Qualifying quantum approaches for hard industrial optimization problems. A case study in the field of smart-charging of electric vehicles
Open accessAbstract
In order to qualify quantum algorithms for industrial NP-Hard problems, comparing them to available polynomial approximate classical algorithms and not only to exact exponential ones is necessary. This is a great challenge as, in many cases, bounds on the reachable approximation ratios exist according to some highly-trusted conjectures of Complexity Theory. An interesting setup for such qualification is thus to focus on particular instances of these problems known to be “less difficult” than the worst-case ones and for which the above bounds can be outperformed: quantum algorithms should perform at least as well as the conventional approximate ones on these instances, up to very large sizes. We present a case study of such a protocol for two industrial problems drawn from the strongly developing field of smart-charging of electric vehicles. Tailored implementations of the Quantum Approximate Optimization Algorithm (QAOA) have been developed for both problems, and tested numerically with classical resources either by emulation of Pasqal’s Rydberg atom based quantum device or using Atos Quantum Learning Machine. In both cases, quantum algorithms exhibit the same approximation ratios as conventional approximation algorithms or improve them. These are very encouraging results, although still for instances of limited size as allowed by studies on classical computing resources. The next step will be to confirm them on larger instances, on actual devices, and for more complex versions of the problems addressed.
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Dalyac et al. utilized tailored implementations of QAOA to solve two problems arising in the field of smart charging electric vehicles, which can be mapped to Max-k-Cut and Maximum Independent Set problems, respectively.
“Dalyac et al. [35] utilized tailored implementations of QAOA to solve two problems arising in the field of smart charging electric vehicles, which can be mapped to Max-k-Cut and Maximum Independent Set problems, respectively [35].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [35]
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Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569 · see citing works
A Simplex Method for Function Minimization
Nelder, J. A., & Mead, R. (1965) · The Computer Journal · doi:10.1093/comjnl/7.4.308 · Cited by 28,905 · see citing works
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830 · see citing works
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830 · see citing works
Algorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713 · see citing works
Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey
Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. H. G. R. (1979) · Annals of discrete mathematics · doi:10.1016/s0167-5060(08)70356-x · Cited by 5,900 · see citing works
Goemans, M. X., & Williamson, D. P. (1995) · Journal of the ACM · doi:10.1145/227683.227684 · Cited by 3,587 · see citing works
Variational quantum algorithms
Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., et al. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00348-9 · Cited by 3,385 · see citing works
Quantum information with Rydberg atoms
Saffman, M. E., Walker, T. G., & Mølmer, K. (2010) · Reviews of Modern Physics · doi:10.1103/revmodphys.82.2313 · Cited by 2,796 · see citing works
(2007) · Springer eBooks · doi:10.1007/978-0-387-71107-2 · Cited by 2,696 · see citing works
QuTiP 2: A Python framework for the dynamics of open quantum systems
