Mediaboss Marketing

Hadfield, S. et al. From the quantum approximate optimization algorithm to a quantum alternating operator Ansatz. Algorithms 12, 34 (2019).

Article MathSciNet Google Scholar

Cook, J., Eidenbenz, S. & Brtschi, A. The quantum alternating operator Ansatz on maximum k-Vertex cover. In IEEE International Conference on Quantum Computing and Engineering QCE20, 8392 (2020). https://doi.org/10.1109/QCE49297.2020.00021.

Wang, Z., Rubin, N. C., Dominy, J. M. & Rieffel, E. G. XY mixers: Analytical and numerical results for the quantum alternating operator ansatz. Phys. Rev. A 101, 012320 (2020).

Article ADS MathSciNet CAS Google Scholar

Farhi, E., Goldstone, J. & Gutmann, S. A Quantum Approximate Optimization Algorithm. arXiv preprint (2014). https://doi.org/10.48550/arXiv.1411.4028.

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

Farhi, E., Goldstone, J., Gutmann, S. & Sipser, M. Quantum computation by adiabatic evolution. arXiv preprint (2000). https://doi.org/10.48550/arXiv.quant-ph/0001106.

Kadowaki, T. & Nishimori, H. Quantum annealing in the transverse ising model. Phys. Rev. E 58, 53555363 (1998).

Article ADS CAS Google Scholar

Das, A. & Chakrabarti, B. K. Quantum annealing and analog quantum computation. Rev. Mod. Phys. 80, 1061 (2008).

Article ADS MathSciNet Google Scholar

Hauke, P., Katzgraber, H. G., Lechner, W., Nishimori, H. & Oliver, W. D. Perspectives of quantum annealing: methods and implementations. Rep. Prog. Phys. 83, 054401 (2020).

Article ADS CAS PubMed Google Scholar

Yarkoni, S., Raponi, E., Bck, T. & Schmitt, S. Quantum annealing for industry applications: Introduction and review. Rep. Prog. Phys. 85, 104001 (2022).

Article ADS MathSciNet Google Scholar

Morita, S. & Nishimori, H. Mathematical foundation of quantum annealing. J. Math. Phys. 49, 125210 (2008).

Article ADS MathSciNet Google Scholar

Santoro, G. E. & Tosatti, E. Optimization using quantum mechanics: Quantum annealing through adiabatic evolution. J. Phys. A: Math. Gen. 39, R393 (2006).

Article ADS MathSciNet CAS Google Scholar

Finnila, A. B., Gomez, M., Sebenik, C., Stenson, C. & Doll, J. D. Quantum annealing: A new method for minimizing multidimensional functions. Chem. Phys. Lett. 219, 343348 (1994).

Article ADS CAS Google Scholar

Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194198 (2011).

Article ADS CAS PubMed Google Scholar

Lanting, T. et al. Entanglement in a quantum annealing processor. Phys. Rev. X 4, 021041 (2014).

Google Scholar

Boixo, S., Albash, T., Spedalieri, F. M., Chancellor, N. & Lidar, D. A. Experimental signature of programmable quantum annealing. Nat. Commun. 4, 2067 (2013).

Article ADS PubMed Google Scholar

King, A. D. et al. Coherent quantum annealing in a programmable 2000-qubit Ising chain. Nat. Phys. 18, 13241328 (2022).

Article CAS Google Scholar

Chow, J. M. et al. Simple all-microwave entangling gate for fixed-frequency superconducting qubits. Phys. Rev. Lett. 107, 080502 (2011).

Article ADS PubMed Google Scholar

Chamberland, C., Zhu, G., Yoder, T. J., Hertzberg, J. B. & Cross, A. W. Topological and subsystem codes on low-degree graphs with flag qubits. Phys. Rev. X 10, 011022 (2020).

CAS Google Scholar

Tasseff, B. et al. On the emerging potential of quantum annealing hardware for combinatorial optimization. arXiv preprint (2022). https://doi.org/10.48550/arXiv.2210.04291.

