Constrained quantum optimization for extractive summarization on a trapped-ion quantum laptop
[ad_1]
Arute, F. et al. Quantum supremacy utilizing a programmable superconducting processor. Nature 574, 505–510. https://doi.org/10.1038/s41586-019-1666-5 (2019).
Wu, Y. et al. Robust quantum computational benefit utilizing a superconducting quantum processor. Phys. Rev. Lett.https://doi.org/10.1103/physrevlett.127.180501 (2021).
Madsen, L. S. et al. Quantum computational benefit with a programmable photonic processor. Nature 606, 75–81. https://doi.org/10.1038/s41586-022-04725-x (2022).
Sbihi, A. & Eglese, R. W. Combinatorial optimization and inexperienced logistics. 4OR 5, 99–116. https://doi.org/10.1007/s10288-007-0047-3 (2007).
Eskandarpour, M., Dejax, P., Miemczyk, J. & Péton, O. Sustainable provide chain community design: An optimization-oriented assessment. Omega 54, 11–32. https://doi.org/10.1016/j.omega.2015.01.006 (2015).
Kennedy, J. P. et al. Utility of combinatorial chemistry science on trendy drug discovery. J. Comb. Chem. 10, 345–354. https://doi.org/10.1021/cc700187t (2008).
Soler-Dominguez, A., Juan, A. A. & Kizys, R. A survey on monetary functions of metaheuristics. ACM Comput. Surv.https://doi.org/10.1145/3054133 (2017).
Wang, Z., Hadfield, S., Jiang, Z. & Rieffel, E. G. Quantum approximate optimization algorithm for MaxCut: A fermionic view. Phys. Rev. A 97, 022304. https://doi.org/10.1103/PhysRevA.97.022304 (2018).
Zhou, L., Wang, S.-T., Choi, S., Pichler, H. & Lukin, M. D. Quantum approximate optimization algorithm: Efficiency, mechanism, and implementation on near-term gadgets. Phys. Rev. X 10, 021067. https://doi.org/10.1103/PhysRevX.10.021067 (2020).
Crooks, G. E. Efficiency of the quantum approximate optimization algorithm on the utmost lower downside. https://doi.org/10.48550/ARXIV.1811.08419 (2018).
Shaydulin, R., Marwaha, Ok., Wurtz, J. & Lotshaw, P. C. QAOAKit: A toolkit for reproducible research, utility, and verification of the QAOA. In 2021 IEEE/ACM Second Worldwide Workshop on Quantum Computing Software program (QCS). https://doi.org/10.1109/qcs54837.2021.00011 (IEEE, 2021).
Wurtz, J. & Love, P. MaxCut quantum approximate optimization algorithm efficiency ensures for (p > 1). Phys. Rev. A 103, 042612. https://doi.org/10.1103/PhysRevA.103.042612 (2021).
Harrigan, M. P. et al. Quantum approximate optimization of non-planar graph issues on a planar superconducting processor. Nat. Phys 17, 332–336 (2021).
Shaydulin, R. & Galda, A. Error mitigation for deep quantum optimization circuits by leveraging downside symmetries. In IEEE Worldwide Convention on Quantum Computing and Engineering, 291–300. https://doi.org/10.1109/QCE52317.2021.00046 (2021).
Ebadi, S. et al. Quantum optimization of most impartial set utilizing rydberg atom arrays. Science 376, 1209–1215. https://doi.org/10.1126/science.abo6587 (2022).
Bravyi, S., Kliesch, A., Koenig, R. & Tang, E. Hybrid quantum-classical algorithms for approximate graph coloring. Quantum 6, 678. https://doi.org/10.22331/q-2022-03-30-678 (2022).
Filippova, Ok., Surdeanu, M., Ciaramita, M. & Zaragoza, H. Firm-oriented extractive summarization of economic information. In Proceedings of the twelfth Convention of the European Chapter of the ACL (EACL 2009), 246–254. (Affiliation for Computational Linguistics, Athens, Greece, 2009) https://doi.org/10.5555/1609067.1609094.
Bhattacharya, P., Poddar, S., Rudra, Ok., Ghosh, Ok. & Ghosh, S. Incorporating area information for extractive summarization of authorized case paperwork. In Proceedings of the Eighteenth Worldwide Convention on Synthetic Intelligence and Regulation, 22–31 (Affiliation for Computing Equipment, New York, NY, USA, 2021).