Johansson, J. R., Nation, P. D., & Nori, F. (2012) · Computer Physics Communications · doi:10.1016/j.cpc.2012.11.019 · Cited by 2,251 · see citing works
Some optimal inapproximability results
Håstad, J. (2001) · Journal of the ACM · doi:10.1145/502090.502098 · Cited by 1,902 · see citing works
Various optimizers for single‐stage production
Smith, W. E. (1956) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800030106 · Cited by 1,847 · see citing works
A Quantum Approximate Optimization Algorithm
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1411.4028 · Cited by 1,789 · see citing works
Gill, P. E., Murray, W., & Wright, M. H. (2019) · doi:10.1137/1.9781611975604 · Cited by 1,737 · see citing works
Optimization, approximation, and complexity classes
Papadimitriou, C. H., & Yannakakis, M. (1991) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(91)90023-x · Cited by 1,653 · see citing works
Strengths and Weaknesses of Quantum Computing
Bennett, C. H., Bernstein, E., Brassard, G., & Vazirani, U. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539796300933 · Cited by 1,470 · see citing works
Powell, M. J. D. (1994) · Advances in Optimization and Numerical Analysis · doi:10.1007/978-94-015-8330-5_4 · Cited by 1,369 · see citing works
Exploring the many-body localization transition in two dimensions
Choi, J.-Y., Hild, S., Zeiher, J., Schauß, P., Rubio-Abadal, A., Yefsah, T., Khemani, V., Huse, D. A., Bloch, I., & Gross, C. (2016) · Science · doi:10.1126/science.aaf8834 · Cited by 979 · see citing works
Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
Ibarra, Ó. H., & Kim, C. E. (1975) · Journal of the ACM · doi:10.1145/321906.321909 · Cited by 969 · see citing works
Zhou, L., Wang, S.-T., Choi, S., Pichler, H., & Lukin, M. D. (2020) · Physical Review X · doi:10.1103/physrevx.10.021067 · Cited by 913 · see citing works
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
Hadfield, S., Wang, Z., O’Gorman, B., Rieffel, E. G., Venturelli, D., & Biswas, R. (2019) · Algorithms · doi:10.3390/a12020034 · Cited by 772 · see citing works
Quantum computing with atomic qubits and Rydberg interactions: progress and challenges
Saffman, M. E. (2016) · Journal of Physics B Atomic Molecular and Optical Physics · doi:10.1088/0953-4075/49/20/202001 · Cited by 691 · see citing works
Quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms
Scholl, P., Schuler, M., Williams, H. J., Eberharter, A. A., Barredo, D., Schymik, K.-N., Lienhard, V., Henry, L.-P., Lang, T. C., Lahaye, T., et al. (2021) · Nature · doi:10.1038/s41586-021-03585-1 · Cited by 688 · see citing works
Algorithms for Scheduling Independent Tasks
Sahni, S. K. (1976) · Journal of the ACM · doi:10.1145/321921.321934 · Cited by 599 · see citing works
Optimal Inapproximability Results for MAX‐CUT and Other 2‐Variable CSPs?
Khot, S., Kindler, G., Mossel, E., & O’Donnell, R. (2007) · SIAM Journal on Computing · doi:10.1137/s0097539705447372 · Cited by 592 · see citing works
Differential Evolution as Applied to Electromagnetics
Rocca, P., Oliveri, G., & Massa, A. (2011) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2011.5773566 · Cited by 581 · see citing works
Quantum computing with neutral atoms
Henriet, L., Beguin, L., Signoles, A., Lahaye, T., Browaeys, A., Reymond, G.-O., & Jurczak, C. (2020) · Quantum · doi:10.22331/q-2020-09-21-327 · Cited by 550 · see citing works
Self-citationQuantum Computation by Adiabatic Evolution
Farhi, E., Goldstone, J., Gutmann, S., & Sipser, M. (2000) · arXiv · doi:10.48550/arxiv.quant-ph/0001106 · Cited by 536 · see citing works
Quantum optimization of maximum independent set using Rydberg atom arrays
