Quantum Optics Research Prospects: Transformation Towards Faster Quantum Computing
Abstract
Advancements in quantum computing have become a primary focus in modern computer science. However, one of the major challenges in creating more powerful quantum computers is developing more stable and efficient qubits. In this context, research in quantum optics offers game-changing solutions. By leveraging quantum physics principles and quantum optics technology, this research aims to transform the quantum computing landscape by creating more stable and faster qubits. The goal of this study is to explore the potential of quantum optics in creating more stable and efficient qubits for quantum computing. This research method involves a combination of experimental and theoretical approaches. Data obtained from these experiments will be analyzed using advanced theoretical methods to understand the quantum properties of the produced qubits. The results indicate that the quantum optics approach can be key in creating more stable and faster qubits for quantum computing. Experiments have successfully demonstrated better control over qubits in photonic systems and compressed matter, producing qubits with higher reliability. Theoretical analysis also reveals a deeper understanding of the quantum properties of the produced qubits, opening the door for further development in this field. The conclusion of this research shows that quantum optics has great potential to transform quantum computing by creating more stable and faster qubits. By continuing to develop quantum optics technology and deepening the understanding of quantum properties of compressed matter and photonic systems, quantum computing can be taken to a new level.
Full text article
References
Alam, N., Thapliyal, K., Pathak, A., Sen, B., Verma, A., & Mandal, S. (2019). Bose-condensed optomechanical-like system and a Fabry–Perot cavity with one movable mirror: Quantum correlations from the perspectives of quantum optics. The European Physical Journal D, 73(7), 139. https://doi.org/10.1140/epjd/e2019-90448-x
Ali, S., Yue, T., & Abreu, R. (2022). When software engineering meets quantum computing. Communications of the ACM, 65(4), 84–88. https://doi.org/10.1145/3512340
Aoudni, Y., Kalra, A., Azhagumurugan, R., Ahmed, M. A., Wanjari, A. K., Singh, B., & Bhardwaj, A. (2023). Correction to: Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics. Optical and Quantum Electronics, 55(6), 518. https://doi.org/10.1007/s11082-023-04794-w
Bepari, K., Malik, S., Spannowsky, M., & Williams, S. (2021). Towards a quantum computing algorithm for helicity amplitudes and parton showers. Physical Review D, 103(7), 076020. https://doi.org/10.1103/PhysRevD.103.076020
Berke, C., Varvelis, E., Trebst, S., Altland, A., & DiVincenzo, D. P. (2022). Transmon platform for quantum computing challenged by chaotic fluctuations. Nature Communications, 13(1), 2495. https://doi.org/10.1038/s41467-022-29940-y
Bitzenbauer, P. (2021). Development of a Test Instrument to Investigate Secondary School Students’ Declarative Knowledge of Quantum Optics. European Journal of Science and Mathematics Education, 9(3), 57–79. https://doi.org/10.30935/scimath/10946
Bitzenbauer, P., Veith, J. M., Girnat, B., & Meyn, J.-P. (2022). Assessing Engineering Students’ Conceptual Understanding of Introductory Quantum Optics. Physics, 4(4), 1180–1201. https://doi.org/10.3390/physics4040077
Borish, V., & Lewandowski, H. J. (2023). Implementation and goals of quantum optics experiments in undergraduate instructional labs. Physical Review Physics Education Research, 19(1), 010117. https://doi.org/10.1103/PhysRevPhysEducRes.19.010117
Bruzewicz, C. D., Chiaverini, J., McConnell, R., & Sage, J. M. (2019). Trapped-ion quantum computing: Progress and challenges. Applied Physics Reviews, 6(2), 021314. https://doi.org/10.1063/1.5088164
Casado, A., Guerra, S., & Plácido, J. (2019). From Stochastic Optics to theWigner Formalism: The Role of the Vacuum Field in Optical Quantum Communication Experiments. Atoms, 7(3), 76. https://doi.org/10.3390/atoms7030076
Chan, K. S., & Chau, H. F. (2023). Reducing the impact of adaptive optics lag on optical and quantum communications rates from rapidly moving sources. AIP Advances, 13(5), 055201. https://doi.org/10.1063/5.0149695
