# Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations

arXiv: https://arxiv.org/abs/2207.00253 · HTML: https://mlsanity.com/paper/2207.00253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems](https://mlsanity.com/paper/1910.13045/) (2019)
- [Hybrid Quantum Computing -- Tabu Search Algorithm for Partitioning Problems: preliminary study on the Traveling Salesman Problem](https://mlsanity.com/paper/2012.04984/) (2020)
- [Quantum Annealing for Vehicle Routing Problem with weighted Segment](https://mlsanity.com/paper/2203.13469/) (2022)
- [Focusing on the Hybrid Quantum Computing -- Tabu Search Algorithm: new results on the Asymmetric Salesman Problem](https://mlsanity.com/paper/2102.05919/) (2021)

## Built on by (most cited first, 1 of 1)

- [QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems](https://mlsanity.com/paper/2404.15852/) (2024)

## Abstract (arXiv)

Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development. Indeed, current commercial quantum computers suffer from some critical restraints, such as noisy processes and a limited amount of qubits, among others, that affect the performance of quantum algorithms. Despite these limitations, researchers have devoted much effort to propose different frameworks for efficiently using these Noisy Intermediate-Scale Quantum (NISQ) devices. One of these procedures is D'WAVE Systems' quantum-annealer, which can be use to solve optimization problems by translating them into an energy minimization problem. In this context, this work is focused on providing useful insights and information into the behaviour of the quantum-annealer when addressing real-world combinatorial optimization problems. Our main motivation with this study is to open some quantum computing frontiers to non-expert stakeholders. To this end, we perform an extensive experimentation, in the form of a parameter sensitive analysis. This experimentation has been conducted using the Traveling Salesman Problem as benchmarking problem, and adopting two QUBOs: state-of-the-art and a heuristically generated. Our analysis has been performed on a single 7-noded instance, and it is based on more than 200 different parameter configurations, comprising more than 3700 unitary runs and 7 million of quantum reads. Thanks to this study, findings related to the energy distribution and most appropriate parameter settings have been obtained. Finally, an additional study has been performed, aiming to determine the efficiency of the heuristically built QUBO in further TSP instances.