McDonald, R. A research of world inference algorithms in multi-document summarization. In Advances in Data Retrieval, ECIR’07 557–564 (Springer, Berlin, 2007). https://doi.org/10.5555/1763653.1763720.
Shaydulin, R., Hadfield, S., Hogg, T. & Safro, I. Classical symmetries and the quantum approximate optimization algorithm. Quantum Inf. Course of.https://doi.org/10.1007/s11128-021-03298-4 (2021).
Shaydulin, R., Lotshaw, P. C., Larson, J., Ostrowski, J. & Humble, T. S. Parameter switch for quantum approximate optimization of weighted MaxCut. https://doi.org/10.48550/ARXIV.2201.11785 (2022).
Hadfield, S. et al. From the quantum approximate optimization algorithm to a quantum alternating operator ansatz. Algorithms 12, 34. https://doi.org/10.3390/a12020034 (2019).
Wang, Z., Rubin, N. C., Dominy, J. M. & Rieffel, E. G. XY-mixers: Analytical and numerical outcomes for the quantum alternating operator ansatz. Phys. Rev. A.https://doi.org/10.1103/physreva.101.012320 (2020).
Liu, X. et al. Layer VQE: A variational strategy for combinatorial optimization on noisy quantum computer systems. IEEE Trans. Quantum Eng. 3, 1–20. https://doi.org/10.1109/tqe.2021.3140190 (2022).
Farhi, E., Goldstone, J. & Gutmann, S. A quantum approximate optimization algorithm. https://doi.org/10.48550/ARXIV.1411.4028(2014).
Hogg, T. & Portnov, D. Quantum optimization. Inf. Sci. 128, 181–197. https://doi.org/10.1016/s0020-0255(00)00052-9 (2000).
Shaydulin, R., Safro, I. & Larson, J. Multistart strategies for quantum approximate optimization. In IEEE Excessive Efficiency Excessive Computing Convention, 1–8. https://doi.org/10.1109/hpec.2019.8916288 (2019).
Wurtz, J. & Lykov, D. Mounted-angle conjectures for the quantum approximate optimization algorithm on common MaxCut graphs. Phys. Rev. A 104, 052419. https://doi.org/10.1103/PhysRevA.104.052419 (2021).
Sherrington, D. & Kirkpatrick, S. Solvable mannequin of a spin-glass. Phys. Rev. Lett. 35, 1792–1796. https://doi.org/10.1103/PhysRevLett.35.1792 (1975).
Farhi, E., Goldstone, J., Gutmann, S. & Zhou, L. The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick mannequin at infinite measurement. https://doi.org/10.48550/ARXIV.1910.08187 (2019).
Basso, J., Farhi, E., Marwaha, Ok., Villalonga, B. & Zhou, L. The quantum approximate optimization algorithm at excessive depth for MaxCut on large-girth common graphs and the Sherrington–Kirkpatrick mannequin. https://doi.org/10.48550/arXiv.2110.14206 (2021).
Xu, J., Gan, Z., Cheng, Y. & Liu, J. Discourse-aware neural extractive textual content summarization. In Proceedings of the 58th Annual Assembly of the Affiliation for Computational Linguistics, 5021–5031 (Affiliation for Computational Linguistics, On-line, 2020) https://doi.org/10.18653/v1/2020.acl-main.451.
Zhong, M. et al. Extractive summarization as textual content matching. In Proceedings of the 58th Annual Assembly of the Affiliation for Computational Linguistics, 6197–6208 (Affiliation for Computational Linguistics, On-line, 2020) https://doi.org/10.18653/v1/2020.acl-main.552.
Liu, Y. Effective-tune BERT for extractive summarization. https://doi.org/10.48550/ARXIV.1903.10318 (2019).
Doc summarization on cnn/day by day mail. https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail. Accessed 6 Oct 2022.
Hermann, Ok. M. et al. Instructing machines to learn and comprehend. In Proceedings of the twenty eighth Worldwide Convention on Neural Data Processing Methods—Quantity 1, NIPS’15, 1693–1701 (MIT Press, Cambridge, MA, USA, 2015).
Reimers, N. & Gurevych, I. Sentence-BERT: Sentence embeddings utilizing Siamese BERT-networks. In Proceedings of the 2019 Convention on Empirical Strategies in Pure Language Processing and the ninth Worldwide Joint Convention on Pure Language Processing (EMNLP-IJCNLP), 3982–3992 (Affiliation for Computational Linguistics, Hong Kong, China, 2019) https://doi.org/10.18653/v1/D19-1410.
