A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Yousra Mahmoudi, Nadjet Zioui, Hacène Belbachir, Mohamed Tadjine, Abdelmounaam Rezgui

Abstract


Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions.

 

Doi: 10.28991/ESJ-2023-07-01-020

Full Text: PDF


Keywords


Quantum Computing; Fourier Transform; Laplace Transform; Differential Equations; Systems of Equations; Optimization.

References


McMahon, D. (2007). Quantum computing explained. John Wiley & Sons, Hoboken, United States. doi:10.1002/9780470181386.

Zioui, N., Mahmoudi, Y., Mahmoudi, A., Tadjine, M., & Bentouba, S. (2021). A New Quantum-computing-based Algorithm for Robotic Arms and Rigid Bodies’ Orientation. Journal of Applied and Computational Mechanics, 7(3), 1836–1846. doi:10.22055/jacm.2021.37611.3048.

Nadjet, Z., Yousra, M., Aicha, M., Mohamed, T., & Said, B. A novel quantum-computing-based quaternions model for a robotic arm position. International Journal of Computational Intelligence in Control, 13(2), 71–77.

Fourier, J. B. J. (2009). The analytical theory of heat. The University Press, Harvard University, Cambridge, Massachusetts, United States. doi:10.1017/CBO9780511693205.

Akis, R., & Ferry, D. K. (2001). Quantum waveguide array generator for performing Fourier transforms: Alternate route to quantum computing. Applied Physics Letters, 79(17), 2823–2825. doi:10.1063/1.1413500.

Bowden, C. M., Chen, G., Diao, Z., & Klappenecker, A. (2002). The universality of the quantum Fourier transform in forming the basis of quantum computing algorithms. Journal of Mathematical Analysis and Applications, 274(1), 69–80. doi:10.1016/S0022-247X(02)00227-5.

Tyson, J. (2003). Operator-Schmidt decomposition of the quantum Fourier transform on ℂN1⊗ℂN2. Journal of Physics A: Mathematical and General, 36(24), 6813–6819. doi:10.1088/0305-4470/36/24/317.

Dorai, K., & Suter, D. (2005). Efficient implementations of the quantum Fourier transform: An experimental perspective. International Journal of Quantum Information, 3(2), 413–424. doi:10.1142/S0219749905000967.

Takahashi, Y., Kunihiro, N., & Ohta, K. (2007). The quantum fourier transform on a linear nearest neighbor architecture. Quantum Information and Computation, 7(4), 383–391. doi:10.26421/qic7.4-7.

Gyongyosi, L., & Imre, S. (2012). An Improvement in Quantum Fourier Transform. arXiv preprint, arXiv:1207.4464. doi:10.48550/arXiv.1207.4464.

Cao, Y., Peng, S. G., Zheng, C., & Long, G. L. (2011). Quantum Fourier transform and phase estimation in QUDIT system. Communications in Theoretical Physics, 55(5), 790–794. doi:10.1088/0253-6102/55/5/11.

Lacroix, G., & Semay, C. (2011). Lagrange-mesh calculations and Fourier transform. Physical Review E, 84(3), 036705. doi:10.1103/physreve.84.036705.

Van den Nest, M. (2013). Efficient classical simulations of Quantum Fourier transforms and normalizer circuits over Abelian groups. Quantum Information and Computation, 13(11&12), 1007–1037. doi:10.26421/qic13.11-12-7.

Ruiz-Perez, L., & Garcia-Escartin, J. C. (2017). Quantum arithmetic with the quantum Fourier transform. Quantum Information Processing, 16(6), 1–14. doi:10.1007/s11128-017-1603-1.

Kulkarni, A., & Kaushik, B. K. (2019). Spin-Torque-Based Quantum Fourier Transform. IEEE Transactions on Magnetics, 55(11), 1–8. doi:10.1109/TMAG.2019.2931278.

Grigoryan, A. M., & Agaian, S. S. (2019). Paired quantum Fourier transform with log2 N Hadamard gates. Quantum Information Processing, 18(7), 1–26. doi:10.1007/s11128-019-2322-6.

Nam, Y., Su, Y., & Maslov, D. (2020). Approximate quantum Fourier transform with O(nlog(n)) T gates. NPJ Quantum Information, 6(1), 26. doi:10.1038/s41534-020-0257-5.

