A hybrid quantum feature selection algorithm using a quantum inspired graph theoretic approach
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S. Chakraborty et al. [22] utilized a hybrid quantum feature selection algorithm (HQFSA) to forecast metal and rocks, achieving an accuracy of 74% on a sonar dataset containing 60 features.
“S. Chakraborty et al. [22] utilized a hybrid quantum feature selection algorithm (HQFSA) to forecast metal and rocks, achieving an accuracy of 74% on a sonar dataset containing 60 features.”
From Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset (2022) · cites this paper as [22]
Cites
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,940 · see citing works
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436 · see citing works
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779 · see citing works
Interpretation of the correlation coefficient: A basic review
Taylor, R. (1990) · Journal of Diagnostic Medical Sonography · doi:10.1177/875647939000600106 · Cited by 2,390 · see citing works
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
Han, K.-H., & Kim, J.-H. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2002.804320 · Cited by 1,601 · see citing works
An introduction to quantum machine learning
Schuld, M., Sinayskiy, I., & Petruccione, F. (2015) · Contemporary Physics · doi:10.1080/00107514.2014.964942 · Cited by 1,381 · see citing works
Whale optimization approaches for wrapper feature selection
Mafarja, M. M., & Mirjalili, S. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.11.006 · Cited by 831 · see citing works
Asunción, A. (2007) · Medical Entomology and Zoology · Cited by 640 · see citing works
Quantum algorithms for supervised and unsupervised machine learning
Lloyd, S., Mohseni, M., & Rebentrost, P. (2013) · arXiv · doi:10.48550/arxiv.1307.0411 · Cited by 542 · see citing works
Abualigah, L., Khader, A. T., & Hanandeh, E. S. (2017) · Journal of Computational Science · doi:10.1016/j.jocs.2017.07.018 · Cited by 486 · see citing works
Han, K.-H., & Kim, J.-H. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.823467 · Cited by 485 · see citing works
An introduction to quantum machine learningPreprint
Schuld, M., Sinayskiy, I., & Petruccione, F. (2014) · Cited by 435 · see citing works
Bennett, C. H., & Shor, P. W. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720553 · Cited by 365 · see citing works
McMahon, D. (2007) · Wiley eBooks · doi:10.1002/9780470181386 · Cited by 328 · see citing works
Pareto front feature selection based on artificial bee colony optimization
Hançer, E., Xue, B., Zhang, M., Karaboğa, D., & Akay, B. (2017) · Information Sciences · doi:10.1016/j.ins.2017.09.028 · Cited by 326 · see citing works
Brassard, G., Høyer, P., & Tapp, A. (1998) · Lecture notes in computer science · doi:10.1007/bfb0055105 · Cited by 322 · see citing works
Sentiment Analysis of Review Datasets Using Naïve Bayes‘ and K-NN Classifier
Dey, L., Chakraborty, S., Biswas, A., Bose, B., & Tiwari, S. (2016) · International Journal of Information Engineering and Electronic Business · doi:10.5815/ijieeb.2016.04.07 · Cited by 279 · see citing works
Self-citationImproved whale optimization algorithm for feature selection in Arabic sentiment analysis
Tubishat, M., Abushariah, M. A. M., Idris, N. B., & Aljarah, I. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1334-8 · Cited by 274 · see citing works
(2017) · WORLD SCIENTIFIC (EUROPE) eBooks · doi:10.1142/9781786342782_0004 · Cited by 245 · see citing works
Bennett, C. H. (2018) · CRC Press eBooks · doi:10.1201/9780429500459-13 · Cited by 226 · see citing works
Entanglement-Based Machine Learning on a Quantum Computer
Cai, X., Wu, D., Su, Z.-E., Chen, M.-C., Wang, X., Li, L., Liu, N.-L., Lu, C., & Pan, J.-W. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.110504 · Cited by 205 · see citing works
Gupta, S., & Zia, R. K. P. (2001) · Journal of Computer and System Sciences · doi:10.1006/jcss.2001.1769 · Cited by 196 · see citing works
Quantum Query Complexity of Some Graph Problems
Dürr, C., Heiligman, M., Høyer, P., & Mhalla, M. (2006) · SIAM Journal on Computing · doi:10.1137/050644719 · Cited by 151 · see citing works
Feature selection based on FDA and F-score for multi-class classification
Song, Q., Jiang, H., & Liu, J. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.049 · Cited by 125 · see citing works
A quantum-inspired gravitational search algorithm for binary encoded optimization problems
Nezamabadi–pour, H. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.01.002 · Cited by 96 · see citing works
IG-GA: A Hybrid Filter/Wrapper Method for Feature Selection of Microarray Data
Yang, C., Chuang, L., & Yang, C. (2010) · Journal of Medical and Biological Engineering · Cited by 96 · see citing works
