As proposed and demonstrated by the Los Alamos team, the architectures and techniques proposed to mitigate or altogether avoid barren plateaus in variational quantum computing make them classically simulable. Courtesy LANL LANL NEWS RELEASE Variational quantum computing is a hybrid quantum-classical approach that has emerged as one of the most promising applications for quantum devices. But this approach is hindered by the “barren plateau” phenomenon, which undermines the approach’s machine learning training capabilities. As a team of Los Alamos researchers suggest in a recent perspective piece in Nature Communications, and as they go on to demonstrate with a simulated quantum neural network, the architectures and techniques proposed to mitigate or altogether avoid barren plateaus make them classically simulable. “Barren plateaus typically result from what is known in the field as the ‘curse of dimensionality,’ where models need to navigate very big spaces, and finding the solution is like finding a needle in a haystack,” said Marco Cerezo, Los Alamos physicist and lead author on the perspective. “One avoids barren plateaus and circumvents the curse of dimensionality by restricting the model to a small subspace. But that solution might mean that the model can just as efficiently be simulated classically.” If the connection between the absence of barren plateaus (for example, by restricting models to small subspaces) and classical simulability holds, the remedy for barren plateaus may prove worse than the problem. The advantage quantum computers have in solving machine learning tasks faster than classical supercomputers would be limited only to those models with no barren plateaus. Subspaces and classical simulability Variational quantum computing’s hybrid approach aims to solve tasks by classically optimizing the parameters of a quantum circuit, thus extending the advantage of classical neural networks to the quantum realm. However, the too-large space of possible quantum states (i.e., “the