Jaffe, A., Jiang, C., Liu, Z., Ren, Y., & Wu, J. (2020). Quantum Fourier analysis. Proceedings of the National Academy of Sciences of the United States of America, 117(20), 10715–10720. doi:10.1073/pnas.2002813117.

Martin, A., Lamata, L., Solano, E., & Sanz, M. (2020). Digital-analog quantum algorithm for the quantum Fourier transform. Physical Review Research, 2(1), 13012. doi:10.1103/PhysRevResearch.2.013012.

Sakk, E. (2021). Quantum Fourier Operators and Their Application. Real Perspective of Fourier Transforms and Current Developments in Superconductivity, Intechopen, London, United Kingdom. doi:10.5772/intechopen.94902.

Obada, A.-S. F., Hessian, H. A., Mohamed, A.-B. A., & Homid, A. H. (2013). Implementing discrete quantum Fourier transform via superconducting qubits coupled to a superconducting cavity. Journal of the Optical Society of America B, 30(5), 1178. doi:10.1364/josab.30.001178.

Obada, A. S. F., Hessian, H. A., Mohamed, A. B. A., & Homid, A. H. (2014). Efficient protocol of N-bit discrete quantum Fourier transform via transmon qubits coupled to a resonator. Quantum Information Processing, 13(2), 475–489. doi:10.1007/s11128-013-0664-z.

Freedman, M. H., & Wang, Z. (2007). Large quantum Fourier transforms are never exactly realized by braiding conformal blocks. Physical Review A, 75(3). doi:10.1103/physreva.75.032322.

Rötteler, M., & Beth, T. (2008). Representation-theoretical properties of the approximate quantum Fourier transform. Applicable Algebra in Engineering, Communication and Computing, 19(3), 177–193. doi:10.1007/s00200-008-0072-2.

Accardi, L., & Boukas, A. (2015). Fourier transform of random variables associated with the multi-dimensional Heisenberg Lie algebra. Proceedings of the American Mathematical Society, 143(9), 4095–4101. doi:10.1090/proc/12539.

Frank, R. (2022). The Discrete or Quantum Fourier Transform. Libretexts Chemistry, UC Davis Library, the California State University. Available online: https://chem.libretexts.org/@go/page/149085 (accessed on September 2022).

Muthukrishnan, A., & Stroud, C. R. (2002). Quantum fast Fourier transform using multilevel atoms. Journal of Modern Optics, 49(13), 2115–2127. doi:10.1080/09500340210123947.

Camps, D., Van Beeumen, R., & Yang, C. (2021). Quantum Fourier transform revisited. Numerical Linear Algebra with Applications, 28(1), e2331. doi:10.1002/nla.2331.

Asaka, R., Sakai, K., & Yahagi, R. (2020). Quantum circuit for the fast Fourier transform. Quantum Information Processing, 19(8), 1–20. doi:10.1007/s11128-020-02776-5.

Heszler, P. (2006). A comparative study of an analogue optical fourier transform quantum computer working in entanglement and non-entanglement mode. Fluctuation and Noise Letters, 6(4), 433– 446. doi:10.1142/S0219477506003598.

Barak, R., & Ben-Aryeh, Y. (2007). Quantum fast Fourier transform and quantum computation by linear optics. Journal of the Optical Society of America B, 24(2), 231. doi:10.1364/josab.24.000231.

Greco, J. A., Wagner, N. L., & Birge, R. R. (2012). Fourier transform holographic associative processors based on bacteriorhodopsin. International Journal of Unconventional Computing, 8(5–6), 433–457.

Li, H. S., Fan, P., Xia, H. ying, Song, S., & He, X. (2018). The quantum Fourier transform based on quantum vision representation. Quantum Information Processing, 17(12). doi:10.1007/s11128-018-2096-2.

Hen, I. (2014). Fourier-transforming with quantum annealers. Frontiers in Physics, 2, 1–7. doi:10.3389/fphy.2014.00044.

Greene, S. M., & Batista, V. S. (2017). Tensor-Train Split-Operator Fourier Transform (TT-SOFT) Method: Multidimensional Nonadiabatic Quantum Dynamics. Journal of Chemical Theory and Computation, 13(9), 4034–4042. doi:10.1021/acs.jctc.7b00608.

Grigoryan, A. M., & Agaian, S. S. (2020). New look on quantum representation of images: Fourier transform representation. Quantum Information Processing, 19(5). doi:10.1007/s11128-020-02643-3.

