In this study, a decision support system for bladder inflammation prediction is presented. The proposed decision support system is built by establishing a hybrid architecture with Gray wolf optimization algorithm (GWO) and Multi-layer perceptron (MLP) networks. In addition to optimizing the hyperparameters in the MLP structure with GWO, the hybrid architecture also optimizes the order of input values to be presented to the MLP structure. The Acute Inflammations data set in the UCI Machine Learning repository was used as the data set in the study. Classification operations were carried out on this data set with the models obtained with hybrid architecture, Decision trees, k-Nearest Neighbors and Support Vector Machines methods. The controversial findings presented as a result of experimental studies have shown that the proposed hybrid architecture produces more successful results than other machine learning methods used in the study. In addition, the MLP network structure optimized with the hybrid architecture offers a new diagnostic method in terms of patient decision support systems.

Urinary Bladder Inflammation Prediction with the Gray Wolf Optimization Algorithm and Multi-Layer Perceptron-Based Hybrid Architecture

Quantum error correction for heavy hexagonal code using deep reinforcement learning with policy reuse

Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints. 

pdf/a-novel-quantum-convolution-neural-network-for-image-sbp71abm2ddg.pdf

A Novel Quantum Convolution Neural Network for Image Classification Applications

Quantum error correction is a crucial technology for realizing quantum computers. These computers achieve fault-tolerant quantum computing by detecting and correcting errors using decoding algorithms. Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models. In this paper, we use a distributed decoding strategy, which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases. Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder. The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy. Then we test the decoding performance of our distributed strategy decoder, recurrent neural network decoder, and the classic minimum weight perfect matching (MWPM) decoder for rotated surface codes with different code distances under the circuit noise model, the thresholds of these three decoders are about 0.0052, 0.0051, and 0.0049, respectively. Our results demonstrate that the distributed strategy decoder outperforms the other two decoders, achieving approximately a 5% improvement in decoding efficiency compared to the MWPM decoder and approximately a 2% improvement compared to the recurrent neural network decoder. 

Recurrent neural network decoding of rotated surface codes based on distributed strategy

Abstract Variational Quantum Eigensolver (VQE) addresses challenges that conventional computation struggles to overcome. However, VQE lacks the ability to autonomously trend towards the direction of quantum advantage, leading to unnecessary consumption of time and resources during parameter optimization. In this work, we present a Simulating Dynamic VQE (D-VQE) that incorporates time evolution for achieving desired simulation accuracy and parameter optimization. Introducing causal cones for Parameterized Quantum Circuits (PQC) and establishing dynamic judgment rules. Consequently, we can reduce the overstacking of components in PQC ansatz. Based on the requirements of precision and circuit scale, a reasonable operator pool is set to deal with the parameters. Through numerical experiments, our method effectively simulates the search for the ground state in the transverse-field Ising model, achieving a relative energy error below 10^(-3) and improving ground state approximation capability. Moreover, in the context of optimized Boltzmann machine learning, the Kullback-Leibler divergence (KLD) value converges to 0.03. These results prove that our work provides an effective and feasible consideration for the improvement of variational quantum algorithms. 

Dynamic Variational Quantum Eigensolver: Incorporating Time-evolution for Ansatz Design and Ground State Simulation

The article discusses the challenge of finding an efficient decoder for quantum error correction codes for fault-tolerant experiments in quantum computing. The study aims to develop a better decoding scheme based on the flag-bridge fault tolerance experiment. The research compares two decoding algorithms, a deep neural network decoding scheme and a simple decoder, and a recurrent neural network decoding scheme based on the belief propagation algorithm variant MBP 4 algorithm. The study improved the syndrome extraction circuit based on the flag-bridge method to meet the requirements of fault-tolerant experiments better. Two decoding schemes were studied, a combination of a deep neural network and a simple decoder and a recurrent neural network structure based on the MBP 4 algorithm. The first scheme used neural networks to assist simple decoders in determining whether additional logical corrections need to be added. The second scheme used a recurrent neural network structure designed through the variant MBP 4 algorithm, along with a post-processing method to pinpoint the error qubit position for decoding. Experimental results showed that the decoding scheme developed in the study improved the pseudo-threshold by 39.52% compared to the minimum-weight perfect matching decoder. The two decoders had thresholds of approximately 15.8% and 16.4%, respectively. The study’s findings suggest that the proposed decoding schemes could improve quantum error correction and fault-tolerant experiments in quantum computing.

/pdf/fault-tolerant-quaternary-belief-propagation-decoding-based-2cc86wif.pdf

Fault-tolerant quaternary belief propagation decoding based on a neural network

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency.

/pdf/cost-sensitive-classification-algorithm-combining-the-vjb4a0hc.pdf

Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical–quantum hybrid training methods. To enhance the system’s resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments.

Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation

When the evolution of discrete time quantum walk is carried out for particles, the ramble state is prone to error due to the influence of system noise. A multiparticle quantum walk error correction algorithm based on the two-lattice Bose–Hubbard model is proposed in this study. First, two point Bose–Hubbard models are constructed according to the local Euclidean generator, and it is proved that the two elements in the model can be replaced arbitrarily. Second, the relationship between the transition intensity and entanglement degree of the particles in the model is obtained by using the Bethe hypothesis method. Third, the position of the quantum lattice is coded and the quantum state exchange gate is constructed. Finally, the state replacement of quantum walk on the lattice point is carried out by switching the walker to the lattice point of quantum error correction code, and the replacement is carried out again. The entanglement of quantum particles in the double-lattice Bose–Hubbard model is simulated numerically. When the ratio of the interaction between particles and the transition intensity of particles is close to 0, the entanglement operation of quantum particles in the model can be realized by using this algorithm. According to the properties of the Bose–Hubbard model, quantum walking error correction can be realized after particle entanglement. This study introduces the popular restnet network as a training model, which increases the decoding speed of the error correction circuit by about 33%. More importantly, the lower threshold limit of the convolutional neural network (CNN) decoder is increased from 0.0058 under the traditional minimum weight perfect matching (MWPM) to 0.0085, which realizes the stable progress of quantum walk with high fault tolerance rate.

Multiparticle quantum walk–based error correction algorithm with two-lattice Bose–Hubbard model


 Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers. In order to solve the problem of the influence of errors on physical qubits, this paper proposes an approximate error correction scheme that performs dimension mapping operations on surface codes. This error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three dimensions. Compared to previous error correction schemes, the three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction capabilities. By reducing the number of ancilla qubits required for error correction, this approach achieves savings in measurement space and reduces resource consumption costs. In order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping, we employ a Reinforcement Learning (RL) decoder based on Deep Qlearning, which enables faster identification of the optimal syndrome and achieves better thresholds through conditional optimization. Compared to the Minimum Weight Perfect Matching (MWPM) decoding, the threshold of the RL trained model reaches 0.78%, which is 56% higher and enables large-scale fault-tolerant quantum computation.

Approximate Error Correction Scheme for Three-Dimensional Surface Codes Base Reinforcement Learning

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