Quantum Information Processing最新文献

Explainable artificial intelligence is a research topic whose relevance has increased in recent years, especially with the advent of large machine learning models. However, very few attempts have been proposed to improve interpretability in the case of quantum artificial intelligence, and many existing quantum machine learning models in the literature can be considered almost as black boxes. In this article, we argue that an appropriate semantic interpretation of a given quantum circuit that solves a problem can be of interest to the user not only to certify the correct behavior of the learned model, but also to obtain a deeper insight into the problem at hand and its solution. We focus on decision-making problems that can be formulated as classification tasks and propose a method for learning quantum rule-based systems to solve them using evolutionary optimization algorithms. The approach is tested to learn rules that solve control and decision-making tasks in reinforcement learning environments, to provide interpretable agent policies that help to understand the internal dynamics of an unknown environment. Our results conclude that the learned policies are not only highly explainable, but can also help detect non-relevant features of problems and produce a minimal set of rules.

One direction to investigate the relation between quantum walks and their underlying graphs is to define geometric quantity concerning quantum walks. In order to contribute to this direction, we define a transport distance between Grover walks, which can be seen as a quantum analogue of symmetric random walks. We employ signed optimal transport theory to formulate this distance. Also, we define coarse Ricci curvature induced by Grover walks and investigate its property. It has been found that this coarse Ricci curvature has similar properties to those of coarse Ricci curvature induced by random walks.

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. The quantum circuits of the S-box are first studied. According to the algebraic structure of the S-box, four kinds of improved quantum circuits of S-box are presented for different phases in SM4 based on composite field arithmetic. In order to optimize 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 49,4208, which is less than the existing best value 82,5792.

Amidst the rapid advancements in technology, there is a growing demand for processing an increasing volume and quality of images, which necessitates faster image processing capabilities. Enhancing the efficiency of image processing algorithms has thus become a critical priority. Existing quantum image edge detection algorithms tend to exhibit high circuit complexity, which is directly linked to the dimensions of the images being processed, leading to less than optimal computational velocities. In this study, we introduce a quantum image edge detection algorithm that is based on the Laplacian of Gaussian operator. This novel algorithm capitalizes on the quantum parallelism of quantum computing, resulting in a marked enhancement in both the speed and performance of edge detection. To substantiate the practicality of our approach, we conduct simulations using the International Business Machines Quantum (IBM Q) platform. The circuit complexity of our algorithm is meticulously computed, revealing a lower complexity compared to analogous quantum edge detection algorithms. Notably, this complexity is detached from the image size and is solely contingent upon the grayscale value range of the image pixels.

Quantum random walks represent a powerful tool for the implementation of various quantum algorithms. We consider a convolution problem for the graphs which provide quantum and classical random walks. We suggest a new method for lattices and hypercycle convolution that preserves quantum walk dynamics. Our method is based on the fact that some graphs represent a result of Kronecker’s product of line graphs. We support our methods by means of various numerical experiments that check quantum and classical random walks on hypercycles and their convolutions. Our findings may be useful for saving a significant number of qubits required for algorithms that use quantum walk simulation on quantum devices.

The foundation of this research is the quantum implementation of two hashing algorithms, namely Secure Hash Algorithm (SHA1) and Message Digest (MD5). Quantum cryptography is a challenging topic in network security for future networks. Quantum cryptography is an outgrowth of two broad topics—cryptology and cryptanalysis. In this paper, SHA1 and MD5 algorithms are designed and implemented for quantum computers. The main aim is to study and investigate the time requirement to build a hash and the bit rate at which a hash value is sent through. In this paper, a comprehensive analysis of these two algorithms is performed. Experiments have been done to compare and contrast the performances of the classical and proposed algorithms. In the experiment, it was found that the total time of execution of quantum SHA1 and quantum MD5 is much higher than the classical SHA1 and MD5. During quantum MD5 execution, it is observed that the time doubles when the number of chunks is increased from 1 to 2. Another experimental observation is that the execution time of the implemented algorithms depends upon the processor’s speed.

A novel method has been devised to compute the local integrals of motion (LIOMs) for a one-dimensional many-body localized system. In this approach, a class of optimal unitary transformations is deduced in a tensor network formalism to diagonalize the Hamiltonian of the specified system. To construct the tensor network, we utilize the eigenstates of the subsystems’ Hamiltonian to attain the desired unitary transformations. Subsequently, we optimize the eigenstates and acquire appropriate unitary localized operators that will represent the LIOMs tensor network. The efficiency of the method was assessed and found to be both fast and almost accurate. In framework of the introduced tensor network representation, we examine how the entanglement spreads along the considered many-body localized system and evaluate the outcomes of the approximations employed in this approach. The important and interesting result is that in the proposed tensor network approximation, if the length of the blocks is greater than the length of localization, then the entropy growth will be linear in terms of the logarithmic time. Also, it has been demonstrated that the entanglement can be calculated by only considering two blocks next to each other, if the Hamiltonian has been diagonalized using the unitary transformation made by the provided tensor network representation.

In this paper, we design the first semiquantum private comparison (SQPC) protocol which is realized via cavity quantum electrodynamics (QED) by making use of the evolution law of atom. With the help of a semi-honest third party (TP), the proposed protocol can compare the equality of private inputs from two semiquantum parties who only have limited quantum capabilities. The proposed protocol uses product states as initial quantum resource and employs none of unitary operations, quantum entanglement swapping operation or delay lines. Security proof turns out that it can defeat both the external attack and the internal attack.

Device-independent quantum secure direct communication (DI-QSDC) is a promising primitive in quantum cryptography aimed towards addressing the problems of device imperfections and key management. However, significant effort is required to tackle practical challenges such as the distance limitation due to the decohering effects of quantum channels. Here, we explore the constructive effect of non-Markovian noise to improve the performance of DI-QSDC. Considering two different environmental dynamics modelled by the amplitude damping and the dephasing channels, we show that for both cases non-Markovianty leads to a considerable improvement over Markovian dynamics in terms of three benchmark performance criteria of the DI-QSDC task. Specifically, we find that non-Markovian noise (i) enhances the protocol security measured by Bell violation, (ii) leads to a lower quantum bit error rate, and (iii) enables larger communication distances by increasing the capacity of secret communication.

As a new functional network supporting communication, quantum network can provide higher security and lower complexity for quantum information processing. Based on the one-to-one quantum state merging and quantum state redistribution, we first generalize them to the multi-to-one situation and give the optimal cost pair for merging or redistributing quantum states from each sender to the receiver in turn. It is proved that previously received information can reduce the cost of the next transfer. Then, using the method of fusion, we propose a state transfer protocol over the multi-access channel network with one intermediate node and provide its optimal cost pair. Finally, two specific examples are given to demonstrate our results when a GHZ-like state or a Werner-type state is taken as the shared entanglement resource.