García-Ripoll, J. J. (2021). Quantum-inspired algorithms for multivariate analysis: from interpolation to partial differential equations. Quantum, 5, 431. doi:10.22331/q-2021-04-15-431.

Englefield, M. J. (1968). Solution of SchrɆdinger equation by Laplace transform. Journal of the Australian Mathematical Society, 8(3), 557–567. doi:10.1017/S1446788700006212.

Farmer, C. M. (1969). Laplace transform wave functions. International Journal of Quantum Chemistry, 3(6), 1027–1043. doi:10.1002/qua.560030621.

Fan, H. Y., Fu, L., & Wünsche, A. (2004). Quantum mechanical version of z-transform related to Eigenkets of boson creation operator. Communications in Theoretical Physics, 42(5), 675–680. doi:10.1088/0253-6102/42/5/675.

Tsaur, G. Y., & Wang, J. (2014). A universal Laplace-transform approach to solving Schrödinger equations for all known solvable models. European Journal of Physics, 35(1), 015006. doi:10.1088/0143-0807/35/1/015006.

Das, T., & Arda, A. (2015). Exact Analytical Solution of the-Dimensional Radial Schrödinger Equation with Pseudoharmonic Potential via Laplace Transform Approach. Advances in High Energy Physics, 137038. doi:10.1155/2015/13703.

Al-Omari, S. K. Q. (2017). On q-analogues of the Natural transform of certain q-Bessel functions and some application. Filomat, 31(9), 2587–2598. doi:10.2298/FIL1709587A.

Al-Omari, S. K. Q., Baleanu, D., & Purohit, S. D. (2018). Some results for Laplace-type integral operator in quantum calculus. Advances in Difference Equations, 2018(1), 1-10. doi:10.1186/s13662-018-1567-1.

Shehata, E. M., Faried, N., & El Zafarani, R. M. (2020). A general quantum Laplace transform. Advances in Difference Equations, 2020(1), 1-16. doi:10.1186/s13662-020-03070-5.

Alp, N., & Sarikaya, M. Z. (2023). q-Laplace transform on quantum integral. Kragujevac Journal of Mathematics, 47(1), 153-164.

Tassaddiq, A., Bhat, A. A., Jain, D. K., & Ali, F. (2020). On (p, q)-sumudu and (p, q)-Laplace transforms of the basic analogue of Aleph-function. Symmetry, 12(3). doi:10.3390/sym12030390.

de Castro, A. S. (2020). Frustating use of the Laplace transform for the quantum states of a particle in a box. Revista Brasileira de Ensino de Fisica, 42. doi:10.1590/1806-9126-RBEF-2020-0079.

Iyer, U. N., & McCune, T. C. (2003). Quantum differential operators on the quantum plane. Journal of Algebra, 260(2), 577–591. doi:10.1016/S0021-8693(03)00052-8.

Hereman, W. (2006). Symbolic computation of conservation laws of nonlinear partial differential equations in multi-dimensions. International Journal of Quantum Chemistry, 106(1), 278–299. doi:10.1002/qua.20727.

Berry, D. W. (2014). High-order quantum algorithm for solving linear differential equations. Journal of Physics A: Mathematical and Theoretical, 47(10). doi:10.1088/1751-8113/47/10/105301.

Berry, D. W., Childs, A. M., Ostrander, A., & Wang, G. (2017). Quantum Algorithm for Linear Differential Equations with Exponentially Improved Dependence on Precision. Communications in Mathematical Physics, 356(3), 1057–1081. doi:10.1007/s00220-017-3002-y.

Khater, M. M. A., Seadawy, A. R., & Lu, D. (2018). Bifurcations of solitary wave solutions for (two and three)-dimensional nonlinear partial differential equation in quantum and magnetized plasma by using two different methods. Results in Physics, 9, 142–150. doi:10.1016/j.rinp.2018.02.010.

Srivastava, S., & Sundararaghavan, V. (2019). Box algorithm for the solution of differential equations on a quantum Annealer. Physical Review A, 99(5). doi:10.1103/PhysRevA.99.052355.

Childs, A. M., & Liu, J. P. (2020). Quantum Spectral Methods for Differential Equations. Communications in Mathematical Physics, 375(2), 1427–1457. doi:10.1007/s00220-020-03699-z.

