Predicting quantum dynamical cost landscapes with deep learning

TL;DR

This paper introduces a deep learning approach to model quantum cost landscapes, significantly improving accuracy and speed over Bayesian methods, enabling faster experimental optimization and landscape analysis.

Contribution

The paper presents a novel deep neural network model for quantum cost landscapes, outperforming existing Bayesian methods in accuracy and computational efficiency.

Findings

Deep learning models achieve an order of magnitude better accuracy.

Faster extraction of landscape information compared to traditional simulations.

Enables real-time experimental optimization and landscape classification.

Abstract

State-of-the-art quantum algorithms routinely tune dynamically parametrized cost functionals for combinatorics, machine learning, equation-solving, or energy minimization. However, large search complexity often demands many (noisy) quantum measurements, leading to the increasing use of classical probability models to estimate which areas in the cost functional landscape are of highest interest. Introducing deep learning based modelling of the landscape, we demonstrate an order of magnitude increases in accuracy and speed over state-of-the-art Bayesian methods. Moreover, once trained the deep neural network enables the extraction of information at a much faster rate than conventional numerical simulation. This allows for on-the-fly experimental optimizations and detailed classification of complexity and navigability throughout the phase diagram of the landscape.