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Quantum Engineering is a peer-reviewed, open access journal that publishes research on the engineering of quantum information. It bridges the gap between engineers and scientists, enabling them to take advantage of new quantum breakthroughs.
Chief Editor, Professor Gui-Lu Long, is based at Tsinghua University, China. His research interests include quantum communication and computing, and optical microcavity.
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Transformation of Superposed Quantum States Using Measurement Operators
Quantum computation based on a gate model is described. This model initially creates a superposition consisting of states, and these states are labeled by an qubit index value . Two working qubits and are added for a measurement. Moreover, one marking qubit is added to discriminate between states in a superposition. Thus, . The Hadamard transformation is applied to and . . After a computation, a set of states is divided into two subsets; one is a subset bad (B) and the other is a subset good (G). . After a marking, a superposition is measured by POVM. The measurement is described by a collection of four measurement operators. The measurement transforms into ; here, , and which is derived from the completeness equation. The state and the state before the measurement are transformed into and , respectively. This paper describes these measurement operators.
QNUS: Reducing Terminal Resources in Quantum Secure Direct Communication Network Using Switches
The quantum network is an indispensable step toward multiuser and wide-area information interchange in the course of the development of quantum technology. When designing and deploying a quantum network, availability, robustness, flexibility, and expenditure need to be considered in a balanced way. In this article, we propose a network connected through optical switches, QNUS, that requires only terminals in the number of nodes in a quantum network, which is a great saving of resources.
Based on Quantum Topological Stabilizer Color Code Morphism Neural Network Decoder
Solving for quantum error correction remains one of the key challenges of quantum computing. Traditional decoding methods are limited by computing power and data scale, which restrict the decoding efficiency of color codes. There are many decoding methods that have been suggested to solve this problem. Machine learning is considered one of the most suitable solutions for decoding task of color code. We project the color code onto the surface code, use the deep Q network to iteratively train the decoding process of the color code and obtain the relationship between the inversion error rate and the logical error rate of the trained model and the performance of error correction. Our results show that through unsupervised learning, when iterative training is at least 300 times, a self-trained model can improve the error correction accuracy to 96.5%, and the error correction speed is about 13.8% higher than that of the traditional algorithm. We numerically show that our decoding method can achieve a fast prediction speed after training and a better error correction threshold.
Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes
Quantum information transfer is an information processing technology with high speed and high entanglement with the help of quantum mechanics principles. To solve the problem of quantum information getting easily lost during transmission, we choose topological quantum error correction codes as the best candidate codes to improve the fidelity of quantum information. The stability of topological error correction codes brings great convenience to error correction. The quantum error correction codes represented by surface codes have produced very good effects in the error correction mechanism. In order to solve the problem of strong spatial correlation and optimal decoding of surface codes, we introduced a reinforcement learning decoder that can effectively characterize the spatial correlation of error correction codes. At the same time, we use a double-layer convolutional neural network model in the confrontation network to find a better error correction chain, and the generation network can approach the best correction model, to ensure that the discriminant network corrects more nontrivial errors. To improve the efficiency of error correction, we introduced a double-Q algorithm and ResNet network to increase the error correction success rate and training speed of the surface code. Compared with the previous MWPM 0.005 decoder threshold, the success rate has slightly improved, which can reach up to 0.0068 decoder threshold. By using the residual neural network architecture, we saved one-third of the training time and increased the training accuracy to about 96.6%. Using a better training model, we have successfully increased the decoder threshold from 0.0068 to 0.0085, and the depolarized noise model being used does not require a priori basic noise, so that the error correction efficiency of the entire model has slightly improved. Finally, the fidelity of the quantum information has successfully improved from 0.2423 to 0.7423 by using the error correction protection schemes.
An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network
Quantum machine learning is emerging as a strategy to solve real-world problems. As a quantum computing model, parameterized quantum circuits provide an approach for constructing quantum machine learning algorithms, which may either realize computational acceleration or achieve better algorithm performance than classical algorithms. Based on the parameterized quantum circuit, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model for image classification that comprises both quantum and classical components. The quantum convolutional layer is designed using a parameterized quantum circuit. It is used to perform linear unitary transformation on the quantum state to extract hidden information. In addition, the quantum pooling unit is used to perform pooling operations. After the evolution of the quantum system, we measure the quantum state and input the measurement results into a classical fully connected layer for further processing. We demonstrate its potential by applying HQCCNN to the MNIST dataset. Compared to a convolutional neural network in a similar architecture, the results reveal that HQCCNN has a faster training speed and higher testing set accuracy than a convolutional neural network.
Four Equivalent Relations between MCP and CP and Its Implication in Quantum and Information Theory
(Norraw) modal propositional logic (MCP) tries to be a strict model for quantum and information theory, but it has serious difficulties in syntax, semantics, and metaphysics. According to the guiding definition of minimal hidden variables, this study presents four equivalences between MCP and classical propositional logic (CP). It concludes that MCP is CP containing minimal syntactic hidden variables, modal axiom represents the classification of CP formulas, and possible world is CP formula as assignment background, which establishes a simpler and unified basis for quantum information theory. The black modal of classical propositional logic (BCP), as an assisted discovery method, reveals that modal nature is actually to express the interdependence between things and their environment in a mathematical way, which can express the holism, dialectics, and uncertainty of metaphysics, and use mark hiding (e.g., f (x) is a function of x, f () is the functional expression, functional-e for short, and “□” is, in fact, a cluster of functional-es in which superscript and subscript are hidden) that does not affect the effectiveness of reasoning to express a more simplified, efficient, and consistent artificial intelligence.
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