Xin, T., Wei, S., Cui, J., Xiao, J., Arrazola, I., Lamata, L., Kong, X., Lu, D., Solano, E., & Long, G. (2020). Quantum algorithm for solving linear differential equations: Theory and experiment. Physical Review A, 101(3), 32307. doi:10.1103/PhysRevA.101.032307.

Kyriienko, O., Paine, A. E., & Elfving, V. E. (2021). Solving nonlinear differential equations with differentiable quantum circuits. Physical Review A, 103(5), 52416. doi:10.1103/PhysRevA.103.052416.

Zanger, B., Mendl, C. B., Schulz, M., & Schreiber, M. (2021). Quantum algorithms for solving ordinary differential equations via classical integration methods. Quantum, 5, 502. doi:10.22331/Q-2021-07-13-502.

Kumar, N., Shaikh, A. A., Mahato, S. K., & Bhunia, A. K. (2021). Applications of new hybrid algorithm based on advanced cuckoo search and adaptive Gaussian quantum behaved particle swarm optimization in solving ordinary differential equations. Expert Systems with Applications, 172, 114646. doi:10.1016/j.eswa.2021.114646.

Ayoola, E. O. (2001). On convergence of one-step schemes for weak solutions of quantum stochastic differential equations. Acta Applicandae Mathematicae, 67(1), 19–58. doi:10.1023/A:1010675803824.

Lindsay, J. M., & Skalski, A. G. (2007). On quantum stochastic differential equations. Journal of Mathematical Analysis and Applications, 330(2), 1093–1114. doi:10.1016/j.jmaa.2006.07.105.

Vissers, G., & Bouten, L. (2019). Implementing quantum stochastic differential equations on a quantum computer. Quantum Information Processing, 18(5), 1–15. doi:10.1007/s11128-019-2272-z.

Kubo, K., Nakagawa, Y. O., Endo, S., & Nagayama, S. (2021). Variational quantum simulations of stochastic differential equations. Physical Review A, 103(5), 52425. doi:10.1103/PhysRevA.103.052425.

An, D., Linden, N., Liu, J.-P., Montanaro, A., Shao, C., & Wang, J. (2021). Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance. Quantum, 5, 481. doi:10.22331/q-2021-06-24-481.

Cao, Y., Papageorgiou, A., Petras, I., Traub, J., & Kais, S. (2013). Quantum algorithm and circuit design solving the Poisson equation. New Journal of Physics, 15. doi:10.1088/1367-2630/15/1/013021.

Arrazola, J. M., Kalajdzievski, T., Weedbrook, C., & Lloyd, S. (2019). Quantum algorithm for nonhomogeneous linear partial differential equations. Physical Review A, 100(3), 032306. doi:10.1103/PhysRevA.100.032306.

Wang, S., Wang, Z., Li, W., Fan, L., Wei, Z., & Gu, Y. (2020). Quantum fast Poisson solver: the algorithm and complete and modular circuit design. Quantum Information Processing, 19(6), 1-25. doi:10.1007/s11128-020-02669-7.

Szkopek, T., Roychowdhury, V., Yablonovitch, E., & Abrams, D. S. (2005). Eigenvalue estimation of differential operators with a quantum algorithm. Physical Review A, 72(6), 062318. doi:10.1103/physreva.72.062318.

Alanazi, A. M., Ebaid, A., Alhawiti, W. M., & Muhiuddin, G. (2020). The Falling Body Problem in Quantum Calculus. Frontiers in Physics, 8(43), 1-5. doi:10.3389/fphy.2020.00043.

Harrow, A. W., Hassidim, A., & Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical Review Letters, 103(15), 150502. doi:10.1103/PhysRevLett.103.150502.

Cai, X. D., Weedbrook, C., Su, Z. E., Chen, M. C., Gu, M., Zhu, M. J., Li, L., Liu, N. Le, Lu, C. Y., & Pan, J. W. (2013). Experimental quantum computing to solve systems of linear equations. Physical Review Letters, 110(23), 230501. doi:10.1103/PhysRevLett.110.230501.

Lee, Y., Joo, J., & Lee, S. (2019). Hybrid quantum linear equation algorithm and its experimental test on IBM Quantum Experience. Scientific Reports, 9(1). doi:10.1038/s41598-019-41324-9.

