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Avoiding symmetry roadblocks and minimizing the measurement overhead of adaptive variational quantum eigensolvers

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V. O. Shkolnikov1,2, Nicholas J. Mayhall3,2, Sophia E. Economou1,2, and Edwin Barnes1,2

1Department of Physics, Virginia Tech, Blacksburg, VA 24061, USA
2Virginia Tech Center for Quantum Information Science and Engineering, Blacksburg, VA 24061, USA
3Department of Chemistry, Virginia Tech, Blacksburg, VA 24061, USA

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Abstract

Quantum simulation of strongly correlated systems is potentially the most feasible useful application of near-term quantum computers. Minimizing quantum computational resources is crucial to achieving this goal. A promising class of algorithms for this purpose consists of variational quantum eigensolvers (VQEs). Among these, problem-tailored versions such as ADAPT-VQE that build variational ansätze step by step from a predefined operator pool perform particularly well in terms of circuit depths and variational parameter counts. However, this improved performance comes at the expense of an additional measurement overhead compared to standard VQEs. Here, we show that this overhead can be reduced to an amount that grows only linearly with the number $n$ of qubits, instead of quartically as in the original ADAPT-VQE. We do this by proving that operator pools of size $2n-2$ can represent any state in Hilbert space if chosen appropriately. We prove that this is the minimal size of such “complete” pools, discuss their algebraic properties, and present necessary and sufficient conditions for their completeness that allow us to find such pools efficiently. We further show that, if the simulated problem possesses symmetries, then complete pools can fail to yield convergent results, unless the pool is chosen to obey certain symmetry rules. We demonstrate the performance of such symmetry-adapted complete pools by using them in classical simulations of ADAPT-VQE for several strongly correlated molecules. Our findings are relevant for any VQE that uses an ansatz based on Pauli strings.

Simulation of strongly correlated systems is one of the key applications envisioned for near-term quantum computers. Realizing this application requires reducing quantum and classical resources as much as possible. One of the leading approaches exploits the variational principle of quantum mechanics, but its feasibility depends crucially on finding suitable trial wavefunctions.

While adaptive algorithms that construct trial wavefunctions on the fly in a problem-tailored fashion seem particularly promising, they may come with an extra measurement cost compared to other variational algorithms. We prove that this extra cost can be reduced to being only linear in the number of qubits, and we provide explicit recipes for achieving this. We also show that it is important that these recipes must account for any symmetries in the system being simulated in order for them to work well.

► BibTeX data

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Cited by

[1] Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H. Booth, and Jonathan Tennyson, “The Variational Quantum Eigensolver: A review of methods and best practices”, Physics Reports 986, 1 (2022).

[2] Panagiotis G. Anastasiou, Yanzhu Chen, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou, “TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit ansätze”, arXiv:2209.10562, (2022).

[3] Hugh G. A. Burton, Daniel Marti-Dafcik, David P. Tew, and David J. Wales, “Exact electronic states with shallow quantum circuits through global optimisation”, arXiv:2207.00085, (2022).

[4] Anirban Mukherjee, Noah F. Berthusen, João C. Getelina, Peter P. Orth, and Yong-Xin Yao, “Comparative study of adaptive variational quantum eigensolvers for multi-orbital impurity models”, Communications Physics 6 1, 4 (2023).

[5] Yanzhu Chen, Linghua Zhu, Chenxu Liu, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou, “How Much Entanglement Do Quantum Optimization Algorithms Require?”, arXiv:2205.12283, (2022).

[6] Ada Warren, Linghua Zhu, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou, “Adaptive variational algorithms for quantum Gibbs state preparation”, arXiv:2203.12757, (2022).

[7] Tatiana A. Bespalova and Oleksandr Kyriienko, “Quantum simulation and ground state preparation for the honeycomb Kitaev model”, arXiv:2109.13883, (2021).

[8] Dmitry A. Fedorov, Yuri Alexeev, Stephen K. Gray, and Matthew Otten, “Unitary Selective Coupled-Cluster Method”, Quantum 6, 703 (2022).

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[10] Takashi Tsuchimochi, Masaki Taii, Taisei Nishimaki, and Seiichiro L. Ten-no, “Adaptive construction of shallower quantum circuits with quantum spin projection for fermionic systems”, Physical Review Research 4 3, 033100 (2022).

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[12] Mohammad Haidar, Marko J. Rančić, Thomas Ayral, Yvon Maday, and Jean-Philip Piquemal, “Open Source Variational Quantum Eigensolver Extension of the Quantum Learning Machine (QLM) for Quantum Chemistry”, arXiv:2206.08798, (2022).

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[15] Yordan S. Yordanov, Crispin H. W. Barnes, and David R. M. Arvidsson-Shukur, “Molecular-excited-state calculations with the qubit-excitation-based adaptive variational quantum eigensolver protocol”, Physical Review A 106 3, 032434 (2022).

[16] Panagiotis G. Anastasiou, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou, “How to really measure operator gradients in ADAPT-VQE”, arXiv:2306.03227, (2023).

The above citations are from SAO/NASA ADS (last updated successfully 2023-06-12 11:40:28). The list may be incomplete as not all publishers provide suitable and complete citation data.

Could not fetch Crossref cited-by data during last attempt 2023-06-12 11:40:27: Could not fetch cited-by data for 10.22331/q-2023-06-12-1040 from Crossref. This is normal if the DOI was registered recently.

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