Curator's Take
AI Commentary
This article introduces PEPRino, a hyper‑parameter‑free, gradient‑free optimal‑control method that uses infinite‑order response theory to map the control landscape far more efficiently than traditional techniques such as CRAB with Nelder‑Mead. By demonstrating faster convergence and lower computational cost for high‑fidelity two‑ and three‑qubit QFT pulses, it shows a practical route toward scaling pulse engineering on near‑term devices where manual tuning is prohibitive. If the approach extends to larger registers, it could streamline calibration pipelines and accelerate experimental rollout of complex quantum algorithms.
— Mark Eatherly
Summary
Optimal control problems arise in a wide range of scientific disciplines, but the corresponding optimization algorithms often display a strong dependence on hyperparameters that significantly influence performance and convergence. For the optimal implementation of quantum algorithms, these challenges are further amplified by high-dimensional control landscapes and the need for high-fidelity operations. Here, we propose an algorithm for optimal control problems in quantum computing to efficiently generate high-fidelity control protocols for multi-qubit systems in a hyperparameter and gradient free manner. The method, referred to as Pulse Engineering via Projection of response functions at infinite nonlinear order (PEPRino), leverages the framework of response theory to navigate the control landscape to find high-fidelity implementations. This is achieved by determining the control landscape via response functions to infinite order, efficiently evaluated by resummation in terms of the first and second order response function. To demonstrate the approach, we apply it to quantum systems consisting of two and three qubits for the optimal implementation of the Quantum Fourier Transform (QFT). We benchmark the proposed algorithm against the Chopped Random Basis (CRAB) algorithm utilizing the Nelder-Mead method, focusing on the 2-qubit scenario. The results indicate faster convergence regarding iteration steps and computational time, highlighting the advantages of our approach.