Bernstein, D. J., & Yang, B.-Y. (2018). Asymptotically Faster Quantum Algorithms to Solve Multivariate Quadratic Equations. Lecture Notes in Computer Science, 487–506. doi:10.1007/978-3-319-79063-3_23.

Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing- STOC ’96. doi:10.1145/237814.237866.

Chang, C. C., Gambhir, A., Humble, T. S., & Sota, S. (2019). Quantum annealing for systems of polynomial equations. Scientific Reports, 9(1). doi:10.1038/s41598-019-46729-0.

Sellier, J.M., Dimov, I. (2016). On a Quantum Algorithm for the Resolution of Systems of Linear Equations. Recent Advances in Computational Optimization. Studies in Computational Intelligence, 610. Springer, Cham, Switzerland. doi:10.1007/978-3-319-21133-6_3.

Li, K., Dai, H., Jing, F., Gao, M., Xue, B., Wang, P., & Zhang, M. (2021). Quantum Algorithms for Solving Linear Regression Equation. Journal of Physics: Conference Series, 1738(1), 012063. doi:10.1088/1742-6596/1738/1/012063.

Han, K. H., & Kim, J. H. (2002). Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation, 6(6), 580–593. doi:10.1109/TEVC.2002.804320.

Jiao, L., Li, Y., Gong, M., & Zhang, X. (2008). Quantum-inspired immune clonal algorithm for global optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38(5), 1234–1253. doi:10.1109/TSMCB.2008.927271.

Baritompa, W. P., Bulger, D. W., & Wood, G. R. (2005). Grover’s quantum algorithm applied to global optimization. SIAM Journal on Optimization, 15(4), 1170–1184. doi:10.1137/040605072.

Liu, Y., & Koehler, G. J. (2010). Using modifications to Grover’s Search algorithm for quantum global optimization. European Journal of Operational Research, 207(2), 620–632. doi:10.1016/j.ejor.2010.05.039.

Zheng, T., & Yamashiro, M. (2010). Solving flow shop scheduling problems by quantum differential evolutionary algorithm. International Journal of Advanced Manufacturing Technology, 49(5–8), 643–662. doi:10.1007/s00170-009-2438-4.

Zhisheng, Z. (2010). Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system. Expert Systems with Applications, 37(2), 1800–1803. doi:10.1016/j.eswa.2009.07.042.

Silva, M. H. Da, & Schirru, R. (2011). Optimization of nuclear reactor core fuel reload using the new Quantum PBIL. Annals of Nuclear Energy, 38(2–3), 610–614. doi:10.1016/j.anucene.2010.09.010.

Karimi, K., Dickson, N. G., Hamze, F., Amin, M. H. S., Drew-Brook, M., Chudak, F. A., Bunyk, P. I., MacReady, W. G., & Rose, G. (2012). Investigating the performance of an adiabatic quantum optimization processor. Quantum Information Processing, 11(1), 77–88. doi:10.1007/s11128-011-0235-0.

Layeb, A. (2013). A hybrid quantum inspired harmony search algorithm for 0-1 optimization problems. Journal of Computational and Applied Mathematics, 253, 14–25. doi:10.1016/j.cam.2013.04.004.

Shang, R., Jiao, L., Ren, Y., Li, L., & Wang, L. (2014). Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization. Soft Computing, 18(4), 743–756. doi:10.1007/s00500-013-1085-8.

Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint, arXiv: 1411.4028. doi:10.48550/arXiv.1411.4028.

Farhi, E., Goldstone, J., & Gutmann, S. (2015). A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem. arXiv preprint, arXiv: 1412.6062. doi:10.48550/arXiv.1412.6062.

Wecker, D., Hastings, M. B., & Troyer, M. (2016). Training a quantum optimizer. Physical Review A, 94(2), 22309. doi:10.1103/PhysRevA.94.022309.

Zouache, D., Nouioua, F., & Moussaoui, A. (2016). Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems. Soft Computing, 20(7), 2781–2799. doi:10.1007/s00500-015-1681-x.

Hen, I., & Spedalieri, F. M. (2016). Quantum Annealing for Constrained Optimization. Physical Review Applied, 5(3), 34007. doi:10.1103/PhysRevApplied.5.034007.

Ranjbar, M., Macready, W. G., Clark, L., & Gaitan, F. (2016). Generalized Ramsey numbers through adiabatic quantum optimization. Quantum Information Processing, 15(9), 3519–3542. doi:10.1007/s11128-016-1363-3.

