IJEMR

Revolutionizing Technology: Exploring the Applications of Quantum Physics in Computing

© 2025 by IJEMR

Volume 1 Issue 1

Year of Publication : 2025

Author : Gokul G, Ishwarya

Citation :

Gokul G, Ishwarya, 2025. "Revolutionizing Technology: Exploring the Applications of Quantum Physics in Computing" ESP International Journal of Emerging Multidisciplinary Research [ESP-IJEMR]  Volume 1, Issue 1: 21-32.

Abstract :

Quantum mechanics, a subject that was once the province of only basic physics but which is now causing a major upheaval in computing. In this paper will be discussed how can quantum-physical principles, such as interference, entanglement and superposition are useful in order to try to build a new model for processing (quantum computing). We begin with the physics of quantum information and describe how qubits, not classical bits, are used to store and process information. 1 After that, we point out important algorithmic advances to factoring integers and search in high dimensions, and we stress how quantum simulation of many-body systems can achieve an advantage exceeding the best classical methods. We next discuss the hardware roadmap and trade-offs between coherence time, connectivity, gate fidelity and scalability. These consist of superconducting circuits, trapped-ion platforms, photonics devices, spin-"based qubits and emerging topological procedures. Applications are then reviewed such as secure quantum-key distribution and this cryptography becoming susceptible to cryptanalytic attack for classical encryption, materials design and plant simulation in chemistry and condensed matter physics, logistics modelling etc., in business and finance, new domain such as quantum machine learning or physical system simulation. The severe difficulties are criticized in a critical paragraph. These open problems include the algorithmic question of where does true quantum advantage lie, the need for software-hardware co-design and control imperatives, the engineering challenge of scaling to many qubit fault-tolerant systems, resorting to huge overheads with fault-tolerance approaches based on quantum error correction, and most importantly prevailing issues concerning decoherence and error accumulation. We also reflect on broader societal implications (social, economic and moral), including security shifts, work force readiness, disparities of access, dual use considerations as well as the worldwide global competitive context. We close with a proposed roadmap consisting of multiple tiers: near-term focus on application-specific demonstrations and noisy intermediate-scale quantum (NISQ) systems; medium-term pursuit of building small fault-tolerant modules and hybrid quantum-classical computing; and long-term vision of constructing large-scale fault-tolerant quantum processors that power disruptive applications.

References :

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

[2] Biamonte, J., et al. (2017). Quantum machine learning. Nature, 549(7671), 195–202.

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

[4] Shor, P. W. (1994). Algorithms for quantum computation: Discrete logarithms and factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science.

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

[6] Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press.

[7] Ladd, T. D., et al. (2010). Quantum computers. Nature, 464(7285), 45–53.

[8] Montanaro, A. (2016). Quantum algorithms: An overview. npj Quantum Information, 2, 15023.

[9] Harrow, A. W., Hassidim, A., & Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical Review Letters, 103(15), 150502.

[10] Farhi, E., et al. (2014). A quantum approximate optimization algorithm. arXiv:1411.4028.

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

[12] Lloyd, S. (1996). Universal quantum simulators. Science, 273(5278), 1073–1078.

[13] Devitt, S. J., et al. (2013). Quantum error correction for beginners. Reports on Progress in Physics, 76(7), 076001.

[14] Fowler, A. G., et al. (2012). Surface codes: Towards practical large-scale quantum computation. Physical Review A, 86(3), 032324.

[15] Brown, K. R., Kim, J., & Monroe, C. (2016). Co-designing a scalable quantum computer with trapped atomic ions. npj Quantum Information, 2, 16034.

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

[17] O’Brien, J. L., Furusawa, A., & Vučković, J. (2009). Photonic quantum technologies. Nature Photonics, 3(12), 687–695.

[18] Hanson, R., & Awschalom, D. D. (2008). Coherent manipulation of single spins in semiconductors. Nature, 453(7198), 1043–1049.

[19] Nayak, C., et al. (2008). Non-Abelian anyons and topological quantum computation. Reviews of Modern Physics, 80(3), 1083–1159.

[20] Gambetta, J. M., Chow, J. M., & Steffen, M. (2017). Building logical qubits in a superconducting quantum computing system. npj Quantum Information, 3, 2.

[21] Barends, R., et al. (2014). Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature, 508, 500–503.

[22] Briegel, H. J., & Raussendorf, R. (2001). Persistent entanglement in arrays of interacting particles. Physical Review Letters, 86(5), 910–913.

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

[24] Schuld, M., & Petruccione, F. (2018). Supervised Learning with Quantum Computers. Springer.

[25] Rebentrost, P., et al. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113(13), 130503.

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

[27] Gao, X., et al. (2021). Quantum advantage in learning from experiments. Science, 372(6539), 948–952.

[28] Arrazola, J. M., et al. (2021). Quantum circuits with photonic systems. Nature, 591(7848), 54–60.

[29] Yoshioka, N., & Hamazaki, R. (2023). Entanglement as a resource for computation. Physical Review Letters, 131(2), 021301.

[30] Childs, A. M., et al. (2018). Toward the first quantum simulation of chemistry on a quantum computer. PNAS, 115(38), 9456–9461.

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

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

[33] Huang, H.-Y., et al. (2022). Quantum advantage in learning from experiments. Nature, 603(7903), 424–429.

[34] Rieffel, E. G., & Polak, W. H. (2011). Quantum Computing: A Gentle Introduction. MIT Press.

[35] Chen, M., et al. (2021). Fault-tolerant quantum computing with superconducting qubits. Nature, 595, 383–387.

[36] Blais, A., et al. (2021). Circuit quantum electrodynamics. Reviews of Modern Physics, 93(2), 025005.

[37] Kok, P., et al. (2007). Linear optical quantum computing with photonic qubits. Reviews of Modern Physics, 79(1), 135–174.

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

[39] Castelvecchi, D. (2017). Quantum computers ready to leap out of the lab in 2017. Nature, 541, 9–10.

[40] Lekitsch, B., et al. (2017). Blueprint for a microwave trapped ion quantum computer. Science Advances, 3(2), e1601540.

Keywords :

Quantum Computing, Qubit, Superposition, Entanglement \ Quantum Algorithms, Quantum Simulation, Fault-Tolerance, Quantum Error Correction, \ Quantum Advantage, Quantum Hardware. The Paper Calls For An Orchestrated Strategic Funding In Hardware, Algorithms, Software And Governance To Ensure Quantum-Computing Is Delivered Responsibly And With Benefit.