Ebadi, S., Keesling, A., Cain, M., Wang, T. T., Levine, H., Bluvstein, D., Semeghini, G., Omran, A., Liu, J.-G., Samajdar, R., et al. (2022) · Science · doi:10.1126/science.abo6587 · Cited by 430 · see citing works
Mathematical foundation of quantum annealing
Morita, S., & Nishimori, H. (2008) · Journal of Mathematical Physics · doi:10.1063/1.2995837 · Cited by 381 · cited 3× · see citing works
“Quantum Annealing [9], the Quantum Adiabatic Algorithm [10], and the Quantum Ap-”
MethodInfluentialQuantum approximate optimization algorithm for MaxCut: A fermionic view
Wang, Z., Hadfield, S., Jiang, Z., & Rieffel, E. G. (2018) · Physical Review A · doi:10.1103/physreva.97.022304 · Cited by 353 · see citing works
Advances in Optimization and Numerical Analysis
Gómez, S., & Hennart, J.-P. (1994) · Springer eBooks · doi:10.1007/978-94-015-8330-5 · Cited by 327 · see citing works
25th Annual Symposium on Foundations of Computer Science
(1985) · IEEE Software · doi:10.1109/ms.1985.231038 · Cited by 304 · see citing works
Warm-starting quantum optimization
Egger, D. J., Mareček, J., & Woerner, S. (2021) · Quantum · doi:10.22331/q-2021-06-17-479 · Cited by 269 · see citing works
A quantum annealing architecture with all-to-all connectivity from local interactions
Lechner, W., Hauke, P., & Zoller, P. (2015) · Science Advances · doi:10.1126/sciadv.1500838 · Cited by 256 · see citing works
Self-citationWoeginger, G. J. (2000) · INFORMS journal on computing · doi:10.1287/ijoc.12.1.57.11901 · Cited by 238 · see citing works
Kolen, A. W. J., Lenstra, J. K., Papadimitriou, C. H., & Spieksma, F. C. R. (2007) · Naval Research Logistics (NRL) · doi:10.1002/nav.20231 · Cited by 224 · see citing works
Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem
Crooks, G. E. (2018) · arXiv · doi:10.48550/arxiv.1811.08419 · Cited by 158 · see citing works
Polynomial time approximation algorithms for machine scheduling: ten open problems
Schuurman, P., & Woeginger, G. J. (1999) · Journal of Scheduling · doi:10.1002/(sici)1099-1425(199909/10)2:5 · Cited by 135 · see citing works
Approximation Algorithms for Maximum Independent Set of Pseudo-Disks
Chan, T. M., & Har-Peled, S. (2012) · Discrete & Computational Geometry · doi:10.1007/s00454-012-9417-5 · Cited by 131 · see citing works
General Heuristics for Nonconvex Quadratically Constrained Quadratic ProgrammingPreprint
Park, J., & Boyd, S. (2017) · arXiv (Cornell University) · doi:10.4230/lipics.sea.2022.24 · Cited by 128 · see citing works
Improved approximation algorithms for MAX k-CUT and MAX BISECTION
FRIEZE, A. M., & Jerrum, M. (1995) · Lecture notes in computer science · doi:10.1007/3-540-59408-6_37 · Cited by 110 · see citing works
Multistart Methods for Quantum Approximate optimization
Shaydulin, R., Safro, I., & Larson, J. (2019) · 2019 IEEE High Performance Extreme Computing Conference (HPEC) · doi:10.1109/hpec.2019.8916288 · Cited by 110 · see citing works
Hybrid quantum-classical algorithms for approximate graph coloring
Bravyi, S. B., Kliesch, A., König, R., & Tang, E. (2022) · Quantum · doi:10.22331/q-2022-03-30-678 · Cited by 105 · see citing works
Practical optimization for hybrid quantum-classical algorithms
Guerreschi, G. G., & Smelyanskiy, M. (2017) · arXiv · doi:10.48550/arxiv.1701.01450 · Cited by 99 · see citing works
Classical and Quantum Bounded Depth Approximation Algorithms
Hastings, M. B. (2019) · arXiv · doi:10.48550/arxiv.1905.07047 · Cited by 96 · see citing works
Approximation Algorithms for the Job Interval Selection Problem and Related Scheduling Problems