Palittapongarnpim, P., Wittek, P., Zahedinejad, E., Vedaie, S., & Sanders, B. C. (2017). Learning in quantum control: High-dimensional global optimization for noisy quantum dynamics. Neurocomputing, 268, 116–126. doi:10.1016/j.neucom.2016.12.087.

Chiang, C. L. (2017). Quantum-behaved Particle Swarm Optimization for Power Economic Dispatch Problem of Units with Multiple Fuel Option. European Journal of Engineering and Technology Research, 2(12), 11–16. doi:10.24018/ejeng.2017.2.12.492.

Kaur, A., Kaur, S., & Dhiman, G. (2018). A quantum method for dynamic nonlinear programming technique using Schrödinger equation and Monte Carlo approach. Modern Physics Letters B, 32(30), 1850374. doi:10.1142/S0217984918503748.

Sayed, G. I., Darwish, A., & Hassanien, A. E. (2019). Quantum multiverse optimization algorithm for optimization problems. Neural Computing and Applications, 31(7), 2763–2780. doi:10.1007/s00521-017-3228-9.

Shao, C. (2019). Fast variational quantum algorithms for training neural networks and solving convex optimizations. Physical Review A, 99(4), 42325. doi:10.1103/PhysRevA.99.042325.

Ajagekar, A., & You, F. (2019). Quantum computing for energy systems optimization: Challenges and opportunities. Energy, 179, 76–89. doi:10.1016/j.energy.2019.04.186.

Greplova, E. (2020). Solving optimization tasks in condensed matter. Nature Machine Intelligence, 2(10), 557–558. doi:10.1038/s42256-020-00240-8.

Miatto, F. M., & Quesada, N. (2020). Fast optimization of parameterized quantum optical circuits. Quantum, 4, 366. doi:10.22331/Q-2020-11-30-366.

van Apeldoorn, J., Gilyén, A., Gribling, S., & de Wolf, R. (2020). Convex optimization using quantum oracles. Quantum, 4, 220. doi:10.22331/q-2020-01-13-220.

Gao, L., Liu, R., Wang, F., Wu, W., Bai, B., Yang, S., & Yao, L. (2020). An Advanced Quantum Optimization Algorithm for Robot Path Planning. Journal of Circuits, Systems and Computers, 29(8), 2050122. doi:10.1142/S0218126620501224.

Alexandru, C. M., Bridgett-Tomkinson, E., Linden, N., MacManus, J., Montanaro, A., & Morris, H. (2020). Quantum speedups of some general-purpose numerical optimisation algorithms. Quantum Science and Technology, 5(4), 45014. doi:10.1088/2058-9565/abb003.

Kaveh, A., Kamalinejad, M., & Arzani, H. (2020). Quantum evolutionary algorithm hybridized with Enhanced colliding bodies for optimization. Structures, 28, 1479–1501. doi:10.1016/j.istruc.2020.09.079.

Tang, D., Liu, Z., Zhao, J., Dong, S., & Cai, Y. (2020). Memetic quantum evolution algorithm for global optimization. Neural Computing and Applications, 32(13), 9299–9329. doi:10.1007/s00521-019-04439-8.

El Gaily, S., & Imre, S. (2021). Constrained Quantum Optimization Algorithm. 20th International Symposium INFOTEH-JAHORINA. doi:10.1109/infoteh51037.2021.9400679.

Khan, A.T., Cao, X., Li, S., Hu, B., & Katsikis, V. N. (2021). Quantum beetle antennae search: a novel technique for the constrained portfolio optimization problem. Science China Information Sciences, 64(5), 1–14. doi:10.1007/s11432-020-2894-9.

Warren, R. H. (2021). A benchmark for quantum optimization: the traveling salesman. Quantum Information and Computation, 21(7 & 8), 557–562. https://doi:10.26421/qic21.7-8-2.

Wang, Y., & Wang, W. (2021). Quantum-inspired differential evolution with grey-wolf optimizer for 0-1 knapsack problem. Mathematics, 9(11), 1233. doi:10.3390/math9111233.

Deng, W., Shang, S., Cai, X., Zhao, H., Zhou, Y., Chen, H., & Deng, W. (2021). Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization. Knowledge-Based Systems, 224, 107080. doi:10.1016/j.knosys.2021.107080.

