QAOA of the Highest Order
- URL: http://arxiv.org/abs/2111.12754v2
- Date: Mon, 6 Dec 2021 23:01:17 GMT
- Title: QAOA of the Highest Order
- Abstract summary: The Quantum Approximate Optimization Algorithm (QAOA) has been one of the leading candidates for near-term quantum advantage in gate-model quantum computers.
This paper gives an example of a graph coloring problem that, depending on its variable encoding scheme, optionally admits higher order terms.
The evidence then motivates an analysis of the scaling behavior of QAOA in this higher order formulation for an ensemble of graph coloring problems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Quantum Approximate Optimization Algorithm (QAOA) has been one of the
leading candidates for near-term quantum advantage in gate-model quantum
computers. From its inception, this algorithm has sparked the desire for
comparison between gate-model and annealing platforms. When preparing problem
statements for these algorithms, the predominant inclination has been to
formulate a quadratic Hamiltonian. This paper gives an example of a graph
coloring problem that, depending on its variable encoding scheme, optionally
admits higher order terms. This paper presents evidence that the higher order
formulation is preferable to two other encoding schemes. The evidence then
motivates an analysis of the scaling behavior of QAOA in this higher order
formulation for an ensemble of graph coloring problems.
Related papers
- Encoding Matters: Benchmarking Binary and D-ary Representations for Quantum Combinatorial Optimization [1.3824488054100907]
We study Quadratic Unconstrained D-ary Optimization (QUDO) as an alternative formulation in which decision variables are encoded directly in higher dimensional Hilbert spaces.<n>We demonstrate that QUDO naturally captures structural constraints across a range of problem classes, including the Traveling Salesman Problem, graph coloring, job scheduling, and Max-K-Cut, without the need for extensive penalty constructions.<n>Our study show consistently improved approximation ratios and substantially reduced computational overhead at comparable circuit depths, highlighting QUDO as a scalable and expressive representation for quantum optimization.
arXiv Detail & Related papers (2026-02-07T04:37:32Z) - An Introduction to the Quantum Approximate Optimization Algorithm [51.56484100374058]
The tutorial begins by outlining variational quantum circuits and QUBO problems.<n>Next, it explores the QAOA in detail, covering its Hamiltonian formulation, gate decomposition, and example applications.<n>The tutorial extends these concepts to higher-order Hamiltonians and discussing the associated symmetries and circuit construction.
arXiv Detail & Related papers (2025-11-23T09:54:20Z) - A Comparison of Quadratic and Higher-Order Representations for QAOA [0.0]
We consider a routing problem and compare quadratic and higher-order representations using the Quantum Approximate optimisation algorithm (QAOA)<n>We find that the higher-order form yields better solution quality and scales better in terms of numbers of qubits, but requires more two-qubit gates.
arXiv Detail & Related papers (2025-09-24T13:53:50Z) - The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm [52.89358421626026]
GPTQ emerged as one of the standard methods for one-shot post-training quantization at LLM scale.<n>We show that GPTQ is mathematically identical to Babai's nearest plane algorithm for the classical closest vector problem.
arXiv Detail & Related papers (2025-07-24T16:22:18Z) - Systematic improvement of the quantum approximate optimisation ansatz for combinatorial optimisation using quantum subspace expansion [0.0]
I study the enhancement of the quantum approximate optimisation ansatz (QAOA) with a generator coordinate method (GCM)<n>I achieve systematic performances improvements in the approximation ratio and fidelity for the maximal independent set on Erd"os-R'enyi graphs.
arXiv Detail & Related papers (2025-06-23T12:54:06Z) - Efficient hybrid variational quantum algorithm for solving graph coloring problem [4.4739537033766705]
This paper proposes a hybrid variational quantum algorithm to solve the $k$-coloring problem of graph vertices.
We employ a hierarchical framework that integrates feedback correction and conflict resolution to achieve $k$-coloring.
We apply the proposed algorithm to optimize the scheduling of a subway transportation network, demonstrating a high degree of fairness.
arXiv Detail & Related papers (2025-04-30T05:45:15Z) - Quantum Computing for a Profusion of Postman Problem Variants [0.0]
We study the viability of solving the Chinese Postman Problem, a graph routing optimization problem.
