Oscar Wallis, Stefano Mensa, et al.
QCE 2025
Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practical implementations crucially depend on the effectiveness of quantum circuit training, which can be severely limited by phenomena such as barren plateaus. While, in general, dissipation is detrimental for quantum algorithms, and noise itself can actually induce barren plateaus, here we describe how the inclusion of properly engineered Markovian losses after each unitary quantum circuit layer allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.
Oscar Wallis, Stefano Mensa, et al.
QCE 2025
Ritajit Majumdar, Dhiraj Madan, et al.
VLSID 2024
Anand Natarajan, Chinmay Nirkhe
QIP 2024
Yukio Kawashima, Tanvi Gujarati, et al.
ACS Fall 2023