Hadfield, S., Wang, Z., O’Gorman, B., Rieffel, E. G., Venturelli, D., & Biswas, R. (2019). From the quantum approximate optimization algorithm to a quantum alternating operator ansatz. Algorithms, 12(2), 34. doi:10.3390/a12020034.

Pagano, G., Bapat, A., Becker, P., Collins, K. S., De, A., Hess, P. W., Kaplan, H. B., Kyprianidis, A., Tan, W. L., Baldwin, C., Brady, L. T., Deshpande, A., Liu, F., Jordan, S., Gorshkov, A. V., & Monroe, C. (2020). Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator. Proceedings of the National Academy of Sciences, 117(41), 25396–25401. doi:10.1073/pnas.2006373117.

Li, L., Fan, M., Coram, M., Riley, P., & Leichenauer, S. (2020). Quantum optimization with a novel Gibbs objective function and ansatz architecture search. Physical Review Research, 2(2), 23074. doi:10.1103/PhysRevResearch.2.023074.

Moussa, C., Calandra, H., & Dunjko, V. (2020). To quantum or not to quantum: Towards algorithm selection in near-term quantum optimization. Quantum Science and Technology, 5(4), 44009. doi:10.1088/2058-9565/abb8e5.

Egger, D. J., Marecek, J., & Woerner, S. (2021). Warm-starting quantum optimization. Quantum, 5, 479–499. doi:10.22331/q-2021-06-17-479.

Harrigan, M. P., Sung, K. J., Neeley, M., Satzinger, K. J., Arute, F., Arya, K., Atalaya, J., Bardin, J. C., Barends, R., Boixo, S., Broughton, M., Buckley, B. B., Buell, D. A., Burkett, B., Bushnell, N., Chen, Y., Chen, Z., Ben Chiaro, Collins, R., … Babbush, R. (2021). Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Nature Physics, 17(3), 332–336. doi:10.1038/s41567-020-01105-y.

Finnila, A. B., Gomez, M. A., Sebenik, C., Stenson, C., & Doll, J. D. (1994). Quantum annealing: A new method for minimizing multidimensional functions. Chemical Physics Letters, 219(5–6), 343–348. doi:10.1016/0009-2614(94)00117-0.

Biswas, R., Jiang, Z., Kechezhi, K., Knysh, S., Mandrà, S., O’Gorman, B., Perdomo-Ortiz, A., Petukhov, A., Realpe-Gómez, J., Rieffel, E., Venturelli, D., Vasko, F., & Wang, Z. (2017). A NASA perspective on quantum computing: Opportunities and challenges. Parallel Computing, 64, 81–98. doi:10.1016/j.parco.2016.11.002.

Vyskocil, T., & Djidjev, H. (2019). Embedding equality constraints of optimization problems into a quantum annealer. Algorithms, 12(4), 77. doi:10.3390/A12040077.

Sao, M., Watanabe, H., Musha, Y., & Utsunomiya, A. (2019). Application of digital annealer for faster combinatorial optimization. Fujitsu Scientific and Technical Journal, 55(2), 45–51.

Ohzeki, M. (2020). Breaking limitation of quantum annealer in solving optimization problems under constraints. Scientific Reports, 10(1), 3126. doi:10.1038/s41598-020-60022-5.

Abel, S., Blance, A., & Spannowsky, M. (2022). Quantum optimization of complex systems with a quantum annealer. Physical Review A, 106(4). doi:10.1103/physreva.106.042607.

Izquierdo, Z. G., Hen, I., & Albash, T. (2021). Testing a Quantum Annealer as a Quantum Thermal Sampler. ACM Transactions on Quantum Computing, 2(2), 1–20. doi:10.1145/3464456.

Gastaldo, P., Ridella, S., & Zunino, R. (2006). Prospects of quantum-classical optimization for digital design. Applied Mathematics and Computation, 179(2), 581–595. doi:10.1016/j.amc.2005.11.129.

Hardy, Y., & Steeb, W. H. (2010). Genetic algorithms and optimization problems in quantum computing. International Journal of Modern Physics C, 21(11), 1359–1375. doi:10.1142/S0129183110015890.

McDonald, R. B., & Katzgraber, H. G. (2013). Genetic braid optimization: A heuristic approach to compute quasiparticle braids. Physical Review B, 87(5). doi:10.1103/physrevb.87.054414.