Aizawa, A. An information-theoretic perspective of tf-idf measures. Inf. Course of. Handle 39, 45–65. https://doi.org/10.1016/S0306-4573(02)00021-3 (2003).
Aleksandrowicz, G. et al. Qiskit: An open-source framework for quantum computing. https://doi.org/10.5281/zenodo.2562111 (2019).
Sivarajah, S. et al. t|ket>: A retargetable compiler for NISQ gadgets. Quantum Sci. Technol. 6, 014003. https://doi.org/10.1088/2058-9565/ab8e92 (2020).
Bärtschi, A. & Eidenbenz, S. Deterministic preparation of Dicke states. In Fundamentals of Computation Concept 126–139 (Springer Worldwide Publishing, 2019). https://doi.org/10.1007/978-3-030-25027-0_9.
Mukherjee, C. S., Maitra, S., Gaurav, V. & Roy, D. On precise preparation of Dicke state on a quantum laptop. https://doi.org/10.48550/ARXIV.2007.01681 (2020).
Aktar, S., Bärtschi, A., Badawy, A.-H.A. & Eidenbenz, S. A divide-and-conquer strategy to Dicke state preparation. IEEE Trans. Quantum Eng. 3, 1–16. https://doi.org/10.1109/TQE.2022.3174547 (2022).
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R. & Neven, H. Barren plateaus in quantum neural community coaching landscapes. Nat. Commun.https://doi.org/10.1038/s41467-018-07090-4 (2018).
Holmes, Z., Sharma, Ok., Cerezo, M. & Coles, P. J. Connecting ansatz expressibility to gradient magnitudes and barren plateaus. PRX Quantum.https://doi.org/10.1103/prxquantum.3.010313 (2022).
Pino, J. M. et al. Demonstration of the trapped-ion quantum CCD laptop structure. Nature 592, 209–213. https://doi.org/10.1038/s41586-021-03318-4 (2021).
Ryan-Anderson, C. et al. Realization of real-time fault-tolerant quantum error correction. Phys. Rev. X 11, 041058. https://doi.org/10.1103/PhysRevX.11.041058 (2021).
Otterbach, J. S. et al. Unsupervised machine studying on a hybrid quantum laptop. https://doi.org/10.48550/ARXIV.1712.05771 (2017).
Lacroix, N. et al. Bettering the efficiency of deep quantum optimization algorithms with steady gate units. PRX Quantum 1, 110304. https://doi.org/10.1103/PRXQuantum.1.020304 (2020).
Baker, J. S. & Radha, S. Ok. Wasserstein answer high quality and the quantum approximate optimization algorithm: A portfolio optimization case research. https://doi.org/10.48550/ARXIV.2202.06782 (2022).
Qiang, X. et al. Massive-scale silicon quantum photonics implementing arbitrary two-qubit processing. Nat. Photon. 12, 534–539. https://doi.org/10.1038/s41566-018-0236-y (2018).
Willsch, M., Willsch, D., Jin, F., Raedt, H. D. & Michielsen, Ok. Benchmarking the quantum approximate optimization algorithm. Quantum Inf. Course of.https://doi.org/10.1007/s11128-020-02692-8 (2020).
Abrams, D. M., Didier, N., Johnson, B. R., da Silva, M. P. & Ryan, C. A. Implementation of XY entangling gates with a single calibrated pulse. Nat. Electron. 3, 744–750. https://doi.org/10.1038/s41928-020-00498-1 (2020).
Bengtsson, A. et al. Improved success chance with larger circuit depth for the quantum approximate optimization algorithm. Phys. Rev. Appl.https://doi.org/10.1103/physrevapplied.14.034010 (2020).
Earnest, N., Tornow, C. & Egger, D. J. Pulse-efficient circuit transpilation for quantum functions on cross-resonance-based {hardware}. Phys. Rev. Res.https://doi.org/10.1103/physrevresearch.3.043088 (2021).
Santra, G. C., Jendrzejewski, F., Hauke, P. & Egger, D. J. Squeezing and quantum approximate optimization. https://doi.org/10.48550/ARXIV.2205.10383 (2022).
Kakkar, A., Larson, J., Galda, A. & Shaydulin, R. Characterizing error mitigation by symmetry verification in QAOA. arXiv:2204.05852 (2022).
