Simulations of polymers? A quantum puzzle

Using computer systems to review polymers has at all times been a serious problem for scientific computation, particularly for lengthy and densely packed biomolecules, like DNA. New views at the moment are opening up by way of quantum computing. Scientists have now recast the fundamental fashions of polymer fashions as optimization issues that may be effectively solved with quantum computer systems. This novel method has made it attainable to harness the appreciable potential of those machines in a hitherto unexplored context.

The research, printed within the Physical Review Letters journal, has concerned Cristian Micheletti from SISSA, and Philipp Hauke and Pietro Faccioli from the University of Trento.

Many of the paradigms of scientific computing, from Monte Carlo methods to simulated annealing—the authors clarify—had been developed, not less than partially, to review the properties of polymers, together with organic ones similar to protein and DNA. On the one hand, the advance of quantum computer systems opens new eventualities for scientific computing generally. At the identical time, it requires the event of latest fashions apt for taking full benefit of this nice potential. In specific, quantum computer systems excel at fixing optimization duties. These issues usually contain discovering the optimum mixture of system variables in line with a preassigned scoring system.

Considering this, the authors have recast the fundamental polymer fashions by establishing a correspondence between every attainable polymer configuration and the options of an acceptable optimization drawback.

“Typically, polymer chains are directly modeled as a sequence of points in three-dimensional space. In classic simulations, this chain is then animated via progressive deformations, mimicking the dynamics of the polymer in nature,” clarify the authors. Now that we’re coming into the quantum computing period, it turns into pure to review polymers with these progressive methods. However, the descriptions based mostly on factors in 3D space can’t be simply used with quantum computer systems. Finding methods to avoid typical polymer descriptions is thus a problem that would open new views.

Micheletti explains that their “strategy was to encode all possible configurations of a system of polymers as solutions of a single optimization problem. The optimization problem is formulated in terms of Ising spin variables—one of the most common models in physics—which is efficiently solved with quantum computers. To simplify, an optimization problem on the Ising model can be viewed as a coloring puzzle. The challenge consists of assigning a blue or red color to each point of a lattice while respecting a large number of rules. For instance, points A and B should have different color, and so should points B and C; at the same time points A and C should be of same color. Quantum computers are extremely efficient at solving such problems, that is, at finding the color assignment that satisfies the largest number of given rules. In our case, at each found solution of the optimization problem, we could associate a specific polymer configuration. By repeating the search for solutions, we could thus collect an increasing number of polymer configurations, all statistically independent.”

The speedy growth of quantum computer systems recommend that these machines might be used to deal with scientific issues way more advanced than these addressable by typical computer systems. “This is why it is important to provide now the algorithmic bases for harnessing the potential of this new paradigm of scientific calculation.” say the researchers. “Our study provides a first example of how quantum computing can be used for studying key polymer models. In perspective, because our approach is general, it ought to provide a basis for tackling more complex and ambitious systems, such as long biopolymers in confined spaces, which are also key to understanding genome organization.”