Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Crossing The Gap Using Variational Quantum Eigensolver: A Comparative Study (2405.11687v2)

Published 19 May 2024 in quant-ph

Abstract: Within the evolving domain of quantum computational chemistry, the Variational Quantum Eigensolver (VQE) has been developed to explore not only the ground state but also the excited states of molecules. In this study, we compare the performance of Variational Quantum Deflation (VQD) and Subspace-Search Variational Quantum Eigensolver (SSVQE) methods in determining the low-lying excited states of $LiH$. Our investigation reveals that while VQD exhibits a slight advantage in accuracy, SSVQE stands out for its efficiency, allowing the determination of all low-lying excited states through a single parameter optimization procedure. We further evaluate the effectiveness of optimizers, including Gradient Descent (GD), Quantum Natural Gradient (QNG), and Adam optimizer, in obtaining $LiH$'s first excited state, with the Adam optimizer demonstrating superior efficiency in requiring the fewest iterations. Moreover, we propose a novel approach combining Folded Spectrum VQE (FS-VQE) with either VQD or SSVQE, enabling the exploration of highly excited states. We test the new approaches for finding all three $H_4$'s excited states. Folded Spectrum SSVQE (FS-SSVQE) can find all three highly excited states near $-1.0$ Ha with only one optimizing procedure, but the procedure converges slowly. In contrast, although Folded spectrum VQD (FS-VQD) gets highly excited states with individual optimizing procedures, the optimizing procedure converges faster.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. Y. Cao, J. Romero, J. P. Olson, M. Degroote, P. D. Johnson, M. Kieferová, I. D. Kivlichan, T. Menke, B. Peropadre, N. P. D. Sawaya, S. Sim, L. Veis, and A. Aspuru-Guzik, “Quantum chemistry in the age of quantum computing,” Chemical Reviews, vol. 119, no. 19, pp. 10 856–10 915, 2019, pMID: 31469277. [Online]. Available: https://doi.org/10.1021/acs.chemrev.8b00803
  2. O. Higgott, D. Wang, and S. Brierley, “Variational Quantum Computation of Excited States,” Quantum, vol. 3, p. 156, Jul. 2019. [Online]. Available: https://doi.org/10.22331/q-2019-07-01-156
  3. K. M. Nakanishi, K. Mitarai, and K. Fujii, “Subspace-search variational quantum eigensolver for excited states,” Phys. Rev. Res., vol. 1, p. 033062, Oct 2019. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.1.033062
  4. S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin, and X. Yuan, “Quantum computational chemistry,” Rev. Mod. Phys., vol. 92, p. 015003, Mar 2020. [Online]. Available: https://link.aps.org/doi/10.1103/RevModPhys.92.015003
  5. H. H. S. Chan, N. Fitzpatrick, J. Segarra-Martí, M. J. Bearpark, and D. P. Tew, “Molecular excited state calculations with adaptive wavefunctions on a quantum eigensolver emulation: reducing circuit depth and separating spin states,” Phys. Chem. Chem. Phys., vol. 23, pp. 26 438–26 450, 2021. [Online]. Available: http://dx.doi.org/10.1039/D1CP02227J
  6. L. Cadi Tazi and A. J. W. Thom, “Folded spectrum vqe: A quantum computing method for the calculation of molecular excited states,” Journal of Chemical Theory and Computation, vol. 20, no. 6, pp. 2491–2504, 2024, pMID: 38492238. [Online]. Available: https://doi.org/10.1021/acs.jctc.3c01378
  7. N. Innan, M. A.-Z. Khan, and M. Bennai, “Quantum computing for electronic structure analysis: Ground state energy and molecular properties calculations,” Materials Today Communications, vol. 38, p. 107760, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352492823024510
  8. V. Marx, “Biology begins to tangle with quantum computing,” Nature Methods, vol. 18, p. 715–719, Jul. 2021. [Online]. Available: https://www.nature.com/articles/s41592-021-01199-z
  9. B. A. Cordier, N. P. D. Sawaya, G. G. Guerreschi, and S. K. McWeeney, “Biology and medicine in the landscape of quantum advantages,” Journal of The Royal Society Interface, vol. 19, no. 196, p. 20220541, Nov. 2022. [Online]. Available: https://royalsocietypublishing.org/doi/10.1098/rsif.2022.0541
  10. M. Meth, J. F. Haase, J. Zhang, C. Edmunds, L. Postler, A. Steiner, A. J. Jena, L. Dellantonio, R. Blatt, P. Zoller, T. Monz, P. Schindler, C. Muschik, and M. Ringbauer, “Simulating 2d lattice gauge theories on a qudit quantum computer,” no. arXiv:2310.12110, Oct. 2023, arXiv:2310.12110 [quant-ph]. [Online]. Available: http://arxiv.org/abs/2310.12110
