English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The Traveling Salesman Problem (TSP) is one of the most often-used NP-Hardproblems in computer science to study the effectiveness of computing models andhardware platforms. In this regard, it is also heavily used as a vehicle tostudy the feasibility of the quantum computing paradigm for this class ofproblems. In this paper, we tackle the TSP using the quantum approximateoptimization algorithm (QAOA) approach by formulating it as an optimizationproblem. By adopting an improved qubit encoding strategy and a layerwiselearning optimization protocol, we present numerical results obtained from thegate-based digital quantum simulator, specifically targeting TSP instances with3, 4, and 5 cities. We focus on the evaluations of three distinctive QAOA mixerdesigns, considering their performances in terms of numerical accuracy andoptimization cost. Notably, we find a well-balanced QAOA mixer design exhibitsmore promising potential for gate-based simulators and realistic quantumdevices in the long run, an observation further supported by our noise modelsimulations. Furthermore, we investigate the sensitivity of the simulations tothe TSP graph. Overall, our simulation results show the digital quantumsimulation of problem-inspired ansatz is a successful candidate for findingoptimal TSP solutions.

Comparative study of variations in quantum approximate optimization algorithms for the Traveling Salesman Problem