose length is much shorter than the data. We provide a security proof for our scheme, based on an entropic uncertainty relation, similar to QKD proofs. Our scheme works if Alice is able to (reversibly) randomise the message to almost-uniformity with only a short key. By constructing an explicit attack we show that short-key unconditional tamper evidence cannot be achieved without this randomisability.