  11. A. Smith, M. S. Johnson, F. Pollmann, and J.Knolle, “Simulating quantum many-body dynamics on a current digital quantum computer,” npj Quantum Information, vol. 5, 2019. [Online]. Available: https://doi.org/10.1038/s41534-019-0217-0
  12. X. Mi, M. Ippoliti, C. Quintana, A. Greene, Z. Chen, J. Gross, F. Arute, K. Arya, J. Atalaya, R. Babbush, J. C. Bardin, J. Basso, A. Bengtsson, A. Bilmes, A. Bourassa, L. Brill, M. Broughton, B. B. Buckley, D. A. Buell, B. Burkett, N. Bushnell, B. Chiaro, R. Collins, W. Courtney, D. Debroy, S. Demura, A. R. Derk, A. Dunsworth, D. Eppens, C. Erickson, E. Farhi, A. G. Fowler, B. Foxen, C. Gidney, M. Giustina, M. P. Harrigan, S. D. Harrington, J. Hilton, A. Ho, S. Hong, T. Huang, A. Huff, W. J. Huggins, L. B. Ioffe, S. V. Isakov, J. Iveland, E. Jeffrey, Z. Jiang, C. Jones, D. Kafri, T. Khattar, S. Kim, A. Kitaev, P. V. Klimov, A. N. Korotkov, F. Kostritsa, D. Landhuis, P. Laptev, J. Lee, K. Lee, A. Locharla, E. Lucero, O. Martin, J. R. McClean, T. McCourt, M. McEwen, K. C. Miao, M. Mohseni, S. Montazeri, W. Mruczkiewicz, O. Naaman, M. Neeley, C. Neill, M. Newman, M. Y. Niu, T. E. O’Brien, A. Opremcak, E. Ostby, B. Pato, A. Petukhov, N. C. Rubin, D. Sank, K. J. Satzinger, V. Shvarts, Y. Su, D. Strain, M. Szalay, M. D. Trevithick, B. Villalonga, T. White, Z. J. Yao, P. Yeh, J. Yoo, A. Zalcman, H. Neven, S. Boixo, V. Smelyanskiy, A. Megrant, J. Kelly, Y. Chen, S. L. Sondhi, R. Moessner, K. Kechedzhi, V. Khemani, and P. Roushan, “Time-crystalline eigenstate order on a quantum processor,” Nature, vol. 601, no. 7894, p. 531–536, Jan. 2022. [Online]. Available: https://www.nature.com/articles/s41586-021-04257-w
  13. I.-C. Chen, B. Burdick, Y. Yao, P. P. Orth, and T. Iadecola, “Error-mitigated simulation of quantum many-body scars on quantum computers with pulse-level control,” Phys. Rev. Res., vol. 4, p. 043027, Oct 2022. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.4.043027
  14. O. Shtanko, D. S. Wang, H. Zhang, N. Harle, A. Seif, R. Movassagh, and Z. Minev, “Uncovering local integrability in quantum many-body dynamics,” no. arXiv:2307.07552, Jul. 2023. [Online]. Available: http://arxiv.org/abs/2307.07552
  15. J. Saroni, H. Lamm, P. P. Orth, and T. Iadecola, “Reconstructing thermal quantum quench dynamics from pure states,” Phys. Rev. B, vol. 108, p. 134301, Oct 2023. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.108.134301
  16. I.-C. Chen, K. Pollock, Y. Yao, P. P. Orth, and T. Iadecola, “Problem-tailored simulation of energy transport on noisy quantum computers,” 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2310.03924
  17. J. Preskill, “Quantum computing in the nisq era and beyond,” Quantum, vol. 2, p. 79, Aug 2018. [Online]. Available: http://dx.doi.org/10.22331/q-2018-08-06-79
  18. M. Pistoia, S. F. Ahmad, A. Ajagekar, A. Buts, S. Chakrabarti, D. Herman, S. Hu, A. Jena, P. Minssen, P. Niroula, A. Rattew, Y. Sun, and R. Yalovetzky, “Quantum machine learning for finance,” no. arXiv:2109.04298, Sep. 2021, arXiv:2109.04298 [quant-ph]. [Online]. Available: http://arxiv.org/abs/2109.04298
  19. D. Herman, C. Googin, X. Liu, A. Galda, I. Safro, Y. Sun, M. Pistoia, and Y. Alexeev, “A survey of quantum computing for finance,” no. arXiv:2201.02773, Jun. 2022, arXiv:2201.02773 [quant-ph, q-fin]. [Online]. Available: http://arxiv.org/abs/2201.02773
  20. C. D. B. Bentley, S. Marsh, A. R. R. Carvalho, P. Kilby, and M. J. Biercuk, “Quantum computing for transport optimization,” no. arXiv:2206.07313, Jun. 2022, arXiv:2206.07313 [quant-ph]. [Online]. Available: http://arxiv.org/abs/2206.07313
  21. A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, “A variational eigenvalue solver on a photonic quantum processor,” Nature Communications, vol. 5, no. 1, p. 4213, Jul. 2014. [Online]. Available: https://www.nature.com/articles/ncomms5213
  22. A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,” nature, vol. 549, no. 7671, pp. 242–246, 2017. [Online]. Available: https://www.nature.com/articles/nature23879
  23. J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Rungger, G. H. Booth et al., “The variational quantum eigensolver: a review of methods and best practices,” Physics Reports, vol. 986, pp. 1–128, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0370157322003118?via%3Dihub