Shaydulin, R., Ushijima-Mwesigwa, H., Safro, I., Mniszewski, S. & Alexeev, Y. Community group detection on small quantum computer systems. Adv. Quantum Technol. 2, 1900029. https://doi.org/10.1002/qute.201900029 (2019).
Ushijima-Mwesigwa, H. et al. Multilevel combinatorial optimization throughout quantum architectures. ACM Trans. Quantum Comput. 2, 1–29. https://doi.org/10.1145/3425607 (2021).
Golden, J., Bärtschi, A., O’Malley, D. & Eidenbenz, S. QAOA-based truthful sampling on NISQ gadgets. https://doi.org/10.48550/ARXIV.2101.03258 (2021).
Bartschi, A. & Eidenbenz, S. Grover mixers for QAOA: Shifting complexity from mixer design to state preparation. In 2020 IEEE Worldwide Convention on Quantum Computing and Engineering (QCE) (IEEE, 2020) https://doi.org/10.1109/qce49297.2020.00020.
Pelofske, E., Golden, J., Bärtschi, A., O’Malley, D. & Eidenbenz, S. Sampling on nisq gadgets: ”who’s the fairest one in every of all?”. In 2021 IEEE Worldwide Convention on Quantum Computing and Engineering (QCE), 207–217. https://doi.org/10.1109/QCE52317.2021.00038 (2021).
Fingerhuth, M., Babej, T. & Ing, C. A quantum alternating operator ansatz with exhausting and tender constraints for lattice protein folding. https://doi.org/10.48550/ARXIV.1810.13411 (2018).
Pagano, G. et al. Quantum approximate optimization of the long-range ising mannequin with a trapped-ion quantum simulator. Proc. Natl. Acad. Sci 117, 25396–25401. https://doi.org/10.1073/pnas.2006373117 (2020).
Herman, D. et al. A survey of quantum computing for finance. https://doi.org/10.48550/ARXIV.2201.02773 (2022).
Pistoia, M. et al. Quantum machine studying for finance iccad particular session paper. In 2021 IEEE/ACM Worldwide Convention On Laptop Aided Design (ICCAD), 1–9. https://doi.org/10.1109/ICCAD51958.2021.9643469 (2021).
Devlin, J., Chang, M.-W., Lee, Ok. & Toutanova, Ok. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Convention of the North American Chapter of the Affiliation for Computational Linguistics: Human Language Applied sciences, Quantity 1 (Lengthy and Brief Papers), 4171–4186 (Affiliation for Computational Linguistics, Minneapolis, Minnesota, 2019) https://doi.org/10.18653/v1/N19-1423.
Vaswani, A. et al. Consideration is all you want. In Advances in Neural Data Processing Methods Vol. 30 (eds Guyon, I. et al.) (Curran Associates, Inc., 2017).
Wang, C., Li, M. & Smola, A. J. Language fashions with transformers. https://doi.org/10.48550/ARXIV.1904.09408 (2019).
Achananuparp, P., Hu, X. & Shen, X. The analysis of sentence similarity measures. In Knowledge Warehousing and Data Discovery (eds Tune, I.-Y. et al.) 305–316 (Springer, 2008). https://doi.org/10.1007/978-3-540-85836-2_29.
Zheng, H. & Lapata, M. Sentence centrality revisited for unsupervised summarization. In Proceedings of the 57th Annual Assembly of the Affiliation for Computational Linguistics, 6236–6247 (Affiliation for Computational Linguistics, Florence, Italy, 2019) https://doi.org/10.18653/v1/P19-1628.
Powell, M. J. D. A direct search optimization technique that fashions the target and constraint features by linear interpolation. In Advances in Optimization and Numerical Evaluation (eds Gomez, S. & Hennart, J.-P.) 51–67 (Springer, 1994). https://doi.org/10.1007/978-94-015-8330-5_4.
Powell, M. Direct search algorithms for optimization calculations. Acta Numer. 7, 287–336. https://doi.org/10.1017/S0962492900002841 (1998).
Quantinuum system mannequin H1 product information sheet [retrieved 08/30/2022]. https://www.quantinuum.com/merchandise/h1.
Lin, C.-Y. ROUGE: A package deal for computerized analysis of summaries. In Textual content Summarization Branches Out 74–81 (Affiliation for Computational Linguistics, 2004).
[ad_2]
Source_link
