Information theory and computation articles from across Nature Portfolio

Normal mode analysis is a crucial step in structural biology, but is based on an expensive diagonalisation of the system’s Hessian. Here the authors present INCHING, a GPU-based approach to accelerate this task up to >250 times over current methods for macromolecular assemblies.

The problem of reversibility within general quantum resource theories is still an open one. Here, the authors prove that a reversible entanglement manipulation framework (and, consequently, the concept of entanglement entropy) can be formally established by adjusting the setting to allow for probabilistic operations

In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.

Scalable quantum computers require quantum error-correcting codes that can robustly store information. Exploiting the structure of well-known classical codes may help create more efficient approaches to quantum error correction.

Most quantum processors rely on native interactions between pairs of qubits to generate quantum entangling gates. Now, by modulating the driving laser fields, gates that entangle a triplet or quartet of trapped-ion qubits have been realized, creating useful new components for quantum computing applications.

Increasing the number of parameters in a quantum neural network leads to a computational ‘phase transition’, beyond which training the network becomes significantly easier. An algebraic theory has been developed for this overparametrization phenomenon and predicts its onset above a certain parameter threshold.

To understand whether or not the design of machine learning systems can integrate domain expertise, a recent work proposes methodologies to synthesize domain science with machine learning, which shows added benefits.