Quantum computing: INESC TEC proposes strategies for greater efficiency and lower cost

INESC TEC researchers developed a study that aims to make adaptive variational quantum algorithms more efficient, thus reducing computational costs and make quantum computing more accessible and efficient.

Adaptive variational quantum algorithms are a promising approach to solving complex problems in different domains, like quantum chemistry, combinatorial optimisation, simulation of physical systems, machine learning, etc. However, and according to Mafalda Ramôa, INESC TEC researcher in quantum computing, “these algorithms require a high number of measurements to be performed on quantum computers”. 

Mafalda Ramôa – co-authoring with Luís Paulo Santos, also a researcher at INESC TEC – focused on studying adaptive variational quantum algorithms and adapting the optimisation process to allow the reuse of information. “In our work, we propose a new method in which the Hessian matrix is recycled from one optimisation to the next, avoiding discarding information about the second derivatives of the cost function. Essentially, we are reusing, as much as possible, the information obtained in each optimisation – which allows us to save resources,” explained Mafalda. 

This new method reduces by orders of magnitude the measurement costs of adaptive variational quantum algorithms, facilitating practical implementations in the future. Moreover, “these algorithms have potential application in several areas, namely in the development of new materials or even medicines”, she added.  

This cost reduction represents a breakthrough in the field of practical quantum computing, boosting the adoption of said technology in different industry sectors and scientific research. Companies like Google and IBM have already been investing in improving these techniques to enable viable commercial applications in the future. 

In partnership with researchers at Virginia Tech, the article “Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms” was published in the journal Quantum Science and Technology, a top journal and classified with Q1, in the area of quantum computing. 

The researchers mentioned in this news piece are associated with INESC TEC and U.Minho.