We put emphasis on the explanation of how to convert such problems into quadratic unconstrained binary optimization problems.
We also expand upon a previously discovered algorithm for solving the Chinese Postman Problem on a closed undirected graph.
arXiv Detail & Related papers (2022-08-17T16:42:23Z) - An entanglement perspective on the quantum approximate optimization
algorithm [0.0]
We study the entanglement growth and spread resulting from randomized and optimized QAOA circuits.
We also investigate the entanglement spectrum in connection with random matrix theory.
arXiv Detail & Related papers (2022-06-14T17:37:44Z) - Quantum approximate optimization algorithm for qudit systems [0.0]
We discuss the quantum approximate optimization algorithm (QAOA) for qudit systems.
We illustrate how the QAOA can be used to formulate a variety of integer optimization problems.
We present numerical results for a charging optimization problem mapped onto a max-$k$-graph coloring problem.
arXiv Detail & Related papers (2022-04-01T10:37:57Z) - Encoding trade-offs and design toolkits in quantum algorithms for
discrete optimization: coloring, routing, scheduling, and other problems [0.0]
We present an intuitive method for synthesizing and analyzing discrete (i.e., integer-based) optimization problems.
This method is expressed using a discrete quantum intermediate representation (DQIR) that is encoding-independent.
Second, we perform numerical studies comparing several runtime encodings.
Third, we design low-depth graph-derived partial mixers (GDPMs) up to 16-level quantum variables.
arXiv Detail & Related papers (2022-03-28T01:01:12Z) - 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) - Benchmarking Small-Scale Quantum Devices on Computing Graph Edit
Distance [52.77024349608834]
Graph Edit Distance (GED) measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical.
In this paper we present a comparative study of two quantum approaches to computing GED.
arXiv Detail & Related papers (2021-11-19T12:35:26Z) - Solving correlation clustering with QAOA and a Rydberg qudit system: a
full-stack approach [94.37521840642141]
We study the correlation clustering problem using the quantum approximate optimization algorithm (QAOA) and qudits.
Specifically, we consider a neutral atom quantum computer and propose a full stack approach for correlation clustering.
We show the qudit implementation is superior to the qubit encoding as quantified by the gate count.
arXiv Detail & Related papers (2021-06-22T11:07:38Z) - On Applying the Lackadaisical Quantum Walk Algorithm to Search for
Multiple Solutions on Grids [63.75363908696257]
The lackadaisical quantum walk is an algorithm developed to search graph structures whose vertices have a self-loop of weight $l$.
This paper addresses several issues related to applying the lackadaisical quantum walk to search for multiple solutions on grids successfully.
arXiv Detail & Related papers (2021-06-11T09:43:09Z) - Q-Match: Iterative Shape Matching via Quantum Annealing [64.74942589569596]
Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP)
This paper proposes Q-Match, a new iterative quantum method for QAPs inspired by the alpha-expansion algorithm.
Q-Match can be applied for shape matching problems iteratively, on a subset of well-chosen correspondences, allowing us to scale to real-world problems.
arXiv Detail & Related papers (2021-05-06T17:59:38Z) - Hybrid quantum-classical algorithms for approximate graph coloring [65.62256987706128]
We show how to apply the quantum approximate optimization algorithm (RQAOA) to MAX-$k$-CUT, the problem of finding an approximate $k$-vertex coloring of a graph.
We construct an efficient classical simulation algorithm which simulates level-$1$ QAOA and level-$1$ RQAOA for arbitrary graphs.
arXiv Detail & Related papers (2020-11-26T18:22:21Z) - Applying the Quantum Alternating Operator Ansatz to the Graph Matching
Problem [0.5584060970507505]
We design a derivation technique to tackle a few problems over maximal matchings in graphs.
We get a superposition over all possible matchings when given the empty state as input and a superposition over all maximal matchings when given the W -states as input.
Our main result is that the expected size of the matchings corresponding to the output states of our QAOA+ algorithm when ran on a 2-regular graph is greater than the expected matching size obtained from a uniform distribution over all matchings.
arXiv Detail & Related papers (2020-11-24T06:36:11Z)
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.