Li Sheng-Hao, Wu Xiao-Bing, Huang Chong-Fu, & Wang Hong-Lei. (2014). Optimization of the projected entangled pair state algorithm for quantum systems. Acta Physica Sinica, 63(14), 140501. doi:10.7498/aps.63.140501.

Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. M., Cross, A., Egger, D. J., Filipp, S., Fuhrer, A., Gambetta, J. M., Ganzhorn, M., Kandala, A., Mezzacapo, A., Müller, P., Riess, W., Salis, G., Smolin, J., Tavernelli, I., & Temme, K. (2018). Quantum optimization using variational algorithms on near-term quantum devices. Quantum Science and Technology, 3(3), 30503. doi:10.1088/2058-9565/aab822.

Jayashree, H. V., Patil, S., & Agrawal, V. K. (2018). Design Approaches for Resource and Performance Optimization of Reversible BCD Addition and Unified BCD Addition/Subtraction Circuits. Journal of Circuits, Systems and Computers, 27(3), 1850048. doi:10.1142/S0218126618500482.

Shaydulin, R., Ushijima-Mwesigwa, H., Negre, C. F. A., Safro, I., Mniszewski, S. M., & Alexeev, Y. (2019). A hybrid approach for solving optimization problems on small quantum computers. Computer, 52(6), 18–26. doi:10.1109/MC.2019.2908942.

Möller, M., & Vuik, C. (2019). A conceptual framework for quantum accelerated automated design optimization. Microprocessors and Microsystems, 66, 67–71. doi:10.1016/j.micpro.2019.02.009.

Li, Y., Tian, M., Liu, G., Peng, C., & Jiao, L. (2020). Quantum optimization and quantum learning: A survey. IEEE Access, 8, 23568–23593. doi:10.1109/ACCESS.2020.2970105.

Wang, B., Hu, F., Yao, H., & Wang, C. (2020). Prime factorization algorithm based on parameter optimization of Ising model. Scientific Reports, 10(1). doi:10.1038/s41598-020-62802-5.

Liu, J., Sun, T., Luo, Y., Yang, S., Cao, Y., & Zhai, J. (2020). An echo state network architecture based on quantum logic gate and its optimization. Neurocomputing, 371, 100–107. doi:10.1016/j.neucom.2019.09.002.

Liao, Y., Hsieh, M. H., & Ferrie, C. (2021). Quantum optimization for training quantum neural networks. arXiv preprint, arXiv:2103.17047. doi:10.48550/arXiv.2103.17047

Medvidović, M., & Carleo, G. (2021). Classical variational simulation of the Quantum Approximate Optimization Algorithm. NPJ Quantum Information, 7(1), 1–7. doi:10.1038/s41534-021-00440-z.

Xiao, Y., Nazarian, S., & Bogdan, P. (2021). A stochastic quantum program synthesis framework based on Bayesian optimization. Scientific Reports, 11(1), 1–9. doi:10.1038/s41598-021-91035-3.

Petersson, D. (2020) Quantum computing challenges and opportunities. TechTarget, SearchCIO. Available online: https://searchcio.techtarget.com/feature/Quantum-computing-challenges-and-opportunities (accessed on 25 July 2022)

de Leon, N. P., Itoh, K. M., Kim, D., Mehta, K. K., Northup, T. E., Paik, H., Palmer, B. S., Samarth, N., Sangtawesin, S., & Steuerman, D. W. (2021). Materials challenges and opportunities for quantum computing hardware. Science, 372(6539), 6539. doi:10.1126/science.abb2823.

Corcoles, A. D., Kandala, A., Javadi-Abhari, A., McClure, D. T., Cross, A. W., Temme, K., Nation, P. D., Steffen, M., & Gambetta, J. M. (2020). Challenges and Opportunities of Near-Term Quantum Computing Systems. Proceedings of the IEEE, 108(8), 1338–1352. doi:10.1109/jproc.2019.2954005.

Fazilat, M., Zioui, N., & St-Arnaud, J. (2022). A novel quantum model of forward kinematics based on quaternion/Pauli gate equivalence: Application to a six-jointed industrial robotic arm. Results in Engineering, 14(100402). doi:10.1016/j.rineng.2022.100402.


Full Text: PDF

DOI: 10.28991/ESJ-2023-07-01-020

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Yousra Mahmoudi, Nadjet Zioui, Hacene Belbachir, Mohamed Tadjine, Abdelmounaam Rezgui