Peer Reviewed Open Access Journal
The current stage of quantum computing development is defined by Noisy Intermediate-Scale Quantum (NISQ) devices, which operate with a moderate number of qubits that are inherently susceptible to noise and decoherence. Despite the absence of full error correction, NISQ systems have demonstrated the ability to perform computational tasks that challenge classical simulation under certain conditions. This paper explores the concept of quantum advantage in the NISQ era by analysing prominent algorithmic approaches, benchmarking methodologies, and the practical constraints imposed by contemporary hardware. Hybrid quantum–classical algorithms, sampling-based experiments, and performance metrics are examined to assess the extent to which near-term quantum devices can outperform classical systems. The study finds that while progress toward quantum advantage is evident, significant technical and algorithmic limitations prevent its widespread realization. The paper concludes by identifying key research directions required to bridge the gap between experimental demonstrations and practical quantum computing applications.
Quantum Advantage, NISQ Devices, Variational Algorithms, Quantum Benchmarking, Quantum Noise, Hybrid Quantum–Classical Computing
Nielsen, M. A., & Chuang, I. L. (2010). Quantum computation and quantum information. Cambridge University Press.
Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.
Aaronson, S. (2015). Quantum advantage. Nature Physics, 11(4), 291–293. https://doi.org/10.1038/nphys3272
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., … Martinis, J. M. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.
Pednault, E., Gunnels, J. A., Nannicini, G., Horesh, L., & Wisnieff, R. (2020). Leveraging secondary storage to simulate deep 54-qubit Sycamore circuits. Physical Review Research, 2(4), 043278. https://doi.org/10.1103/PhysRevResearch.2.043278
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., … O’Brien, J. L. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.
Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv.
Gambetta, J. M., Córcoles, A. D., Magesan, E., Chow, J. M., Smolin, J. A., Merkel, S. T., … Steffen, M. (2017). Benchmarking quantum computers. npj Quantum Information, 3, 2.
Montanaro, A. (2016). Quantum algorithms: An overview. npj Quantum Information, 2, 15023.
Monroe, C., Campbell, W. C., Duan, L.-M., Gong, Z.-X., Gorshkov, A. V., Hess, P. W., … Yao, N. Y. (2021). Programmable quantum simulations of spin systems with trapped ions. Reviews of Modern Physics, 93(2), 025001.
Harrow, A. W., & Montanaro, A. (2017). Quantum computational supremacy. Nature, 549(7671), 203–209. https://doi.org/10.1038/nature23458
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016). The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18(2), 023023.
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., & Neven, H. (2018). Barren plateaus in quantum neural network training landscapes. Nature Communications, 9, 4812.
Aaronson, S., & Arkhipov, A. (2013). The computational complexity of linear optics. Theory of Computing, 9(4), 143–
Cross, A. W., Bishop, L. S., Sheldon, S., Nation, P. D., & Gambetta, J. M. (2019). Validating quantum computers using randomized model circuits. Physical Review A, 100(3), 032328.
Knill, E., Leibfried, D., Reichle, R., Britton, J., Blakestad, R. B., Jost, J. D., … Wineland, D. J. (2008). Randomized benchmarking of quantum gates. Physical Review A, 77(1), 012307.
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., … Aspuru-Guzik, A. (2019). Quantum chemistry in the age of quantum computing. Chemical Reviews, 119(19), 10856–10915.
Temme, K., Bravyi, S., & Gambetta, J. M. (2017). Error mitigation for short-depth quantum circuits. Physical Review Letters, 119(18), 180509.
Gambetta, J. M., Chow, J. M., & Steffen, M. (2017). Building logical qubits in a superconducting quantum computing system. Nature Physics, 13(2), 146–151.
Peng, T., Harrow, A. W., Ozols, M., & Wu, X. (2020). Simulating large quantum circuits on a small quantum computer. Physical Review Letters, 125(15), 150504.
