The development of quantum computers is opening up previously unimaginable horizons in computing, promising to solve problems considered insurmountable for conventional computers, ranging from cryptography and pharmacology to the study of the physical and chemical properties of molecules and materials. However, existing quantum computers still have relatively limited computational capabilities. Into this phase of technological transformation comes the study just published in Science Advances, which shows the possibility of an unexpected alliance between methods used in quantum and traditional computing.
The research team, consisting of 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 (from 'Quadratic Unconstraint Binary Optimisation'), which makes it possible to take full advantage of the properties of certain quantum computers, called 'quantum annealers'.
The new study used the QUBO approach to simulate dense polymer mixtures, complex physical systems that play a key role in both biology and materials science, in a radically new way. The result? The use of quantum computers resulted in an increase in computational power compared to traditional techniques, providing an important example of the great future potential of these new technologies. However, the QUBO approach also proved particularly effective on traditional computers, allowing researchers to discover surprising properties of these polymer blends.
The implications could be far-reaching, as the approach used in the study is naturally transferable to many other molecular systems.
A new perspective inspired by quantum computing research
"For decades, a simulation technique called 'Monte Carlo' has been a reference method for studying complex systems, such as synthetic polymers or biological systems, such as DNA," explains Cristian Micheletti, who coordinated the study. Unfortunately, the efficiency of these simulations decreases rapidly as the density and size of the system increase. Therefore, the study of realistic systems, such as the organisation of chromosomes in the nucleus, requires an enormous amount of computational resources". Francesco Slongo, a PhD student at SISSA and first author of the study, continues: "Quantum computers promise an extraordinary increase in computational power, but with all the limitations of an emerging technology. This is where the new simulation strategy comes in, which can be applied to existing quantum computers of the future, but can also be successfully transferred to classical computers.
Unexpected boost for classical simulations
As Philipp Hauke and Pietro Faccioli note: "Quantum machines dedicated to solving problems formulated with the QUBO approach already exist today, and they can be very effective. To take advantage of such machines, we rewrote conventional polymer models in the QUBO formulation. To our surprise, we found that QUBO rewriting also proved advantageous on conventional computers, allowing us to simulate dense polymers faster than with established methods. As a result, we have discovered previously unknown properties for these systems, all on conventional computers.
Implications, challenges and future directions
In the past, physical models created to take advantage of innovations in computing technology have become so well established that they have been transferred to other fields. The best known case is that of lattice fluid models, which were developed for supercomputers in the 1990s but are now widely used for many other systems and types of computers. The study in Science Advances is another example, showing how methods inspired by quantum computing can pave the way for studying new materials and understanding how molecular systems of biological interest work.
The research was supported by the PNRR CN grant 00000013 CN-HPC, M4C2I1.4, Speaker 7, funded by NextGenerationEU and the ERC Seed Grant StrEnQTh (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 solely 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 funding body can be held responsible.