Cherkas, S. L., & Kalashnikov, V. L. (2021). Wave optics of quantum gravity for massive particles. Physica Scripta, 96(11), 115001. https://doi.org/10.1088/1402-4896/ac14e5
Clemente, G., Crippa, A., & Jansen, K. (2022). Strategies for the determination of the running coupling of ( 2 + 1 )-dimensional QED with quantum computing. Physical Review D, 106(11), 114511. https://doi.org/10.1103/PhysRevD.106.114511
Cortes, C. L., Adhikari, S., Ma, X., & Gray, S. K. (2020). Accelerating quantum optics experiments with statistical learning. Applied Physics Letters, 116(18), 184003. https://doi.org/10.1063/1.5143786
Cour, B. R. L., & Williamson, M. C. (2020). Emergence of the Born rule in quantum optics. Quantum, 4, 350. https://doi.org/10.22331/q-2020-10-26-350
Darcie, A., Mitchell, M., Awan, K. M., Abdolahi, M., Hammood, M., Pfenning, A., Yan, X., Afifi, A., Witt, D., Lin, B., Gou, S., Jhoja, J., Wu, J., Taghavi, I., Weekes, D., Jaeger, N. A., Young, J., & Chrostowski, L. (2021). SiEPICfab: The Canadian silicon photonics rapid-prototyping foundry for integrated optics and quantum computing. In G. T. Reed & A. P. Knights (Eds.), Silicon Photonics XVI (p. 9). SPIE. https://doi.org/10.1117/12.2583432
Georgescu, I. (2020). Trapped ion quantum computing turns 25. Nature Reviews Physics, 2(6), 278–278. https://doi.org/10.1038/s42254-020-0189-1
Gulbahar, B. (2020). Theory of quantum path computing with Fourier optics and future applications for quantum supremacy, neural networks and nonlinear Schrödinger equations. Scientific Reports, 10(1), 10968. https://doi.org/10.1038/s41598-020-67364-0
Huang, H.-L., Wu, D., Fan, D., & Zhu, X. (2020). Superconducting quantum computing: A review. Science China Information Sciences, 63(8), 180501. https://doi.org/10.1007/s11432-020-2881-9
McCaskey, A. J., Lyakh, D. I., Dumitrescu, E. F., Powers, S. S., & Humble, T. S. (2020). XACC: A system-level software infrastructure for heterogeneous quantum–classical computing. Quantum Science and Technology, 5(2), 024002. https://doi.org/10.1088/2058-9565/ab6bf6
Pechal, M., Arrangoiz-Arriola, P., & Safavi-Naeini, A. H. (2018). Superconducting circuit quantum computing with nanomechanical resonators as storage. Quantum Science and Technology, 4(1), 015006. https://doi.org/10.1088/2058-9565/aadc6c
Romero, J., Babbush, R., McClean, J. R., Hempel, C., Love, P. J., & Aspuru-Guzik, A. (2018). Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz. Quantum Science and Technology, 4(1), 014008. https://doi.org/10.1088/2058-9565/aad3e4
Singh, S., Chawla, P., Sarkar, A., & Chandrashekar, C. M. (2021). Universal quantum computing using single-particle discrete-time quantum walk. Scientific Reports, 11(1), 11551. https://doi.org/10.1038/s41598-021-91033-5
Smyser, K. E., & Eaves, J. D. (2020). Singlet fission for quantum information and quantum computing: The parallel JDE model. Scientific Reports, 10(1), 18480. https://doi.org/10.1038/s41598-020-75459-x
Stetcu, I., Baroni, A., & Carlson, J. (2022). Variational approaches to constructing the many-body nuclear ground state for quantum computing. Physical Review C, 105(6), 064308. https://doi.org/10.1103/PhysRevC.105.064308
Walsh, J. A., Fenech, M., Tucker, D. L., Riegle-Crumb, C., & La Cour, B. R. (2022). Piloting a full-year, optics-based high school course on quantum computing. Physics Education, 57(2), 025010. https://doi.org/10.1088/1361-6552/ac3dc2
Xu, L., Yuan, S., Zeng, H., & Song, J. (2019). A comprehensive review of doping in perovskite nanocrystals/quantum dots: Evolution of structure, electronics, optics, and light-emitting diodes. Materials Today Nano, 6, 100036. https://doi.org/10.1016/j.mtnano.2019.100036
Xu, Z., Li, F., Wu, C., Ma, F., Zheng, Y., Yang, K., Chen, W., Hu, H., Guo, T., & Kim, T. W. (2019). Ultrathin electronic synapse having high temporal/spatial uniformity and an Al2O3/graphene quantum dots/Al2O3 sandwich structure for neuromorphic computing. NPG Asia Materials, 11(1), 18. https://doi.org/10.1038/s41427-019-0118-x
Authors
Copyright (c) 2024 Uwe Barroso, Mahon Nitin, Snyder Bradford

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.