# Hybrid Quantum Computing -- Tabu Search Algorithm for Partitioning Problems: preliminary study on the Traveling Salesman Problem

arXiv: https://arxiv.org/abs/2012.04984 · HTML: https://mlsanity.com/paper/2012.04984/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis](https://mlsanity.com/paper/2410.07980/) (2024)
- [Focusing on the Hybrid Quantum Computing -- Tabu Search Algorithm: new results on the Asymmetric Salesman Problem](https://mlsanity.com/paper/2102.05919/) (2021)
- [Hybrid classical-quantum computing: are we forgetting the classical part in the binomial?](https://mlsanity.com/paper/2308.10513/) (2023)
- [QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems](https://mlsanity.com/paper/2404.15852/) (2024)
- [Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations](https://mlsanity.com/paper/2207.00253/) (2022)

## Abstract (arXiv)

Quantum Computing is considered as the next frontier in computing, and it is attracting a lot of attention from the current scientific community. This kind of computation provides to researchers with a revolutionary paradigm for addressing complex optimization problems, offering a significant speed advantage and an efficient search ability. Anyway, Quantum Computing is still in an incipient stage of development. For this reason, present architectures show certain limitations, which have motivated the carrying out of this paper. In this paper, we introduce a novel solving scheme coined as hybrid Quantum Computing - Tabu Search Algorithm. Main pillars of operation of the proposed method are a greater control over the access to quantum resources, and a considerable reduction of non-profitable accesses. To assess the quality of our method, we have used 7 different Traveling Salesman Problem instances as benchmarking set. The obtained outcomes support the preliminary conclusion that our algorithm is an approach which offers promising results for solving partitioning problems while it drastically reduces the access to quantum computing resources. We also contribute to the field of Transfer Optimization by developing an evolutionary multiform multitasking algorithm as initialization method.