A key component of the comprehending genetic variants and their biological effects is the rapid identification of protein mutations. For mutation classification, a novel machine learning framework is used in this study that combines quantum and classical deep learning techniques. Protein sequence data from the National Centre for Biotechnology Information (NCBI) is used and undergo preprocessing, artificial mutation simulation, and one-hot encoding to convert sequences into numerical representations. Feature selection is performed using Ant Colony Optimization (ACO) to reduce dimensionality and enhance computational efficiency. The classification process uses a Quantum Long Short-Term Memory (QLSTM) network with linear, variational quantum circuit (VQC), fully connected, dropout, and output layers. The hybrid model successfully separates mutant and non-mutated protein sequences and the hybrid methodology's overall accuracy was 95%, according to experimental assessment, providing a viable approach for bioinformatics applications that make use of quantum computing.
A key component of the comprehending genetic variants and their biological effects is the rapid identification of protein mutations. For mutation classification, a novel machine learning framework is used in this study that combines quantum and classical deep learning techniques. Protein sequence data from the National Centre for Biotechnology Information (NCBI) is used and undergo preprocessing, artificial mutation simulation, and one-hot encoding to convert sequences into numerical representations. Feature selection is performed using Ant Colony Optimization (ACO) to reduce dimensionality and enhance computational efficiency. The classification process uses a Quantum Long Short-Term Memory (QLSTM) network with linear, variational quantum circuit (VQC), fully connected, dropout, and output layers. The hybrid model successfully separates mutant and non-mutated protein sequences and the hybrid methodology's overall accuracy was 95%, according to experimental assessment, providing a viable approach for bioinformatics applications that make use of quantum computing.
The integration of metaheuristics with machine learning methodologies presents significant advantages, particularly in optimization and computational intelligence. This amalgamation leverages the global search capabilities of metaheuristics alongside the pattern recognition and predictive prowess of machine learning, facilitating enhanced convergence rates and solution quality in complex problem spaces. The Quantum Long Short-Term Memory (QLSTM) emerges as a highly efficient deep learning model tailored to tackle such intricate engineering problems. The QLSTM's architecture, comprising data encoding, variational, and quantum measurement layers, facilitates the effective encoding and processing of civil engineering data, leading to heightened prediction accuracy. However, the task of determining optimal values for QLSTM parameters presents challenges due to its NP-problem nature and time-consuming characteristics. To address this, we propose an alternative technique to optimize the QLSTM based on a modified Electric Eel Foraging Optimization (MEEFO). The MEEFO is a modified version of the original EEFO that applies triangular mutation operators to boost the search capability of the traditional EEFO. Thus, the MEEFO optimizes the QLSTM and boosts its prediction performance. To validate the efficacy of our proposed method, we conduct comprehensive experiments utilizing five real-world engineering datasets related to construction and structure engineering. The evaluation outcomes unequivocally demonstrate that the MMEFO significantly enhances the performance of the QLSTM.
Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quantum computing. A crucial task within this domain involves the functional classification of proteins. However, a challenge lies in adequately representing lengthy protein sequences given the limited number of qubits available in existing noisy quantum computers. We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various protein-related machine-learning problems. We classify proteins based on their sub-cellular locations—a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. It’s noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.
Traditional machine learning (ML) and deep learning (DL) models have shown effectiveness in natural language processing (NLP) tasks, such as sentiment analysis.However, they often struggle with complex linguistic structures, such as sarcasm and implicit claims.This paper introduces a Quantum Long Short-Term Memory (QLSTM) framework for detecting sarcasm and identifying claims in text, aiming to enhance the analysis of complex sentences.We evaluate four approaches: (1) classical LSTM, (2) quantum framework using QLSTM, (3) voting ensemble combining classical and quantum LSTMs, and (4) hybrid framework integrating both types.The experimental results indicate that the QLSTM approach excels in sarcasm detection, while the voting framework performs best in claim identification.
Computational methods in drug discovery significantly reduce both time and experimental costs. Nonetheless, certain computational tasks in drug discovery can be daunting with classical computing techniques which can be potentially overcome using quantum computing. A crucial task within this domain involves the functional classification of proteins. However, a challenge lies in adequately representing lengthy protein sequences given the limited number of qubits available in existing noisy quantum computers. We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various proteinrelated machine-learning problems. We classify proteins based on their sub-cellular locations—a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. It’s noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.
Long-short term memory (LSTM) neural network solves the problems of long-term dependence, gradient disappearance and gradient explosion by introducing memory units, and is widely used in time series analysis and prediction. Combining quantum computing with LSTM neural network will help to improve its computational efficiency and reduce the number of model parameters, thus significantly improving the performance of traditional LSTM neural network. This paper proposes a hybrid quantum LSTM (hybrid quantum long-short term memory, HQLSTM) network model that can be used to realize the image classification. It uses variable quantum circuits to replace the nerve cells in the classical LSTM network to realize the memory function of the quantum network. At the same time, it introduces Choquet integral operator to enhance the degree of aggregation between data. The memory cells in the HQLSTM network are composed of multiple variation quantum circuits (VQC) that can realize different functions. Each VQC consists of three parts: the coding layer, which uses angle coding to reduce the complexity of network model design; the variation layer, which is designed with quantum natural gradient optimization algorithm, so that the gradient descent direction does not target specific parameters, thereby optimizing the parameter update process and improving the generalization and convergence speed of the network model; the measurement layer, which uses the Pauli Z gate to measure, and the expected value of the measurement result is input to the next layer to extract useful information from the quantum circuit. The experimental results on the MNIST, FASHION-MNIST and CIFAR datasets show that the HQLSTM model achieves higher image classification accuracy and lower loss value than the classical LSTM model and quantum LSTM model. At the same time, the network space complexity of HQLSTM and quantum LSTM are significantly reduced compared with the classical LSTM network.
By facilitating quicker and more precise analysis of intricate biological data, the union of quantum computing and Artificial Intelligence (AI) may transform genomic medicine and drug discovery altogether. The study suggests a model for artificial intelligence with quantum-enhanced acceleration based on hybrid quantum-classical models in order to speed up drug discovery and customize therapies. It utilizes deep learning models and Quantum Machine Learning (QML) techniques to analyze patient-specific data, protein structures, and genetic sequences. Large genomic and clinical data sets are processed by conventional deep learning models, while the quantum component carries out efficient data encoding and optimization processes. As compared to other traditional AI-based approaches, the proposed model performs a 30% improved drug target identification when evaluated against more than 1,000 genetic sequences and related clinical outcomes. The hybrid model was as accurate as 98.4% in its prediction and was more accurate than the conventional AI techniques including random forests (accuracy: 91.3%) and gradient boosting machines (accuracy: 93.7%). The methodology also produced an F1-score of 0.95 with accuracy and recall scores of 96.1% and 94.7%, respectively. These results demonstrate how quantum-enhanced AI can reduce the time for computation and improve the accuracy of genomic data analysis, driving significant progress toward personalized therapy and drug development.