State Preparation Boosters for Early Fault-Tolerant Quantum Computation

Guoming Wang1, Sukin Sim2, and Peter D. Johnson2

1Zapata Computing Canada Inc., 25 Adelaide St E, Suite 1500, Toronto, ON M5C 3A1, Canada
2Zapata Computing Inc., 100 Federal Street, 20th Floor, Boston, MA 02110, USA

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Quantum computing is believed to be particularly useful for the simulation of chemistry and materials, among the various applications. In recent years, there have been significant advancements in the development of near-term quantum algorithms for quantum simulation, including VQE and many of its variants. However, for such algorithms to be useful, they need to overcome several critical barriers including the inability to prepare high-quality approximations of the ground state. Current challenges to state preparation, including barren plateaus and the high-dimensionality of the optimization landscape, make state preparation through ansatz optimization unreliable. In this work, we introduce the method of ground state boosting, which uses a limited-depth quantum circuit to reliably increase the overlap with the ground state. This circuit, which we call a booster, can be used to augment an ansatz from VQE or be used as a stand-alone state preparation method. The booster converts circuit depth into ground state overlap in a controllable manner. We numerically demonstrate the capabilities of boosters by simulating the performance of a particular type of booster, namely the Gaussian booster, for preparing the ground state of $N_2$ molecular system. Beyond ground state preparation as a direct objective, many quantum algorithms, such as quantum phase estimation, rely on high-quality state preparation as a subroutine. Therefore, we foresee ground state boosting and similar methods as becoming essential algorithmic components as the field transitions into using early fault-tolerant quantum computers.

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Cited by

[1] Guoming Wang, Daniel Stilck França, Ruizhe Zhang, Shuchen Zhu, and Peter D. Johnson, "Quantum algorithm for ground state energy estimation using circuit depth with exponentially improved dependence on precision", Quantum 7, 1167 (2023).

[2] César Feniou, Olivier Adjoua, Baptiste Claudon, Julien Zylberman, Emmanuel Giner, and Jean-Philip Piquemal, "Sparse Quantum State Preparation for Strongly Correlated Systems", The Journal of Physical Chemistry Letters 15 11, 3197 (2024).

[3] Hirofumi Nishi, Koki Hamada, Yusuke Nishiya, Taichi Kosugi, and Yu-ichiro Matsushita, "Optimal scheduling in probabilistic imaginary-time evolution on a quantum computer", Physical Review Research 5 4, 043048 (2023).

[4] Soumen Pal, Manojit Bhattacharya, Snehasish Dash, Sang-Soo Lee, and Chiranjib Chakraborty, "Future Potential of Quantum Computing and Simulations in Biological Science", Molecular Biotechnology (2023).

[5] Yulong Dong, Lin Lin, and Yu Tong, "Ground-State Preparation and Energy Estimation on Early Fault-Tolerant Quantum Computers via Quantum Eigenvalue Transformation of Unitary Matrices", PRX Quantum 3 4, 040305 (2022).

[6] Travis L. Scholten, Carl J. Williams, Dustin Moody, Michele Mosca, William Hurley, William J. Zeng, Matthias Troyer, and Jay M. Gambetta, "Assessing the Benefits and Risks of Quantum Computers", arXiv:2401.16317, (2024).

[7] Peter D. Johnson, Alexander A. Kunitsa, Jérôme F. Gonthier, Maxwell D. Radin, Corneliu Buda, Eric J. Doskocil, Clena M. Abuan, and Jhonathan Romero, "Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation", arXiv:2203.07275, (2022).

[8] Min-Quan He, Dan-Bo Zhang, and Z. D. Wang, "Quantum Gaussian filter for exploring ground-state properties", Physical Review A 106 3, 032420 (2022).

The above citations are from Crossref's cited-by service (last updated successfully 2024-04-18 20:28:36) and SAO/NASA ADS (last updated successfully 2024-04-18 20:28:37). The list may be incomplete as not all publishers provide suitable and complete citation data.