Application of binary quantum-inspired gravitational search algorithm in feature subset selection
Barani, F., Mirhosseini, M., & Nezamabadi–pour, H. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0894-3 · Cited by 72 · see citing works
Zhou, H., Zhang, Y., Zhang, Y., & Liu, H. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1305-0 · Cited by 62 · see citing works
A Feature Selection Method Based on Hybrid Improved Binary Quantum Particle Swarm Optimization
Wu, Q., Ma, Z., Fan, J., Xu, G., & Shen, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2919956 · Cited by 59 · see citing works
Das, A. K., Goswami, S., Chakrabarti, A., & Chakraborty, B. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.06.032 · Cited by 57 · see citing works
Self-citationAl-Rajab, M. M., Lu, J., & Xu, Q. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.05.001 · Cited by 50 · see citing works
A feature cluster taxonomy based feature selection technique
Goswami, S., Das, A. K., Chakrabarti, A., & Chakraborty, B. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.044 · Cited by 48 · see citing works
Self-citationZouache, D., & Abdelaziz, F. B. (2017) · Computers & Industrial Engineering · doi:10.1016/j.cie.2017.10.025 · Cited by 46 · see citing works
Ibrahim, A. A., Mohamed, A., & Shareef, H. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2013.12.019 · Cited by 42 · see citing works
Quantum Algorithms for Matching and Network Flows
Ambainis, A., & Špalek, R. (2006) · Lecture notes in computer science · doi:10.1007/11672142_13 · Cited by 38 · see citing works
Quantum-enhanced feature selection with forward selection and backward elimination
He, Z., Li, L., Huang, Z., & Situ, H. (2018) · Quantum Information Processing · doi:10.1007/s11128-018-1924-8 · Cited by 32 · see citing works
Resemblance Coefficient and a Quantum Genetic Algorithm for Feature Selection
Zhang, G., Hu, L., & Jin, W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30214-8_12 · Cited by 24 · see citing works
Filter-Based Feature Selection Methods Using Hill Climbing Approach
Goswami, S., Chakraborty, S., Guha, P., Tarafdar, A., & Kedia, A. (2018) · Unsupervised and semi-supervised learning · doi:10.1007/978-3-319-98566-4_10 · Cited by 15 · see citing works
Self-citationSingh, K. V., & Raza, Z. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4103 · Cited by 14 · see citing works
Correlation Based Feature Selection Using Quantum Bio Inspired Estimation of Distribution Algorithm
Soliman, O. S., & Rassem, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35455-7_29 · Cited by 11 · see citing works
A new method for feature selection based on intelligent water drops
Khosravi, M. H., & Bagherzadeh, P. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1313-0 · Cited by 10 · see citing works
Quantum approaches to graph colouring
D’Hondt, E. (2008) · Theoretical Computer Science · doi:10.1016/j.tcs.2008.09.055 · Cited by 8 · see citing works
Dasgupta, A., Banerjee, A., Dastidar, A. G., Barman, A., & Chakraborty, S. (2019) · CRC Press eBooks · doi:10.1201/9780429263798-3 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
2 of the 45 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Systematic literature review: Quantum machine learning and its applications
García, D. P., Cruz-Benito, J., & García‐Peñalvo, F. J. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100619 · Cited by 328 · see citing works
Ullah, U., & García-Zapirain, B. (2024) · IEEE Access · doi:10.1109/access.2024.3353461 · Cited by 211 · see citing works
Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision
Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116512 · Cited by 157 · see citing works
Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
Maheshwari, D., García-Zapirain, B., & Sierra-Sosa, D. (2022) · IEEE Access · doi:10.1109/access.2022.3195044 · Cited by 147 · see citing works
Quantum Machine Learning—An Overview
Tychola, K. A., Kalampokas, Τ., & Papakostas, G. A. (2023) · Electronics · doi:10.3390/electronics12112379 · Cited by 132 · see citing works
Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset
Maheshwari, D., Sierra-Sosa, D., & Garcia-Zapirain, B. (2022) · IEEE Access · doi:10.1109/access.2021.3139323 · Cited by 108 · see citing works
Classification with Quantum Machine Learning: A Survey
Abohashima, Z., Elhosen, M., Houssein, E. H., & Mohamed, W. M. (2020) · arXiv · doi:10.48550/arxiv.2006.12270 · Cited by 68 · see citing works
Rishiwal, V., Agarwal, U., Yadav, M., Tanwar, S., Garg, D., & Guizani, M. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3535414 · Cited by 61 · see citing works
An Image Denoising Technique using Quantum Wavelet Transform
