Researchers from three Italian universities have published a study showing how research on quantum computers can be exploited to discover new properties of polymeric materials that can be of great interest in biology and materials science
The development of quantum computers is opening up previously unimaginable perspectives for solving problems deemed beyond the reach of conventional computers in cryptography, pharmacology, and the physical and chemical properties of molecules and materials, to name a few. The computational capabilities of existing quantum computers however are still relatively limited. Now a study recently published in Science Advances promotes the combination of methods used in quantum and traditional computing, contributing to this phase of technological development.
The research team, Cristian Micheletti and Francesco Slongo from SISSA in Trieste, Philip Hauke from the University of Trento, and Pietro Faccioli from the University of Milano-Bicocca, used a mathematical approach called Qubo (Quadratic Unconstrained Binary Optimization) that maximizes the characteristics of certain quantum computers known as 'quantum annealers'.
The study utilized the Qubo approach to simulate in a radically new way densely packed polymer blends, complex physical systems that play a key role in both biology and materials science. By using quantum computers, the researchers obtained an increase in the computing performance compared to traditional techniques, providing an important example of the vast potential of these emerging technologies. The Qubo approach also proved particularly effective on conventional computers, allowing the researchers to discover surprising properties of these polymeric blends.
The implications can be far-reaching since the approach used in the study is naturally suited to be transferred to many other molecular systems.
A new perspective inspired by quantum computer research
The Monte Carlo simulation technique has been a reference method for studying complex systems such as synthetic polymers or biological ones like DNA for decades," explains Cristian Micheletti, who coordinated the study. "Unfortunately, the efficiency of these simulations rapidly decreases with the increasing density and size of the system. Therefore, studying realistic systems, such as the organization of chromosomes in the cell nucleus, requires a huge computational resource expenditure." Francesco Slongo, doctoral student at SISSA and first author of the study, continues: "Quantum computers promise to exceptionally increase computing performance, but they have the limitations of an evolving technology. Here the new simulation strategy comes into play, as it is applicable to existing pioneering quantum computers but can be successfully transferred even to conventional computers."
An unexpected boost for classical simulations
As Philipp Hauke and Pietro Faccioli note: "Quantum machines focused on solving problems formulated with the Qubo approach are already in use and can be extremely effective. It was precisely to take advantage of such machines that we rewrote the models of conventional polymers in the Qubo formulation. To our surprise, we discovered that Qubo rewriting proved advantageous even on conventional computers, allowing us to simulate dense polymers more quicker than with established methods. Thanks to this, we established previously unknown properties for these systems, all while using ordinary computers."
Implications, challenges, and future directions
It has happened in the past that physical models designed to make the most of innovations in computing technologies have then emerged to be transferred to different fields. The most well-known case is that of lattice fluid models designed for 1990s supercomputers which are now widely used for many other systems and types of computers. The study published in Science Advances is another example of this, as it shows how methodologies inspired by quantum computing can pave the way for the study of new materials and the understanding of the functioning of molecular systems of biological interest.
The research received support from the NRRP CN 00000013 CN-HPC, M4C2I1.4, spoke 7, funded by NextGenerationEU and the ERC StrEnQTh starting grant (project ID 804305). This project was funded by the European Union under the Horizon Europe programme - Grant Agreement 101080086 - NeQST. The views and opinions expressed are however those of the author(s) and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.