CERN Accelerating science

In this talk we will dive into the topic of barren plateaus and investigate a new method to avoid them. Barren plateaus appear to be a major obstacle for using variational quantum algorithms to simulate large-scale quantum systems or to replace traditional machine learning algorithms. They can be caused by multiple factors such as the expressivity of the ansatz, excessive entanglement, the locality of observables under consideration, or even hardware noise. We propose classical splitting of parametric ansatz circuits to avoid barren plateaus. Classical splitting is realized by subdividing an $N$ qubit ansatz into multiple ansätze that consist of $\mathcal{O}(\log N)$ qubits. We show that such an approach allows for avoiding barren plateaus and carry out numerical experiments, and perform binary classification on classical and quantum datasets. Moreover, we propose an extension of the ansatz that is compatible with variational quantum simulations. Finally, we discuss a speed-up for gradient-based optimization and hardware implementation, robustness against noise and parallelization, making classical splitting an ideal tool for noisy intermediate scale quantum (NISQ) applications.

About the speaker

Cenk Tüysüz is PhD student at Centre for Quantum Technologies and Applications (CQTA) of Deutsches Elektronen-Synchrotron (DESY) and Humboldt University of Berlin. Cenk is working on understanding trainability issues of variational quantum algorithms (VQAs) and how to use VQAs and other Quantum Computing methods to solve problems in High Energy Physics.

Collaborators  
Cenk Tüysüz, Giuseppe Clemente, Arianna Crippa, Tobias Hartung, Stefan Kühn and Karl Jansen