Chakraborty, S., Shaikh, S. H., Chakrabarti, A., & Ghosh, R. (2020) · International Journal of Theoretical Physics · doi:10.1007/s10773-020-04590-2 · Cited by 51 · see citing works
Self-citationDhar, J., & Jodder, A. K. (2020) · Ingénierie des systèmes d information · doi:10.18280/isi.250502 · Cited by 35 · see citing works
A Comprehensive Survey on Quantum Machine Learning and Possible Applications
Umer, M. J., & Sharif, M. I. (2022) · International Journal of E-Health and Medical Communications · doi:10.4018/ijehmc.315730 · Cited by 35 · see citing works
Quantum-inspired evolutionary algorithms for feature subset selection: a comprehensive survey
Vivek, Y., Ravi, V., & Krishna, P. R. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04787-6 · Cited by 36 · see citing works
Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm
Li, Y., Zhou, R., Xu, R., Luo, J., Hu, W., & Fan, P. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3190042 · Cited by 24 · see citing works
Quantum computing and quantum-inspired techniques for feature subset selection: a review
Mandal, A. K., & Chakraborty, B. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02282-5 · Cited by 21 · see citing works
Robust Quantum Feature Selection With Sparse Optimization Circuit
Li, J., Song, J., Shi, J., Xu, H., Yu, H., Chen, G., & Zhang, S. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3526060 · Cited by 20 · see citing works
Malik, S., Patro, S. G. K., Mahanty, C., Lasisi, A., Naveed, Q. N., Buradi, A., Emma, A. F., Kumar, S., & Mubarakali, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-14793-4 · Cited by 16 · see citing works
Nouioua, T., & Belbachir, A. H. (2022) · Chinese Journal of Physics · doi:10.1016/j.cjph.2022.11.006 · Cited by 17 · see citing works
The role of entanglement for enhancing the efficiency of quantum kernels towards classification
Sharma, D., Singh, P., & Kumar, A. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.128938 · Cited by 17 · see citing works
Quantum Artificial Intelligence Scalability in the NISQ Era: Pathways to Quantum Utility
Moreau, G. S., Pisani, L., Profir, M., Podda, C., Leoni, L., & Cao, G. (2025) · Advanced Quantum Technologies · doi:10.1002/qute.202400716 · Cited by 16 · see citing works
Quantum artificial intelligence: A survey
Acampora, G., Chiatto, A., Schiattarella, R., & Vitiello, A. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100807 · Cited by 14 · see citing works
Quantum-Enhanced Support Vector Classifier for Image Classification
Golchha, R., & Verma, G. K. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126421 · Cited by 14 · see citing works
Quantum Computing and Quantum Machine Learning Classification – A Survey
Kuppusamy, P., Kumar, N. Y., Dontireddy, J., & Iwendi, C. (2022) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla56841.2022.9989137 · Cited by 14 · see citing works
Quantum image edge extraction based on classical robinson operator
Chakraborty, S., Shaikh, S. H., Chakrabarti, A., & Ghosh, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12627-3 · Cited by 12 · see citing works
Self-citationQuantum autoencoders for image classification
Asaoka, H., & Kudo, K. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00297-x · Cited by 11 · see citing works
Xue, Y., & Deng, Y. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.111835 · Cited by 11 · see citing works
Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases
Malik, S., Patro, S. G. K., Mahanty, C., Kumar, S., Lasisi, A., Naveed, Q. N., Kulkarni, A., Buradi, A., Emma, A. F., & Kraïem, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-91136-3 · Cited by 10 · see citing works
VQNE: Variational Quantum Network Embedding with Application to Network Alignment
Ye, X., Yan, G., & Yan, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599542 · Cited by 9 · see citing works
Benchmarking quantum versions of the kNN algorithm with a metric based on amplitude-encoded features
Guerrero-Estrada, A. Y., Quezada, L. F., & Sun, G.-H. (2024) · Scientific Reports · doi:10.1038/s41598-024-67392-0 · Cited by 6 · see citing works
Light‐Cone Feature Selection for Quantum Machine Learning
Suzuki, Y., Sakuma, R., & Kawaguchi, H. (2025) · Advanced Quantum Technologies · doi:10.1002/qute.202400647 · Cited by 8 · see citing works
Intertwine: Nonlinear Quantum Feature Selection With Multikernel Circuits
Xu, H., & Li, J. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3635568 · Cited by 7 · see citing works
Quantum-Inspired Aspect-Based Sentiment Analysis Using Natural Language Processing
Kayal, M., Sahoo, M., Pati, J., & Behera, R. K. (2025) · Intelligent systems reference library · doi:10.1007/978-3-031-89905-8_8 · Cited by 5 · see citing works