Chuzhoy, J., Ostrovsky, R., & Rabani, Y. (2006) · Mathematics of Operations Research · doi:10.1287/moor.1060.0218 · Cited by 92 · see citing works
Viamontes, G. F., Markov, I. L., & Hayes, J. P. (2005) · Computing in Science & Engineering · doi:10.1109/mcse.2005.53 · Cited by 90 · see citing works
Brandao, F. G. S. L., Broughton, M., Farhi, E., Gutmann, S., & Neven, H. (2018) · arXiv · doi:10.48550/arxiv.1812.04170 · Cited by 88 · see citing works
On the Hardness of Approximating Max k-Cut and Its Dual
Kann, V., Khanna, S., Lagergren, J., & Panconesi, A. (1997) · Cited by 80 · see citing works
On Approximate Graph Colouring and MAX-k-CUT Algorithms Based on the θ-Function
de Klerk, E., Ṗasechnik, D. V., & Warners, J. P. (2004) · Journal of Combinatorial Optimization · doi:10.1023/b:joco.0000038911.67280.3f · Cited by 71 · see citing works
Classical and quantum bounded depth approximation algorithms
Hastings, M. B. (2019) · Quantum Information and Computation · doi:10.26421/qic19.13-14-3 · Cited by 65 · see citing works
A Robust PTAS for Maximum Weight Independent Sets in Unit Disk Graphs
Nieberg, T., Hurink, J. L., & Kern, W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30559-0_18 · Cited by 64 · see citing works
A PTAS for Minimizing the Total Weighted Completion Time on Identical Parallel Machines
Skutella, M., & Woeginger, G. J. (2000) · Mathematics of Operations Research · doi:10.1287/moor.25.1.63.15212 · Cited by 62 · see citing works
Interval scheduling and colorful independent sets
van Bevern, R., Mnich, M., Niedermeier, R., & Weller, M. (2014) · Journal of Scheduling · doi:10.1007/s10951-014-0398-5 · Cited by 58 · see citing works
Quantum Approximate Optimization With Parallelizable Gates
Lechner, W. (2020) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2020.3034798 · Cited by 57 · see citing works
Self-citationEvaluating Quantum Approximate Optimization Algorithm: A Case Study
Shaydulin, R., & Alexeev, Y. (2019) · 2019 Tenth International Green and Sustainable Computing Conference (IGSC) · doi:10.1109/igsc48788.2019.8957201 · Cited by 55 · see citing works
Quantum Optimization for Maximum Independent Set Using Rydberg Atom Arrays
Pichler, H., Wang, S.-T., Zhou, L., Choi, S., & Lukin, M. D. (2018) · arXiv · doi:10.48550/arxiv.1808.10816 · Cited by 35 · see citing works
Serret, M. F., Marchand, B., & Ayral, T. (2020) · Physical Review A · doi:10.1103/physreva.102.052617 · Cited by 32 · see citing works
Constrained quantum annealing of graph coloring
Kudo, K. (2018) · Physical Review A · doi:10.1103/physreva.98.022301 · Cited by 29 · see citing works
Semidefinite relaxations for parallel machine scheduling
Skutella, M. (2002) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1998.743498 · Cited by 25 · see citing works
Global solution of non-convex quadratically constrained quadratic programs
Elloumi, S., & Lambert, A. (2017) · Optimization methods & software · doi:10.1080/10556788.2017.1350675 · Cited by 24 · see citing works
Quantum computing using continuous-time evolution
Kendon, V. M. (2020) · Interface Focus · doi:10.1098/rsfs.2019.0143 · Cited by 24 · see citing works
Linear Reformulations of Integer Quadratic Programs
Billionnet, A., Elloumi, S., & Lambert, A. (2008) · Communications in computer and information science · doi:10.1007/978-3-540-87477-5_5 · Cited by 19 · see citing works
Quantum Learning MachinePreprint
Bang, J., Lim, J., Kim, M. S., & Lee, J. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0803.2976 · Cited by 15 · see citing works
An approximation algorithm for scheduling two parallel machines with capacity constraints
