Advantages of multistage quantum walks over QAOA
- URL: http://arxiv.org/abs/2407.06663v2
- Date: Tue, 16 Jul 2024 09:35:53 GMT
- Title: Advantages of multistage quantum walks over QAOA
- Authors: Lasse Gerblich, Tamanna Dasanjh, Horatio Q. X. Wong, David Ross, Leonardo Novo, Nicholas Chancellor, Viv Kendon,
- Abstract summary: We compare the quantum approximate optimization algorithm (QAOA) with multi-stage quantum walks (MSQW)
We obtain evidence that MSQW outperforms QAOA, using equivalent resources.
- Score: 0.7852714805965528
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Methods to find the solution state for optimization problems encoded into Ising Hamiltonians are a very active area of current research. In this work we compare the quantum approximate optimization algorithm (QAOA) with multi-stage quantum walks (MSQW). Both can be used as variational quantum algorithms, where the control parameters are optimized classically. A fair comparison requires both quantum and classical resources to be assessed. Alternatively, parameters can be chosen heuristically, as we do in this work, providing a simpler setting for comparisons. Using both numerical and analytical methods, we obtain evidence that MSQW outperforms QAOA, using equivalent resources. We also show numerically for random spin glass ground state problems that MSQW performs well even for few stages and heuristic parameters, with no classical optimization.
Related papers
- Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational
Quantum Systems [65.268245109828]
We compare the performance of classicals across a series of partially-randomized tasks.
We focus on local zeroth-orders due to their generally favorable performance and query-efficiency on quantum systems.
arXiv Detail & Related papers (2023-10-14T02:13:26Z) - A Parameter Setting Heuristic for the Quantum Alternating Operator
Ansatz [0.0]
We introduce a strategy for parameter setting suitable for common cases in which the number of distinct cost values grows onlyly with the problem size.
We define a Classical Homogeneous Proxy for QAOA in which Perfect Homogeneity holds exactly, and which yields information describing both states and expectation values.
For up to $3$ QAOA levels we are easily able to find parameters that match approximation ratios returned by previous globally optimized approaches.
arXiv Detail & Related papers (2022-11-17T00:18:06Z) - The QAOA with Few Measurements [4.713817702376467]
The Approximate Quantum Optimization Algorithm (QAOA) was originally developed to solve optimization problems.
Fully descriptive benchmarking techniques are often expensive for large numbers of qubits.
Some experimental quantum computing platforms such as neutral atom quantum computers have slow repetition rates.
arXiv Detail & Related papers (2022-05-13T18:42:20Z) - Twisted hybrid algorithms for combinatorial optimization [68.8204255655161]
Proposed hybrid algorithms encode a cost function into a problem Hamiltonian and optimize its energy by varying over a set of states with low circuit complexity.
We show that for levels $p=2,ldots, 6$, the level $p$ can be reduced by one while roughly maintaining the expected approximation ratio.
arXiv Detail & Related papers (2022-03-01T19:47:16Z) - Unsupervised strategies for identifying optimal parameters in Quantum
Approximate Optimization Algorithm [3.508346077709686]
We study unsupervised Machine Learning approaches for setting parameters without optimization.
We showcase them within Recursive-QAOA up to depth $3$ where the number of QAOA parameters used per iteration is limited to $3$.
We obtain similar performances to the case where we extensively optimize the angles, hence saving numerous circuit calls.
arXiv Detail & Related papers (2022-02-18T19:55:42Z) - Efficient Classical Computation of Quantum Mean Values for Shallow QAOA
Circuits [15.279642278652654]
We present a novel graph decomposition based classical algorithm that scales linearly with the number of qubits for the shallow QAOA circuits.
Our results are not only important for the exploration of quantum advantages with QAOA, but also useful for the benchmarking of NISQ processors.
arXiv Detail & Related papers (2021-12-21T12:41:31Z) - Quantum Approximate Optimization Algorithm Based Maximum Likelihood
Detection [80.28858481461418]
Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
arXiv Detail & Related papers (2021-07-11T10:56:24Z) - Accelerating variational quantum algorithms with multiple quantum
processors [78.36566711543476]
Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages.
Modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large data.
Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue.
arXiv Detail & Related papers (2021-06-24T08:18:42Z) - Quantum-optimal-control-inspired ansatz for variational quantum
algorithms [105.54048699217668]
A central component of variational quantum algorithms (VQA) is the state-preparation circuit, also known as ansatz or variational form.
Here, we show that this approach is not always advantageous by introducing ans"atze that incorporate symmetry-breaking unitaries.
This work constitutes a first step towards the development of a more general class of symmetry-breaking ans"atze with applications to physics and chemistry problems.
arXiv Detail & Related papers (2020-08-03T18:00:05Z) - Evaluation of QAOA based on the approximation ratio of individual
samples [0.0]
We simulate the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives.
Because of the evolving QAOA computational complexity-theoretic guidance, we utilize a framework for the search for quantum advantage.
arXiv Detail & Related papers (2020-06-08T18:00:18Z) - Cross Entropy Hyperparameter Optimization for Constrained Problem
Hamiltonians Applied to QAOA [68.11912614360878]
Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications.
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
arXiv Detail & Related papers (2020-03-11T13:52:41Z)
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.