  24. M. Cerezo, K. Sharma, A. Arrasmith, and P. J. Coles, “Variational quantum state eigensolver,” npj Quantum Information, vol. 8, no. 1, p. 113, 2022. [Online]. Available: https://www.nature.com/articles/s41534-022-00611-6
  25. J. Lee, W. J. Huggins, M. Head-Gordon, and K. B. Whaley, “Generalized unitary coupled cluster wave functions for quantum computation,” Journal of Chemical Theory and Computation, vol. 15, no. 1, p. 311–324, Nov. 2018. [Online]. Available: http://dx.doi.org/10.1021/acs.jctc.8b01004
  26. Y. S. Yordanov, D. R. M. Arvidsson-Shukur, and C. H. W. Barnes, “Efficient quantum circuits for quantum computational chemistry,” Phys. Rev. A, vol. 102, p. 062612, Dec 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevA.102.062612
  27. Y. S. Yordanov, V. Armaos, C. H. W. Barnes, and D. R. M. Arvidsson-Shukur, “Qubit-excitation-based adaptive variational quantum eigensolver,” Communications Physics, vol. 4, no. 1, p. 1–11, Oct. 2021. [Online]. Available: https://www.nature.com/articles/s42005-021-00730-0
  28. M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” Phys. Rev. A, vol. 101, p. 032308, Mar 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevA.101.032308
  29. S. Bravyi, J. M. Gambetta, A. Mezzacapo, and K. Temme, “Tapering off qubits to simulate fermionic hamiltonians,” 2017. [Online]. Available: https://arxiv.org/abs/1701.08213
  30. K. Setia, R. Chen, J. E. Rice, A. Mezzacapo, M. Pistoia, and J. Whitfield, “Reducing qubit requirements for quantum simulations using molecular point group symmetries,” Journal of Chemical Theory and Computation, vol. 16, p. 6091–6097, Aug 2020. [Online]. Available: https://pubs.acs.org/doi/10.1021/acs.jctc.0c00113
  31. S. Ruder, “An overview of gradient descent optimization algorithms,” 2017.
  32. J. Stokes, J. Izaac, N. Killoran, and G. Carleo, “Quantum natural gradient,” Quantum, vol. 4, p. 269, May 2020. [Online]. Available: https://quantum-journal.org/papers/q-2020-05-25-269/
  33. D. Wierichs, C. Gogolin, and M. Kastoryano, “Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer,” Phys. Rev. Res., vol. 2, p. 043246, Nov 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.2.043246
  34. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2014. [Online]. Available: https://doi.org/10.48550/arXiv.1412.6980
  35. T. Tieleman and G. Hinton, ““lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude,” COURSERA Neural networks Mach. Learn., vol. 4, pp. 26–31, 2012.
  36. “Qhack2024 project.” [Online]. Available: https://github.com/ichen17/Qhack_project
  37. F. Zhang, N. Gomes, Y. Yao, P. P. Orth, and T. Iadecola, “Adaptive variational quantum eigensolvers for highly excited states,” Phys. Rev. B, vol. 104, p. 075159, Aug 2021. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.104.075159
  38. C. J. Turner, A. A. Michailidis, D. A. Abanin, M. Serbyn, and Z. Papić, “Weak ergodicity breaking from quantum many-body scars,” Nature Physics, vol. 14, no. 7, p. 745–749, Jul. 2018. [Online]. Available: https://www.nature.com/articles/s41567-018-0137-5
  39. C. J. Turner, A. A. Michailidis, D. A. Abanin, M. Serbyn, and Z. Papić, “Quantum scarred eigenstates in a rydberg atom chain: Entanglement, breakdown of thermalization, and stability to perturbations,” Phys. Rev. B, vol. 98, p. 155134, Oct 2018. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.98.155134
  40. S. Moudgalya, N. Regnault, and B. A. Bernevig, “Entanglement of exact excited states of affleck-kennedy-lieb-tasaki models: Exact results, many-body scars, and violation of the strong eigenstate thermalization hypothesis,” Phys. Rev. B, vol. 98, p. 235156, Dec 2018. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.98.235156
  41. G. Cenedese, M. Bondani, A. Andreanov, M. Carrega, R. Benenti, and Dario, “Shallow quantum circuits are robust hunters for quantum many-body scars,” 2024. [Online]. Available: https://doi.org/10.48550/arXiv.2401.09279
  42. E. J. Gustafson, A. C. Y. Li, A. Khan, J. Kim, D. M. Kurkcuoglu, M. S. Alam, P. P. Orth, A. Rahmani, and T. Iadecola, “Preparing quantum many-body scar states on quantum computers,” Quantum, vol. 7, p. 1171, Nov. 2023. [Online]. Available: https://quantum-journal.org/papers/q-2023-11-07-1171/
  43. M. Schecter and T. Iadecola, “Weak ergodicity breaking and quantum many-body scars in spin-1 x⁢y𝑥𝑦xyitalic_x italic_y magnets,” Phys. Rev. Lett., vol. 123, p. 147201, Oct 2019. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.123.147201

Summary

We haven't generated a summary for this paper yet.