Yang, H., Ye, Y., & Zhang, J. (2003) · Discrete Applied Mathematics · doi:10.1016/s0166-218x(02)00601-7 · Cited by 15 · see citing works
Robustness to spontaneous emission of a variational quantum algorithm
Henriet, L. (2020) · Physical Review A · doi:10.1103/physreva.101.012335 · Cited by 12 · see citing works
Self-citationBounds on MAXCUT QAOA performance for p>1Preprint
Wurtz, J., & Love, P. J. (2020) · arXiv (Cornell University) · Cited by 11 · see citing works
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- [53]de Klerk E Pasechnik DV Warners JP On approximate graph colouring and max-k-cut algorithms based on the θ-function J Comb Optim 2004 8 3 267 294 10.1023/B:JOCO.0000038911.67280.3f · DOI
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Sorted by citations
Zhang, Q., Yan, J., Gao, H. O., & You, F. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100147 · Cited by 106 · see citing works
RETRACTED ARTICLE: A review on quantum computing and deep learning algorithms and their applications
Valdez, F., & Melín, P. (2022) · Soft Computing · doi:10.1007/s00500-022-07037-4 · Cited by 75 · see citing works
RetractedPulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays
Silvério, H., Grijalva, S., Dalyac, C., Leclerc, L., Karalekas, P. J., Shammah, N., Beji, M., Henry, L.-P., & Henriet, L. (2022) · Quantum · doi:10.22331/q-2022-01-24-629 · Cited by 62 · see citing works
Self-citationBenchmarking Quantum Coprocessors in an Application-Centric, Hardware-Agnostic, and Scalable Way
Martiel, S., Ayral, T., & Allouche, C. (2021) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2021.3090207 · Cited by 60 · see citing works
Implementation of Quantum Annealing: A Systematic Review
Yulianti, L. P., & Surendro, K. (2022) · IEEE Access · doi:10.1109/access.2022.3188117 · Cited by 61 · see citing works
Solving correlation clustering with QAOA and a Rydberg qudit system: a full-stack approach
Weggemans, J. R., Urech, A., Rausch, A., Spreeuw, R. J. C., Boucherie, R. J., Schreck, F., Schoutens, K., Minář, J., & Speelman, F. (2022) · Quantum · doi:10.22331/q-2022-04-13-687 · Cited by 41 · see citing works
Hybrid quantum ResNet for car classification and its hyperparameter optimization
Sagingalieva, A., Kordzanganeh, M., Kurkin, A., Melnikov, A., Kuhmistrov, D., Perelshtein, M. R., Melnikov, A., Skolik, A., & Dollen, D. V. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00123-2 · Cited by 54 · see citing works
Quantum feature maps for graph machine learning on a neutral atom quantum processor
Albrecht, B., Dalyac, C., Leclerc, L., Ortiz-Gutiérrez, L., Thabet, S., D'Arcangelo, M., Cline, J., Elfving, V. E., Lassablière, L., Silvério, H., et al. (2023) · Physical Review A · doi:10.1103/physreva.107.042615 · Cited by 38 · see citing works
Self-citationQuantum approximate optimization algorithm for qudit systems
Deller, Y., Schmitt, S., Lewenstein, M., Lenk, S., Federer, M., Jendrzejewski, F., Hauke, P., & Kasper, V. (2023) · Physical Review A · doi:10.1103/physreva.107.062410 · Cited by 40 · see citing works
A scientometric analysis of quantum driven innovations in intelligent transportation systems
Monika, & Sood, S. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109258 · Cited by 27 · see citing works
Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study
Gemeinhardt, F., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2024) · ACM Computing Surveys · doi:10.1145/3620668 · Cited by 22 · see citing works
“Dalyac et al. [35] utilized tailored implementations of QAOA to solve two problems arising in the field of smart charging electric vehicles, which can be mapped to Max-k-Cut and Maximum Independent Set problems, respectively [35].”
