Citation :

Aishwarya B, Gopika P, 2025. "Applications of Quantum Computing: A Review of Current and Emerging Use Cases" ESP International Journal of Emerging Multidisciplinary Research [ESP-IJEMR]  Volume 1, Issue 1: 12-20.

Abstract :

Quantum computing is one of the most innovative technological advancements in the 21st century. Quantum computers leverage the principles of superposition, entanglement or quantum interference, and can perform computations at exponentially faster rates than classical computers do with just binary bits. Major breakthroughs in machine learning, optimisation, cryptography and molecular simulation that are computationally intractable using classical computers are expected to stem from this paradigm shift. Recent breakthroughs in hybrid quantum- classical algorithms, error mitigation, and hardware have turned quantum computing from a theoretical curiosity to a novel tool for applied research. Based on our interview with them, they also discuss the top use cases and advancements in technology as well as challenges to come (and how real they are) when it comes to where quantum computing customers apply quantum compute accross different industries. Following an overview of the state-of-art quantum hardware platforms (e.g. superconducting qubits, trapped ions, quantum dots, photonic systems and arrays of neutral atoms), the work commences by recapping a set of foundational ideas from quantum mechanics that are relevant to computation. Then, it groups the application domains with respect to known algorithmic frameworks such as Quantum Annealing, Variational Quantum Eigensolver and QAOA etc., and compares their feasibility on NISQ devices. Special attention is being paid to topics where proof-of-concept demonstrations have shown promise, including quantum chemistry, materials discovery, financial modeling, supply chain optimization and cryptographic protocols. The paper also compiles research and industry efforts that are exploring the boundaries of quantum usefulness. In addition to building roadmaps towards fault-tolerant quantum computing, companies including IBM, Google, Rigetti, IonQ and D-Wave have also demonstrated algorithmic milestones that suggest they are close to being commercially viable. Quantum advantage is on its way, thanks to the integration of quantum computing with high-performance computing (HPC) ecosystems as well as advances in software interoperability, qubit communication and error correction.Despite remarkable progress, several challenges remain; among them are the degree of algorithmic maturity, issues related to scaling and decoherence. To attain fault tolerance requires millions of physical qubits and creative quantum error correction codes that can overcome noise at scale. Data representation, benchmarking norm and economic validation of quantum advantage are other open questions in the field. Domain-specific pilot projects and hybrid quantum–classical workflows do, however, demonstrate a practical path to adoption in the near term. After all, what quantum computing offers is a prfound shift in how computation could be conceived and realized, not merely a relatively minor improvement. Building on this consensus, our work contributes to a systematic assessment by considering established as well as high-potential applications when examining the economic impact of quantum computing in more detail.

References :

[1] Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.

[2] Abrams, D. S., & Lloyd, S. (1999). Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors. Physical Review Letters, 83(24), 5162–5165.

[3] Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.

[4] McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. C., & Yuan, X. (2020). Quantum computational chemistry. Reviews of Modern Physics, 92(1), 015003.

[5] Peruzzo, A., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.

[6] Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv:1411.4028.

[7] Nielsen, M. A., & Chuang, I. L. (2010). Quantum computation and quantum information. Cambridge University Press.

[8] Kandala, A., et al. (2017). Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature, 549(7671), 242–246.

[9] Moll, N., et al. (2018). Quantum optimization using variational algorithms on near-term quantum devices. Quantum Science and Technology, 3(3), 030503.

[10] Broughton, M., et al. (2020). TensorFlow Quantum: A software framework for quantum machine learning. arXiv:2003.02989.

[11] Schuld, M., & Petruccione, F. (2021). Machine learning with quantum computers. Springer.

[12] Huang, H. Y., et al. (2022). Quantum advantage in learning from experiments. Science, 376(6598), 1182–1186.

[13] Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018). Quantum circuit learning. Physical Review A, 98(3), 032309.

[14] Cerezo, M., et al. (2021). Variational quantum algorithms. Nature Reviews Physics, 3, 625–644.