Sanders, Y. R. et al. Compilation of fault-tolerant quantum heuristics for combinatorial optimization. PRX Quantum 1, 020312 (2020).

Article Google Scholar

Lotshaw, P. C. et al. Scaling quantum approximate optimization on near-term hardware. Sci. Rep. 12, 12388 (2022).

Article ADS CAS PubMed PubMed Central Google Scholar

Albash, T. & Lidar, D. A. Demonstration of a scaling advantage for a quantum annealer over simulated annealing. Phys. Rev. X 8, 031016 (2018).

CAS Google Scholar

King, A. D. et al. Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets. Nat. Commun. 12, 1113 (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

Farhi, E. & Harrow, A. W. Quantum supremacy through the quantum approximate optimization algorithm. arXiv preprint (2019). https://doi.org/10.48550/arXiv.1602.07674.

Brady, L. T., Baldwin, C. L., Bapat, A., Kharkov, Y. & Gorshkov, A. V. Optimal protocols in quantum annealing and quantum approximate optimization algorithm problems. Phys. Rev. Lett. 126, 070505 (2021).

Article ADS MathSciNet CAS PubMed Google Scholar

Willsch, M., Willsch, D., Jin, F., De Raedt, H. & Michielsen, K. Benchmarking the quantum approximate optimization algorithm. Quantum Inf. Process. 19, 197 (2020).

Article ADS MathSciNet Google Scholar

Sack, S. H. & Serbyn, M. Quantum annealing initialization of the quantum approximate optimization algorithm. Quantum 5, 491 (2021).

Article Google Scholar

Golden, J., Brtschi, A., Eidenbenz, S. & OMalley, D. Numerical Evidence for Exponential Speed-up of QAOA over Unstructured Search for Approximate Constrained Optimization. In IEEE International Conference on Quantum Computing and Engineering QCE23, 496505 (2023). https://doi.org/10.1109/QCE57702.2023.00063.

Golden, J., Brtschi, A., OMalley, D. & Eidenbenz, S. The Quantum Alternating Operator Ansatz for Satisfiability Problems. In IEEE International Conference on Quantum Computing and Engineering QCE23, 307312 (2023). https://doi.org/10.1109/QCE57702.2023.00042.

Binkowski, L., Komann, G., Ziegler, T. & Schwonnek, R. Elementary Proof of QAOA Convergence. arXiv preprint (2023). https://doi.org/10.48550/arXiv.2302.04968.

Lubinski, T. et al. Optimization Applications as Quantum Performance Benchmarks. arXiv preprint (2024). https://doi.org/10.48550/arXiv.2302.02278.

Pelofske, E., Golden, J., Brtschi, A., OMalley, D. & Eidenbenz, S. Sampling on NISQ Devices: Whos the Fairest One of All?. In IEEE International Conference on Quantum Computing and Engineering QCE21, 207217 (2021). https://doi.org/10.1109/qce52317.2021.00038.

Ushijima-Mwesigwa, H. et al. Multilevel combinatorial optimization across quantum architectures. ACM Trans. Quantum Comput. 2, 1:11:29 (2021).

Article MathSciNet Google Scholar

Streif, M. & Leib, M. Comparison of QAOA with quantum and simulated annealing. arXiv preprint (2019). https://doi.org/10.48550/arXiv.1901.01903.

Pelofske, E., Brtschi, A. & Eidenbenz, S. Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computers. In International Conference on High Performance Computing ISC HPC23, 240258 (2023). https://doi.org/10.1007/978-3-031-32041-5_13.

Suau, A. et al. Single-Qubit Cross Platform Comparison of Quantum Computing Hardware. In IEEE International Conference on Quantum Computing and Engineering QCE23, 13691377 (2023). https://doi.org/10.1109/QCE57702.2023.00155.

Pagano, G. et al. Quantum approximate optimization of the long-range ising model with a trapped-ion quantum simulator. Proc. Natl. Acad. Sci. 117, 2539625401 (2020).

Article ADS MathSciNet CAS PubMed PubMed Central Google Scholar

Weidenfeller, J. et al. Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware. Quantum 6, 870 (2022).

