International Conference on Quantum Science & Technology (6-9th October, 2025) - call for abstracts

The main aim of the conference, to be held in Quy Nhon, Vietnam, is to develop links between physicists in Vietnam and those in France and around the world who are contributing to the advances of quantum physics. The scientific programme features eminent invited speakers including Serge Haroche. The following themes are envisaged:

• quantum optics, quantum communication and quantum computation
• topics where condensed matter, atomic physics and chemical physics overlap
• high precision experiments involving spectroscopy and metrology
• cold atoms and simulation of materials
• theory and methods in quantum mechanics
• quantum high energy physics and cosmology
• quantum technologies and energy production

A focus on inter-generational exchanges will be planned between top level invited senior physicists and young students, opening new scientific horizons to them. Tutorials will be given (half a day before the colloquium) to provide the basis of the fields which will be covered by the speakers. Time will be given to young PhDs and postdocs to present their work. Round tables will allow informal discussions raised by the presentations and identify opportunities to develop scientific cooperative projects between Vietnamese and foreign laboratories. 

For more details and registration information, please visit the conference website. The abstract submission and registration deadline is September 7th, 2025. Registration is free, but participants must cover their own travel and accommodation expenses.

What's next for applied quantum computing?

NISQ (noisy intermediate-scale quantum) algorithms generated a lot of excitement and a lot of publications - the 2022 review has amassed almost 2000 citations! Nowadays the tone is more subdued, with many experts believing any useful practical applications of quantum processors will need quantum error correction. The new hot topics are understanding how to make useful error correction a reality, and what might be done with a few hundred logical qubits. 

What then should a new student interested in applied quantum computing focus on?

Ryan Babbush and collaborators already argued in 2021 that algorithms with quadratic speedups won't be useful in practice. So sorry, but we won't be able to solve complex industry optimization problems using Grover search. However, their analysis indicated that quartic speedups and beyond could be practically useful. Which quantum algorithms have this property?

Consulting the excellent review article Quantum algorithms: A survey of applications and end-to-end complexities, there are only a few examples of known or suspected quartic or beyond end-to-end quantum speedups! They are:

Tensor principal component analysis (PCA). Ordinary PCA is a data reduction step widely used in data analysis and machine learning. It's not yet clear what tensor PCA might be useful for, but if an application can be found quantum computers will probably give a useful speedup.

Topological data analysis (TDA). This is another promising direction where a useful speedup for certain problems is possible. Following an initial buzz of excitement in 2022, it's unclear whether there are practical applications for where such a speedup can be useful. Recently-developed quantum-inspired classical algorithms will be useful to identify potential use-cases for quantum TDA.

On the classical computing side, quantum-inspired tensor network methods are very promising for near-term applications.  

There are also other approaches (QAOA, quantum machine learning) which attracted a lot of interest since 2020 and are still being explored theoretically, but at least in their present formulations they seem unable to provide a useful speedup for classical problems, with their most promising applications related to directly studying or simulating certain quantum systems. Thus, interest has shifted from "beating" classical methods on carefully-selected problems to better understanding the foundations of quantum machine learning. While this is a fascinating topic, it is at this stage it is more theoretical than applied research.