Quantum Feature Selection with Variance Estimation
Poggiali, A., Bernasconi, A., Berti, A., Corso, G. M. D., & Guidotti, R. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-99 · Cited by 5 · see citing works
Quantum-Inspired Statistical Frameworks: Enhancing Traditional Methods with Quantum Principles
Kyriazos, T. A., & Poga, M. (2025) · Encyclopedia · doi:10.3390/encyclopedia5020048 · Cited by 6 · see citing works
Bisection Grover’s Search Algorithm and Its Application in Analyzing CITE-seq Data
Ma, P., Chen, Y., Lu, H., & Zhong, W. (2024) · Journal of the American Statistical Association · doi:10.1080/01621459.2024.2404259 · Cited by 4 · see citing works
Quantum subroutine for variance estimation: algorithmic design and applications
Bernasconi, A., Berti, A., Corso, G. M. D., Guidotti, R., & Poggiali, A. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00213-9 · Cited by 4 · see citing works
Multi-Objective Feature Selection: Reviewing Decades of Research
Takramian, A., Nouri-Moghaddam, B., Mirzaei, A., Mikaeilvand, N., Gudakahriz, S. J., & Hoseini, F. (2025) · International Conference on Web Research (ICWR) · doi:10.1109/icwr65219.2025.11006227 · Cited by 3 · see citing works
Quantum Variational Autoencoder for Feature Compression and Classification of Cancer Images
Andra, M. B., Zilvan, V., Yuwana, R. S., Krisnandi, D., Suryawati, E., Heryana, A., & Pardede, H. F. (2026) · SN Computer Science · doi:10.1007/s42979-026-04742-x · Cited by 3 · see citing works
Majumder, S., Ray, S., Dasgupta, M., Bhattacharya, P., Gadekallu, T. R., & Srivastava, G. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3635525 · Cited by 3 · see citing works
Quantum Multiview Feature Selection With Configurable Kernel Circuits and Adaptive Fusion
Li, J., Xu, H., Chen, G., & Zhang, S. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3650093 · Cited by 2 · see citing works
Optimized Quantum Classifiers for the Prevention of Anxiety Disorders Using Wearable Data
Papamentzelopoulos, S., & Nikoletseas, S. (2026) · Applied Sciences · doi:10.3390/app16126132 · Cited by 1 · see citing works
Quantum Artificial Intelligence: A tutorial
Martín‐Guerrero, J. D., Lamata, L., & Villmann, T. (2023) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2023.es2023-2 · Cited by 1 · see citing works
Wei, L. (2022) · Security and Communication Networks · doi:10.1155/2022/5766802 · Cited by 1 · see citing works
RetractedGlints Detection in Noisy Images Using Graph Matching
Liu, W., Liu, Q., Yang, J., & Chen, Y. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3213656 · Cited by 1 · see citing works
Robust Quantum Feature Selection AlgorithmPreprint
Li, J., Zhang, S., Xu, H., Yu, H., & Shi, J. (2022) · doi:10.36227/techrxiv.21533610.v1 · Cited by 1 · see citing works
Robust Quantum Feature Selection AlgorithmPreprint
Zhang, S., Li, J., Xu, H., Yu, H., & Shi, J. (2022) · doi:10.36227/techrxiv.21533610
Reduction of Search Space of Unmanned Aerial Vehicles Using Improved Swarm Based Algorithm
Pandey, K., & Jha, C. K. (2023) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscna58489.2023.10370360
Research on Key Technologies for Analyzing Quantum Computing with Deep Learning
Chen, K., Shi, L., He, C., Yu, Y., & Deng, K. (2024) · SPIN · doi:10.1142/s2010324724400083
Quantum feature-map learning with reduced resource overhead
Jäger, J., Elsässer, P., & Torabian, E. (2026) · Physical Review Research · doi:10.1103/v29j-rh32
A Clustering-Induced Opposition-Based Grasshopper Optimization Algorithm
Pramanik, S., & Jana, B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6351-4_27
Katib, I. A., Ragab, M., Sharaf, S. A., Bedewi, W., Albassam, E., & Al-Ghamdi, A. A. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.04.033
Fostering security in healthcare recommendation systems with quantum federated learning
Nedungadi, N., Sankaran, S., & Sharma, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33700-0.00003-0
- Authors
- Chakraborty, S., Shaikh, S. H., Chakrabarti, A., & Ghosh, R.
- Affiliations
- University of Calcutta (IN)
- BML Munjal University (IN)
- Corresponding author
- Chakraborty, Sanjay
- Venue
- Applied Intelligence
- Published
- 2020-02-12
- Volume
- 50
- Issue
- 6
- Pages
- 1775-1793
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- OpenAlex
- W3005630791
- Cited by
- 51
- References
- 45
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 7 · 2023: 7 · 2022: 12 · 2021: 1 · 2020: 3
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum machine learning, dimensionality reduction, correlation coefficient, quantum oracle, CNOT gate, amplitude estimation, amplitude amplification, query complexity, supervised classification
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