Article Google Scholar

Harrigan, M. P. et al. Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Nat. Phys. 17, 332336 (2021).

Article CAS Google Scholar

Herman, D. et al. Constrained optimization via quantum Zeno dynamics. Commun. Phys. 6, 219 (2023).

Article Google Scholar

Niroula, P. et al. Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer. Sci. Rep. 12, 17171 (2022).

Article ADS CAS PubMed PubMed Central Google Scholar

Zhou, L., Wang, S.-T., Choi, S., Pichler, H. & Lukin, M. D. Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Phys. Rev. X 10, 021067 (2020).

CAS Google Scholar

Basso, J., Farhi, E., Marwaha, K., Villalonga, B. & Zhou, L. The quantum approximate optimization algorithm at high depth for maxcut on large-girth regular graphs and the Sherrington-Kirkpatrick Model. In 17th Conference on the Theory of Quantum Computation, Communication and Cryptography TQC22 (2022). https://doi.org/10.4230/LIPICS.TQC.2022.7.

Wang, Z., Hadfield, S., Jiang, Z. & Rieffel, E. G. Quantum approximate optimization algorithm for MaxCut: A fermionic view. Phys. Rev. A 97, 022304 (2018).

Article ADS CAS Google Scholar

Crooks, G. E. Performance of the quantum approximate optimization algorithm on the maximum cut problem. arXiv preprint (2018). https://doi.org/10.48550/arXiv.1811.08419.

Guerreschi, G. G. & Matsuura, A. Y. QAOA for Max-Cut requires hundreds of qubits for quantum speed-up. Sci. Rep. 9, 6903 (2019).

Article ADS CAS PubMed PubMed Central Google Scholar

Marwaha, K. Local classical MAX-CUT algorithm outperforms p=2 QAOA on high-girth regular graphs. Quantum 5, 437 (2021).

Article Google Scholar

Hastings, M. B. Classical and quantum bounded depth approximation algorithms. Quantum Inf. Comput. 19, 11161140 (2019).

MathSciNet Google Scholar

Saleem, Z. H. Max independent set and quantum alternating operator Ansatz. Int. J. Quantum Inf. 18, 2050011 (2020).

Article MathSciNet Google Scholar

de la Grandrive, P. D. & Hullo, J.-F. Knapsack Problem variants of QAOA for battery revenue optimisation. arXiv preprint (2019). https://doi.org/10.48550/arXiv.1908.02210.

Farhi, E., Goldstone, J., Gutmann, S. & Zhou, L. The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size. Quantum 6, 759 (2022).

Article Google Scholar

Jiang, S., Britt, K. A., McCaskey, A. J., Humble, T. S. & Kais, S. Quantum annealing for prime factorization. Sci. Rep. 8, 17667 (2018).

Article ADS PubMed PubMed Central Google Scholar

Ji, X., Wang, B., Hu, F., Wang, C. & Zhang, H. New advanced computing architecture for cryptography design and analysis by D-Wave quantum annealer. Tsinghua Sci. Technol. 27, 751759 (2022).

Article Google Scholar

Dridi, R. & Alghassi, H. Prime factorization using quantum annealing and computational algebraic geometry. Sci. Rep. 7, 43048 (2017).

Article ADS CAS PubMed PubMed Central Google Scholar

Peng, W. et al. Factoring larger integers with fewer qubits via quantum annealing with optimized parameters. Sci. China Phys., Mech. Astron. 62, 60311 (2019).

Article ADS Google Scholar

Warren, R. H. Factoring on a quantum annealing computer. Quantum Inf. Comput. 19, 252261 (2019).

MathSciNet Google Scholar

Titiloye, O. & Crispin, A. Quantum annealing of the graph coloring problem. Discret. Optim. 8, 376384 (2011).

Article MathSciNet Google Scholar

Kwok, J. & Pudenz, K. Graph coloring with quantum annealing. arXiv preprint (2020). https://doi.org/10.48550/arXiv.2012.04470.

See the article here:
Short-depth QAOA circuits and quantum annealing on higher-order ising models | npj Quantum Information - Nature.com