Bounds on approximating Max $k$XOR with quantum and classical local algorithms

Kunal Marwaha; Stuart Hadfield

doi:10.22331/q-2022-07-07-757

Bounds on approximating Max $k$XOR with quantum and classical local algorithms

Kunal Marwaha^1,2,3,4 and Stuart Hadfield^1,2

¹Quantum Artificial Intelligence Laboratory (QuAIL), NASA Ames Research Center, Moffett Field, CA
²USRA Research Institute for Advanced Computer Science (RIACS), Mountain View, CA
³Berkeley Center for Quantum Information and Computation, University of California, Berkeley, CA
⁴Department of Computer Science, University of Chicago, Chicago, IL

Published:	2022-07-07, volume 6, page 757
Eprint:	arXiv:2109.10833v3
Doi:	https://doi.org/10.22331/q-2022-07-07-757
Citation:	Quantum 6, 757 (2022).

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Abstract

We consider the power of local algorithms for approximately solving Max $k$XOR, a generalization of two constraint satisfaction problems previously studied with classical and quantum algorithms (MaxCut and Max E3LIN2). In Max $k$XOR each constraint is the XOR of exactly $k$ variables and a parity bit. On instances with either random signs (parities) or no overlapping clauses and $D+1$ clauses per variable, we calculate the expected satisfying fraction of the depth-1 QAOA from Farhi et al [arXiv:1411.4028] and compare with a generalization of the local threshold algorithm from Hirvonen et al [arXiv:1402.2543]. Notably, the quantum algorithm outperforms the threshold algorithm for $k$$\gt$$4$.

On the other hand, we highlight potential difficulties for the QAOA to achieve computational quantum advantage on this problem. We first compute a tight upper bound on the maximum satisfying fraction of nearly all large random regular Max $k$XOR instances by numerically calculating the ground state energy density $P(k)$ of a mean-field $k$-spin glass [arXiv:1606.02365]. The upper bound grows with $k$ much faster than the performance of both one-local algorithms. We also identify a new obstruction result for low-depth quantum circuits (including the QAOA) when $k=3$, generalizing a result of Bravyi et al [arXiv:1910.08980] when $k=2$. We conjecture that a similar obstruction exists for all $k$.

Featured image: Comparison of the satisfying fraction (performance) of the threshold algorithm and the depth-1 QAOA for Max kXOR at large $D$ (and $D \gg k$). The constant $C_k$ for QAOA grows more quickly with $k$ than for the threshold algorithm. Although the threshold algorithm outperforms the depth-1 QAOA for $k \in \{2,3,4\}$, at all other k, the QAOA is the better algorithm.

► BibTeX data

@article{Marwaha2022boundsapproximating,
  doi = {10.22331/q-2022-07-07-757},
  url = {https://doi.org/10.22331/q-2022-07-07-757},
  title = {Bounds on approximating {M}ax {$k$}{XOR} with quantum and classical local algorithms},
  author = {Marwaha, Kunal and Hadfield, Stuart},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {6},
  pages = {757},
  month = jul,
  year = {2022}
}

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Cited by

[1] Taylor L. Patti, Jean Kossaifi, Anima Anandkumar, and Susanne F. Yelin, "Variational quantum optimization with multibasis encodings", Physical Review Research 4 3, 033142 (2022).

[2] Ojas Parekh, "Synergies Between Operations Research and Quantum Information Science", INFORMS Journal on Computing ijoc.2023.1268 (2023).

[3] Stuart Hadfield, Tad Hogg, and Eleanor G Rieffel, "Analytical framework for quantum alternating operator ansätze", Quantum Science and Technology 8 1, 015017 (2023).

[4] Stefan H. Sack, Raimel A. Medina, Richard Kueng, and Maksym Serbyn, "Transition states and greedy exploration of the QAOA optimization landscape", arXiv:2209.01159, (2022).

[5] Joao Basso, Edward Farhi, Kunal Marwaha, Benjamin Villalonga, and Leo Zhou, "The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model", arXiv:2110.14206, (2021).

[6] Yash J. Patel, Sofiene Jerbi, Thomas Bäck, and Vedran Dunjko, "Reinforcement Learning Assisted Recursive QAOA", arXiv:2207.06294, (2022).

[7] Nicolas PD Sawaya, Albert T Schmitz, and Stuart Hadfield, "Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems", arXiv:2203.14432, (2022).

The above citations are from Crossref's cited-by service (last updated successfully 2023-03-31 05:25:57) and SAO/NASA ADS (last updated successfully 2023-03-31 05:25:58). The list may be incomplete as not all publishers provide suitable and complete citation data.

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