BackgroundQuantum pricing-based column-generation framework for hard combinatorial problems
Coelho, W. D. S., Henriet, L., & Henry, L.-P. (2023) · Physical Review A · doi:10.1103/physreva.107.032426 · Cited by 19 · see citing works
Self-citationFrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes
Ayanzadeh, R., Alavisamani, N., Das, P., & Qureshi, M. K. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575741 · Cited by 15 · see citing works
Dalyac, C., Henry, L.-P., Kim, M., Ahn, J., & Henriet, L. (2023) · Physical Review A · doi:10.1103/physreva.108.052423 · Cited by 15 · see citing works
Self-citationBlueprint for a digital-analog variational quantum eigensolver using Rydberg atom arrays
Michel, A., Grijalva, S., Henriet, L., Domain, C., & Browaeys, A. (2023) · Physical Review A · doi:10.1103/physreva.107.042602 · Cited by 14 · see citing works
Self-citationSawaya, N. P. D., Schmitz, A. T., & Hadfield, S. (2023) · Quantum · doi:10.22331/q-2023-09-14-1111 · Cited by 16 · see citing works
Quantum Optimization Algorithms in Operations Research: Methods, Applications, and ImplicationsPreprint
Klug, F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4854668 · Cited by 18 · see citing works
Variational quantum multiobjective optimization
Ekstrøm, L., Wang, H., & Schmitt, S. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023141 · Cited by 13 · see citing works
Reinforcement Learning Quantum Local Search
Liu, C.-Y., & Goan, H. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.10226 · Cited by 11 · see citing works
A Hybrid Quantum-Classical Approach to the Electric Mobility Problem
Veshchezerova, M., Somov, M., Bertsche, D., Limmer, S., Schmitt, S., Perelshtein, M. R., & Tripathi, A. J. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00078 · Cited by 9 · see citing works
Self-citationHybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising ProblemsPreprint
Liu, C.-Y., & Goan, H. (2024) · Research Square · doi:10.21203/rs.3.rs-4707131/v1 · Cited by 9 · see citing works
Quantifying the impact of precision errors on quantum approximate optimization algorithms
Quiroz, G., Titum, P., Lotshaw, P. C., Lougovski, P., Schultz, K., Dumitrescu, E., & Hen, I. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023240 · Cited by 10 · see citing works
Leclerc, L., Dalyac, C., Bendotti, P., Griset, R., Mikael, J., & Henriet, L. (2025) · Physical Review A · doi:10.1103/physreva.111.032611 · Cited by 7 · see citing works
Self-citationNeural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Vercellino, C., Viviani, P., Vitali, G., Scionti, A., Scarabosio, A., Terzo, O., Giusto, E., & Montrucchio, B. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00038 · Cited by 6 · see citing works
BBQ-mIS: A Parallel Quantum Algorithm for Graph Coloring Problems
Vercellino, C., Vitali, G., Viviani, P., Giusto, E., Scionti, A., Scarabosio, A., Terzo, O., & Montrucchio, B. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.10198 · Cited by 6 · see citing works
Utilizing modern computer architectures to solve mathematical optimization problems: A survey
Bernal, D. E., Laird, C. D., Lueg, L. R., Harwood, S. M., Trenev, D., & Venturelli, D. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108627 · Cited by 6 · see citing works
Davis, P. T., & Chandrasekar, T. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100554 · Cited by 5 · see citing works
Cazals, P., Sorondo, A., Onofre, V., Dalyac, C., Coelho, W. D. S., & Vitale, V. (2025) · Physical Review A · doi:10.1103/3sjz-gfmr · Cited by 5 · see citing works
Self-citationFrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot NodesPreprint
Ayanzadeh, R., Alavisamani, N., Das, P., & Qureshi, M. K. (2022) · Research Square · doi:10.21203/rs.3.rs-2252963/v1 · Cited by 5 · see citing works
NISQ-Ready Community Detection Based on Separation-Node Identification
Stein, J., Ott, D., Nüßlein, J., Bucher, D., Schönfeld, M., & Feld, S. (2023) · Mathematics · doi:10.3390/math11153323 · Cited by 4 · see citing works