[15] Endo, S., Benjamin, S. C., & Li, Y. (2018). Practical quantum error mitigation for near-future applications. Physical Review X, 8(3), 031027.

[16] IBM Quantum. (2023). IBM Quantum technology roadmap 2023–2026. IBM Research.

[17] Google Quantum AI. (2023). Scaling beyond quantum supremacy. Nature, 621(7979), 456–463.

[18] D-Wave Systems. (2023). Advantage2 quantum annealer technical overview. D-Wave White Paper.

[19] Rigetti Computing. (2023). Aspen-M series quantum processors: Architecture and performance. Rigetti Technical Report.

[20] IonQ Inc. (2023). IonQ roadmap and commercial applications. IonQ Technical Paper.

[21] Quantinuum. (2024). Hybrid quantum–classical computing advances. Quantinuum Technical Report.

[22] Microsoft Azure Quantum. (2024). Hybrid integration of quantum hardware and HPC systems. Microsoft Research.

[23] Bharti, K., et al. (2022). Noisy intermediate-scale quantum algorithms. Reviews of Modern Physics, 94(1), 015004.

[24] Childs, A. M., & van Dam, W. (2010). Quantum algorithms for algebraic problems. Reviews of Modern Physics, 82(1), 1–52.

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

[26] Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the 28th ACM Symposium on Theory of Computing, 212–219.

[27] Ekert, A. K. (1991). Quantum cryptography based on Bell’s theorem. Physical Review Letters, 67(6), 661–663.

[28] Pirandola, S., et al. (2020). Advances in quantum cryptography. Advances in Optics and Photonics, 12(4), 1012–1236.

[29] Gyongyosi, L., Imre, S., & Nguyen, H. V. (2019). A survey on quantum computing technology. Computer Science Review, 31, 51–71.

[30] Mohseni, M., Read, P., Neven, H., Boixo, S., Denchev, V., & Smelyanskiy, V. (2017). Commercialize quantum technologies sooner rather than later. Nature, 543(7644), 171–174.

[31] Cao, Y., et al. (2019). Quantum chemistry in the age of quantum computing. Chemical Reviews, 119(19), 10856–10915.

[32] Kjaergaard, M., et al. (2020). Superconducting qubits: Current state of play. Annual Review of Condensed Matter Physics, 11, 369–395.

[33] Monroe, C., & Kim, J. (2013). Scaling the ion trap quantum processor. Science, 339(6124), 1164–1169.

[34] Kim, M., et al. (2022). Hybrid quantum-classical computing architectures for optimization problems. IEEE Transactions on Quantum Engineering, 3, 3100812.

[35] Krantz, P., et al. (2019). A quantum engineer’s guide to superconducting qubits. Applied Physics Reviews, 6(2), 021318.

[36] Cross, A. W., Bishop, L. S., Smolin, J. A., & Gambetta, J. M. (2017). OpenQASM: A language for quantum circuits. IBM Research Report.

[37] Qiskit Development Team. (2023). Qiskit: Open-source SDK for quantum computing. IBM Quantum Open-Source Project.

[38] Amazon Web Services. (2024). Amazon Braket documentation: Quantum computing in the cloud. AWS Quantum Solutions Lab.

[39] McKinsey & Company. (2023). Quantum technology: The next frontier in drug discovery. McKinsey Insights Report.

[40] National Institute of Standards and Technology (NIST). (2022). Post-quantum cryptography standards and transitions. U.S. Department of Commerce.

Keywords :

Quantum Computers Quantum Algorithms Quantum Machine Learning Quantum Optimisation Quantum Simulation Variational Quantum Eigensolver (VQE) Quantum Approximate Optimisation Algorithm (QAOA) Quantum Annealing Quantum Cryptography NISQ Devices Faulttolerant Quantum Computing Quantum Advantage Hybrid Classical-Quantum Computing, Kllrclassifier For HHL Drug Discovery And Financial Problems Post-Quantum Cryptography Hardware Platforms/Gate-Based QC 1.