Developing a Model for Predicting Lung Cancer Using Variational Quantum-Classical Algorithm: A Survey


  • Philip Adebayo Kogi State Polytechnic, Lokoja.
  • Frederick Basaky Federal University Lokoja, Kogi State
  • Edgar Osaghae Federal University Lokoja, Kogi State


Classical Computers, Parameterized Quantum Circuits, Artificial Intelligence, Machine Learning, Algorithm


There are real life problems that currently proved hard for classical computers (even the best of supercomputers) to solve-the so called computationally intractable problems. Computers and computing devices are limited, by virtue of the fact that they cannot perform certain complex problems. For example, modern cryptography assumes that it is impossible to factorize a large number as the complexity of solving this problem increases exponentially. Even the best supercomputers of today cannot sufficiently find the prime factors of a number with 700-1,000 digits. With quantum computing, however, Shor’s algorithm proved that in principle, a quantum computer can be used to break the security of conventional cryptosystems. Quantum computing also promises exponential improvements for many optimization problems. Lov Grover algorithm was also designed to search for an element in unstructured database. Effort is currently on to leverage quantum computing and quantum mechanical phenomena to tackle complex machine learning problems. Using machine learning algorithm to solve problems is common place. What is however new is using quantum machine learning algorithm to solve problems such as detecting, classifying and predicting disease such as lung cancer. In this work, we highlight the limitations of classical computers to solve certain problems and then propose a hybrid model called the variational quantum-classical algorithm to predict the possibility of a patient developing lung cancer given a set of features which are spelt out in the dataset. The dataset is initially pre-processed, and made to pass through a Parameterized Quantum Circuit (PQC) and then post-processed. Finally, the output of the post-processing phase is used for prediction of lung cancer.


Download data is not yet available.

Author Biographies

Frederick Basaky , Federal University Lokoja, Kogi State

Department of Computer Science, Faculty of Science.


Edgar Osaghae, Federal University Lokoja, Kogi State

Department of Computer Science, Faculty of Science.



Aaronson S. (2015). Read the fine print. Nat. Phys. 11, 291–293 DOI 10.1038/nphys3272.

Amogh A., Dhruv S., Pratyanush G., & Madhavi R. (2020). A Review of Supervised Variational Quantum Classifiers. International Journal of Engineering Research & Technology (IJERT) 9(4), 574-576.

Amoldeep S., Kapal D., Harun S., Hem D., Maurizio M. (2021). Quantum Internet- Applications, Functionalities, Enabling Technologies, Challenges, and Research Directions

Ananya, M., Bernard R., Audrey B., Stephen P., Laura M. (2021). Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis

Anschuetz E, Olson J, Aspuru-Guzik A & Cao Y (2019). Variational quantum factoring International Workshop on Quantum Technology and Optimization Problems (Berlin: Springer) pp 74–85

Arpita S., Amit S.,Debasri S., Banani S., Amlan C. (2021). Circuit Design for Clique Problem and its Implementation on Quantum Computer, ResearchGate.

Basaky F., Oladipe E., Adebayo P., Hussein U., Sani G. et al. (2021). Artificial Intelligence and Robotics: A Cloud Based Secured Surveillance System and Reduced Human Influence, American Journal of Computer Sciences and Applications 29(02)

Carlo C., Mark H., Alessandro D., Massimiliano P., Andrea R., Simone S. and Leonard W. (2018) Quantum machine learning: a classical perspective. (Accessed 10th January, 2022).

Chalumuri A., Kune R., Manoj B. (2021). A hybrid classical-quantum approach for multi-class classification, Quantum Information Processing 20(3) doi:10.1007/s11128-021-03029-9.

Cincio L, Subaşı Y, Sornborger A. & Coles P J (2018). Learning the quantum algorithm for state overlap New J. Phys. 20 113022

Cowtan A., Dilkes S., Duncan R., Krajenbrink A., Simmons W., Sivarajah S. (2019). On the qubit routing problem arXiv:1902.08091

Frank Z. (2021). Hands-on Quantum Machine Learning with Python.

Huang H., Wu D., Fan D., et al. (2020). Superconducting quantum computing: a review. Sci China Inf Sci, 63(8): 180501,

Iten R., Reardon-Smith O., Mondada L., Redmond E., Kohli R., Colbeck R. (2019). Introduction to universal qcompiler arXiv:1904.

Kashif M., Al-Kuwari S. (2021). Design Space Exploration of Hybrid Quantum–Classical Neural Networks. Electronics, 10, 2980. (Accessed 20th January, 2020)

Kok D. (2021). Building a quantum kNN classifier with Qiskit: theoretical gains put to practice.

Marcello B., Erika L., Stefan S., Mattia F. (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology. 4 043001.

Milan J., & Setu K. (2013). An Efficient Cancer Disease Prediction System through Quantum Computing Technique, International Journal of Computer Applications 81(3).

Mitarai K. & Fujii K. (2019). Methodology for replacing indirect measurements with direct measurements Phys. Rev. Res. 1 013006

Morales M., Tlyachev T & Biamonte J (2018). Variational learning of groverʼs quantum search algorithm Phys. Rev. A 98 062333

Peter W. Shor. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5):1484 1509.

Sathyakumar K., Munoz M., Singh J, et al. (2020). Automated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Literature Review. Cureus 12(8): e10017. DOI 10.7759/cureus.10017. Springer Nature Singapore Pte Ltd. techniques. Journal of Software Engineering and Applications 69–77 (2014).

Stephens R. (2019). Essential Algorithms, A practical Approach to Computer Algorithms using Python and C#. Indianapolis: John Wiley and Sons, Inc.(Pp 572).

Wan K., Liu F., Dahlsten O. & Kim M. (2018). Learning simonas quantum algorithm arXiv:1806.10448

Wang D., Higgott O. and Brierley S. (2019). Accelerated variational quantum eigensolver Phys. Rev. Lett. 122 140504



How to Cite

Adebayo, P., Basaky , F., & Osaghae, E. (2022). Developing a Model for Predicting Lung Cancer Using Variational Quantum-Classical Algorithm: A Survey. Journal of Applied Artificial Intelligence, 3(1), 47–60.




Most read articles by the same author(s)