Application benchmark for quantum optimization on electromobility use case
Federer, M., Müssig, D., Klaiber, S., Lässig, J., Bretschneider, P., & Lenk, S. (2022) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc55846.2022.10003292 · Cited by 4 · see citing works
Vercellino, C., Vitali, G., Viviani, P., Scionti, A., Scarabosio, A., Terzo, O., Giusto, E., & Montrucchio, B. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00058 · Cited by 3 · see citing works
Cazals, P., François, A., Henriet, L., Leclerc, L., Marin, M., Naghmouchi, M. Y., Coelho, W. D. S., Sikora, F., Vitale, V., Watrigant, R., et al. (2026) · Physical Review Applied · doi:10.1103/8gjv-ll1f · Cited by 2 · see citing works
Self-citationExploration of smart E-transportation system research aspects in the quantum realm
Agrewal, M., & Sood, S. K. (2026) · Environment Development and Sustainability · doi:10.1007/s10668-026-07374-6 · Cited by 1 · see citing works
Solving power grid optimization problems with Rydberg atoms
Bauer, N. M., Aydeniz, K. Y., Kokkas, E., & Siopsis, G. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-05020-0 · Cited by 1 · see citing works
PUSHO, a pulse-level variational quantum algorithm for Rydberg atom platforms
Leclerc, L., & Henriet, L. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00138
Self-citationImprove the Quantum Approximate Optimization Algorithm with Genetic Algorithm
Phuc, N. H. H., Ta, A. S., & Nguyễn, V. H. (2023) · International Symposium on Information and Communication Technology · doi:10.1145/3628797.3628818
Research on Key Technologies for Analyzing Quantum Computing with Deep Learning
Chen, K., Shi, L., He, C., Yu, Y., & Deng, K. (2024) · SPIN · doi:10.1142/s2010324724400083
The Art of Quantum Computing for Finance: Brief Overview and ProspectsPreprint
Mengara, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5373357
The Art of Quantum Computing for Finance: Brief Overview and ProspectsPreprint
Mengara, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5362526
Quantum algorithms for scheduling problems: a survey
Werner, T., & Ullinger, F. (2026) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-026-00494-y
Quantum algorithms for energy management optimization problems
Veshchezerova, M. (2022) · theses.fr (ABES) · doi:10.70675/f0d70a66z3066z4c81z8aa7zf9e72830f940 · Cited by 1 · see citing works
Self-citationQuantum many-body dynamics for combinatorial optimisation and machine learning
Dalyac, C. (2023) · theses.fr (ABES) · doi:10.70675/d8d31cecza123z48adz86b7ze8a30aaa7c58
Self-citationQUBO-Based Scheduling of Heterogeneous Quantum Computing Resources in Scale-Free Industrial Networks
Wang, Y., Wang, X., Pan, Y., & Gao, Q. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11616218
Tightiz, L., Dang, L. M., & Yang, H.-S. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105312
- Authors
- Dalyac, C., Henriet, L., Jeandel, E., Lechner, W., Perdrix, S., Porcheron, M., & Veshchezerova, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Pasqal (France) (FR)
- Universität Innsbruck (AT)
- Électricité de France (France) (FR)
- Corresponding author
- Henriet, Loïc
- ORCID
- Venue
- EPJ Quantum Technology
- Published
- 2021-05-17
- Volume
- 8
- Issue
- 1
- Pages
- 12-12
- ISSN
- 2196-0763 (ISSN-L)
- 2662-4400
- PMC
- PMC8550161
- OpenAlex
- W3163997203
- Cited by
- 46
- References
- 71
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 5 · 2023: 18 · 2022: 9 · 2021: 1
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- EPJ Quantum Technology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Austrian Science Fund (Y1067-N27, F7108-N38)
- Defense Advanced Research Projects Agency (HR001120C0068)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- quantum approximate optimization algorithm, quantum algorithms, NP-hard optimization, approximation ratio, electric vehicle charging scheduling
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