We describe a quantum chemistry simulation software program <i>BQ-Chem</i>, which can calculate the low-energy spectrum and potential energy surface of molecules on a quantum computer. <i>BQ-Chem</i> is based on the full quantum eigensolver (FQE), which is implemented with a quantum gradient descent algorithm. Benefiting from FQE, <i>BQ-Chem</i> can perform all the calculations on a quantum computer. Compared with the classical optimization methods which encounter the optimization difficulty of high-dimensional and multivariable functions in dealing with multielectron orbitals of macromolecules, FQE provides an exponential speedup. FQE works fully on a quantum computer; thus, <i>BQ-Chem</i> can be smoothly transited to future large-scale quantum computers.

Hang Li

Pan Gao

Jiang Zhang

Zhiyuan Liu

Hai Wei

Kai Wen

Shijie Wei

Gui-Lu Long

<i>BQ-Chem</i>: A Quantum Software Program for Chemistry Simulation Based on the Full Quantum Eigensolver Algorithm

Quantum error correction (QEC) is a key technique for building scalable quantum computers that can be used to mitigate the effects of errors on physical quantum bits. Since quantum states are more or less affected by noise, errors are inevitable. Traditional QEC codes face huge challenges. Therefore, designing an error suppression algorithm based on neural networks (NN) and quantum topological error correction (QTEC) codes is particularly important for quantum teleportation. In this paper, QTEC codes: semion codes—a greater than 2 dimensional (2D) error correction code based on the double semion model—are used to suppress errors during quantum teleportation, using a NN to build a decoder based on semion codes and to simulate the quantum information error suppression process and the suppression effect. The proposed convolutional neural network (CNN) decoder is suitable for small distance topological semion codes. 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xlink:href="#g121-63"/></g><g transform="matrix(.013,0,0,-0.013,8.944,0)"><use xlink:href="#g121-112"/></g><g transform="matrix(.013,0,0,-0.013,13.597,0)"><use xlink:href="#g121-99"/></g><g transform="matrix(.013,0,0,-0.013,19.2,0)"><use xlink:href="#g121-95"/></g><g transform="matrix(.013,0,0,-0.013,28.539,0)"><use xlink:href="#g117-91"/></g></svg><span class="irelop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="38.895pt" style="vertical-align:-0.6370001pt" height="9.87315pt" version="1.1" viewBox="39.7521838 -9.23615 38.895 9.87315"><g transform="matrix(.013,0,0,-0.013,39.802,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,46.042,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,49.006,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,55.246,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g><g transform="matrix(.013,0,0,-0.013,63.662,0)"><use xlink:href="#g121-98"/></g><g transform="matrix(.013,0,0,-0.013,70.526,0)"><use xlink:href="#g113-67"/></g></svg>.</span></span> This paper demonstrates the ability of CNNs to suppress errors in quantum transmission information and the great potential of NNs in the field of quantum computing.

Qian Cao

Hao-Wen Wang

Ying-Jie Qu

Yun-Jia Xue

Shu-Mei Wang

Quantum Teleportation Error Suppression Algorithm Based on Convolutional Neural Networks and Quantum Topological Semion Codes

Hideaki Ito

Saburou Iida

Transformation of Superposed Quantum States Using Measurement Operators

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.

Peng-Hao Niu

Fei-Hao Zhang

Xiu-Wei Chen

Min Wang

QNUS: Reducing Terminal Resources in Quantum Secure Direct Communication Network Using Switches

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&#x0025;, and the error correction speed is about 13.8&#x0025; 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.

Li Ding

Haowen Wang

Yinuo Wang

Shumei Wang

Based on Quantum Topological Stabilizer Color Code Morphism Neural Network Decoder

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&#x0025;. 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.

Yan-Bing Tian

Yi-Nuo Wang

Yu-Xuan Wang

Quantum Information Protection Scheme Based on Reinforcement Learning for Periodic Surface Codes

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.

Wei Li

Peng-Cheng Chu

Guang-Zhe Liu

Tian-Hui Qiu

An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network

(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., <i>f </i>(<i>x</i>) is a function of <i>x</i>, <i>f </i>() is the functional expression, functional-e for short, and &#x201c;&#x25a1;&#x201d; 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.

Xiao-long Wan

Four Equivalent Relations between MCP and CP and Its Implication in Quantum and Information Theory

The entangled &#x201C;measurement state&#x201D; (MS), predicted by von Neumann to arise during quantum measurement, seems to display paradoxical properties such as multiple macroscopic outcomes. But analysis of interferometry experiments using entangled photon pairs shows that entangled states differ surprisingly from simple superposition states. Based on standard quantum theory, this paper shows that the MS (i) does not represent multiple detector readings but instead represents nonparadoxical multiple <i>statistical correlations between</i> system states and detector readings, (ii) implies that exactly one outcome actually occurs, and (iii) implies that when one outcome occurs, the other possible outcomes simultaneously collapse nonlocally. Point (iii) resolves an issue first raised in 1927 by Einstein who demonstrated that quantum theory requires instantaneous state collapse. This conundrum&#x2019;s resolution requires nonlocal correlations, which from today&#x2019;s perspective suggests the MS should be an entangled state. Thus, contrary to previous presumed proofs of the measurement problem&#x2019;s insolubility, we find the MS to be the collapsed state and just what we expect upon measurement.

Art Hobson

Entanglement and the Measurement Problem

Quantum key distribution (QKD) enables two remote users to share a string of key bits with information-theoretical security. Parameter optimization is a crucial step in achieving optimal performance in practical QKD systems. In general, such optimization is implemented using a local search algorithm (LSA). However, LSAs inevitably fail to find out the optimal values when the searched key rate function is nonconvex or has a discontinuity of first-order derivatives and a narrow parameter search space. This paper proposes a genetic algorithm-based method to overcome the limitations of LSAs for QKD parameter optimization. We tested the proposed method with various types of common QKD protocols and found that it has very high parameter optimization performance for QKD with a time consumption comparable to that using a standard LSA. We expect our method to be a valuable optimization tool for quantum information processing tasks.

Zijian Li

Kejin Wei

Improving Parameter Optimization in Decoy-State Quantum Key Distribution

Quantum mechanics is a cornerstone of our current understanding of nature and extremely successful in describing physics covering a huge range of scales. However, its interpretation remains controversial since the early days of quantum mechanics. What does a quantum state really mean? Is there any way out of the so-called quantum measurement problem? Here, we present an information-complete quantum theory (ICQT) and the trinary property of nature to beat the above problems. We assume that a quantum system&#x2019;s state provides an information-complete description of the system in the trinary picture. We present a consistent formalism of quantum theory that makes the information-completeness explicitly and argue that conventional quantum mechanics is an approximation of the ICQT. We then show how our ICQT provides a coherent picture and fresh angle of some existing problems in physics. The computational content of our theory is uncovered by defining an information-complete quantum computer.

Zeng-Bing Chen

The Information-Complete Quantum Theory

About this journal

<p><em>Quantum Engineering</em>&nbsp;is a peer-reviewed, open access journal that publishes research on&nbsp;the engineering of quantum information. It bridges the gap between engineers and scientists, enabling them to take advantage of new quantum breakthroughs.</p>


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<p>Chief Editor, Professor&nbsp;Gui-Lu Long, is based at Tsinghua University, China.&nbsp;His research interests include quantum communication and computing, and optical microcavity.</p>


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Quantum Engineering - Table of contents 2022

title

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.

Quantum Engineering