The Overlap Gap Property limits limit swapping in QAOA
- URL: http://arxiv.org/abs/2404.06087v4
- Date: Mon, 11 Nov 2024 13:35:02 GMT
- Title: The Overlap Gap Property limits limit swapping in QAOA
- Authors: Mark Xin Hong Goh,
- Abstract summary: We show that the average-case value obtained by QAOA for the Max-$q$-XORSAT for even $qge 4$ is bounded away from optimality.
Results suggest that even when sub-optimised, the performance of QAOA on spin glass is equal in performance to classical algorithms.
- Score: 0.0
- License:
- Abstract: The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm designed for Combinatorial Optimization Problem (COP). We show that if a local algorithm is limited in performance at logarithmic depth for a spin glass type COP with an underlying Erd\"os--R\'enyi hypergraph, then a random regular hypergraph exhibits it as well. As such, we re-derived the fact that the average-case value obtained by QAOA for the Max-$q$-XORSAT for even $q\ge 4$ is bounded away from optimality even when the algorithm runs indefinitely if optimised using the so-called tree parameters due to the presence of the Overlap Gap Property (OGP). While this result was proven before, the proof is rather technical compared to ours. In addition, we show that the earlier result implicitly also implies limitation at logarithmic depth $p \le \epsilon \log n$ providing an improvement over limitation at superconstant depth. Lastly, the results suggests that even when sub-optimised, the performance of QAOA on spin glass is equal in performance to classical algorithms in solving the mean field spin glass problem providing further evidence that the conjecture of getting the exact solution under limit swapping for the Sherrington--Kirkpatrick model to be true.
Related papers
- Sum-of-Squares inspired Quantum Metaheuristic for Polynomial Optimization with the Hadamard Test and Approximate Amplitude Constraints [76.53316706600717]
Recently proposed quantum algorithm arXiv:2206.14999 is based on semidefinite programming (SDP)
We generalize the SDP-inspired quantum algorithm to sum-of-squares.
Our results show that our algorithm is suitable for large problems and approximate the best known classicals.
arXiv Detail & Related papers (2024-08-14T19:04:13Z) - Extending relax-and-round combinatorial optimization solvers with
quantum correlations [0.0]
We introduce a relax-and-round approach embedding the quantum approximate optimization algorithm (QAOA) with $pgeq 1$ layers.
We show for many problems, including Sherrington-Kirk glasses, that at $p=1$, it is as accurate as its classical counterpart.
We pave the way for an overarching quantum relax-and-round framework with performance on par with some of the best classical algorithms.
arXiv Detail & Related papers (2023-07-11T22:02:01Z) - Solving boolean satisfiability problems with the quantum approximate
optimization algorithm [0.05076419064097732]
We study the ability of QAOA to solve hard constraint satisfaction problems, as opposed to quantum computing problems.
We develop analytic bounds on the average success probability of QAOA over random formulae at the satisfiability threshold.
We find that for around 14 ansatz layers, QAOA matches the scaling performance of the highest-performance classical solver.
arXiv Detail & Related papers (2022-08-14T20:39:48Z) - Quantum Goemans-Williamson Algorithm with the Hadamard Test and
Approximate Amplitude Constraints [62.72309460291971]
We introduce a variational quantum algorithm for Goemans-Williamson algorithm that uses only $n+1$ qubits.
Efficient optimization is achieved by encoding the objective matrix as a properly parameterized unitary conditioned on an auxilary qubit.
We demonstrate the effectiveness of our protocol by devising an efficient quantum implementation of the Goemans-Williamson algorithm for various NP-hard problems.
arXiv Detail & Related papers (2022-06-30T03:15:23Z) - Performance and limitations of the QAOA at constant levels on large
sparse hypergraphs and spin glass models [15.857373057387669]
We prove concentration properties at any constant level (number of layers) on ensembles of random optimization problems in the infinite size limit.
Our analysis can be understood via a saddle-point approximation of a sum-over-paths integral.
We show that the performance of the QAOA at constant levels is bounded away from optimality for pure $q$-spin models when $qge 4$ and is even.
arXiv Detail & Related papers (2022-04-21T17:40:39Z) - Regret Bounds for Expected Improvement Algorithms in Gaussian Process
Bandit Optimization [63.8557841188626]
The expected improvement (EI) algorithm is one of the most popular strategies for optimization under uncertainty.
We propose a variant of EI with a standard incumbent defined via the GP predictive mean.
We show that our algorithm converges, and achieves a cumulative regret bound of $mathcal O(gamma_TsqrtT)$.
arXiv Detail & Related papers (2022-03-15T13:17:53Z) - Misspecified Gaussian Process Bandit Optimization [59.30399661155574]
Kernelized bandit algorithms have shown strong empirical and theoretical performance for this problem.
We introduce a emphmisspecified kernelized bandit setting where the unknown function can be $epsilon$--uniformly approximated by a function with a bounded norm in some Reproducing Kernel Hilbert Space (RKHS)
We show that our algorithm achieves optimal dependence on $epsilon$ with no prior knowledge of misspecification.
arXiv Detail & Related papers (2021-11-09T09:00:02Z) - The Quantum Approximate Optimization Algorithm at High Depth for MaxCut
on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model [0.0]
The Quantum Approximate Optimization Algorithm (QAOA) finds approximate solutions to optimization problems.
We give an iterative formula to evaluate performance for any $D$ at any depth $p$.
We make an optimistic conjecture that the QAOA, as $p$ goes to infinity, will achieve the Parisi value.
arXiv Detail & Related papers (2021-10-27T06:35:59Z) - Predicting parameters for the Quantum Approximate Optimization Algorithm
for MAX-CUT from the infinite-size limit [0.05076419064097732]
We present an explicit algorithm to evaluate the performance of QAOA on MAX-CUT applied to random Erdos-Renyi graphs of expected degree $d$.
This analysis yields an explicit mapping between QAOA parameters for MAX-CUT on Erdos-Renyi graphs, and the Sherrington-Kirkpatrick model.
arXiv Detail & Related papers (2021-10-20T17:58:53Z) - An Asymptotically Optimal Primal-Dual Incremental Algorithm for
Contextual Linear Bandits [129.1029690825929]
We introduce a novel algorithm improving over the state-of-the-art along multiple dimensions.
We establish minimax optimality for any learning horizon in the special case of non-contextual linear bandits.
arXiv Detail & Related papers (2020-10-23T09:12:47Z) - Hardness of Random Optimization Problems for Boolean Circuits,
Low-Degree Polynomials, and Langevin Dynamics [78.46689176407936]
We show that families of algorithms fail to produce nearly optimal solutions with high probability.
For the case of Boolean circuits, our results improve the state-of-the-art bounds known in circuit complexity theory.
arXiv Detail & Related papers (2020-04-25T05:45:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.