adiabatic quantum computing
Dates:
Monday, November 2, 2026 to Wednesday, November 4, 2026
Submission deadline:
Tuesday, September 8, 2026
Registration deadline:
Friday, September 25, 2026
Quantum annealing is a method of quantum computation for solving combinatorial optimisation problems operating in continuous time, a feature that is shared with various methods of analog quantum simulation. The International Network on Quantum Annealing (INQA) 2026 conference aims to bring together leading experts in analog quantum computation and simulation to share and discuss their latest results. Theoretical, numerical and experimental works are welcome, as well as contributions within the theme from related fields. This year’s conference of INQA will be held in Bled, Slovenia, from November 2 – 4, 2026. This is the 5th instalment of this conference series, which previously took place in London (2022), Innsbruck (2023), Tokyo (2024) and Barcelona (2025).
Application deadline:
Wednesday, November 1, 2017
Atos is a leader in digital services with annual revenue of € 12 billion and 100,000 employees in 72 countries. Serving a global client base, Atos offers a variety of services including: Business & Platform Solutions, Infrastructure & Data Management, Technology Transformation Services, BPO, Cloud and Big Data & Cyber Security.
Application deadline:
Saturday, September 30, 2017
The Research Scientist for Quantum Computing will design experiments to run on emerging hardware to give insights into quantum approaches to NASA problems of interest, explore the robustness of the hardware, develop best practice programming techniques for quantum hardware, and illuminate the inner workings to better understand the mechanisms that can be used to provide a quantum computational advantage.
Essential Duties/Responsibilities:
European COST Action to develop “A quantum theory of complex and networked systems”
Open webnair 4:00 p.m. GMT – Thursday, June 2
One could argue that the fields of quantum information science and complex network theory (a.k.a. complexity science) both address complexity, yet from opposite perspectives. Indeed, the former makes use of a complex system as a computational resource whereas the later generally studies (and often using computer simulations) the scaling, collective behavior and emergent properties of complex system(s).
