Crossing The Gap Using Variational Quantum Eigensolver: A Comparative Study (2405.11687v2)
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- S. Ruder, “An overview of gradient descent optimization algorithms,” 2017.
- 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/
- 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
- D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2014. [Online]. Available: https://doi.org/10.48550/arXiv.1412.6980
- 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.
- “Qhack2024 project.” [Online]. Available: https://github.com/ichen17/Qhack_project
- 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
- 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
- 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
- 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
- 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
- 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/
- M. Schecter and T. Iadecola, “Weak ergodicity breaking and quantum many-body scars in spin-1 xy𝑥𝑦xyitalic_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