Supanut Thanasilp

According to our database, Supanut Thanasilp authored at least 9 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

A Review of Barren Plateaus in Variational Quantum Computing.

Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob D. Biamonte, Patrick J. Coles, Lukasz Cincio, Jarrod R. McClean, Zoë Holmes, Marco Cerezo

CoRR, 2024

Variational quantum simulation: a case study for understanding warm starts.

Ricard Puig-i-Valls, Marc Drudis, Supanut Thanasilp, Zoë Holmes

CoRR, 2024

2023

Subtleties in the trainability of quantum machine learning models.

Supanut Thanasilp, Samson Wang, Nhat A. Nghiem, Patrick J. Coles, Marco Cerezo

Quantum Mach. Intell., June, 2023

On fundamental aspects of quantum extreme learning machines.

Weijie Xiong, Giorgio Facelli, Mehrad Sahebi, Owen Agnel, Thiparat Chotibut, Supanut Thanasilp, Zoë Holmes

CoRR, 2023

Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing.

Marco Cerezo, Martin Larocca, Diego García-Martín, Nelson L. Diaz, Paolo Braccia, Enrico Fontana, Manuel S. Rudolph, Pablo Bermejo, Aroosa Ijaz, Supanut Thanasilp, Eric R. Anschuetz, Zoë Holmes

CoRR, 2023

A Unified Framework for Trace-induced Quantum Kernels.

Beng Yee Gan, Daniel Leykam, Supanut Thanasilp

CoRR, 2023

Trainability barriers and opportunities in quantum generative modeling.

Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp, Oriel Kiss, Sofia Vallecorsa, Michele Grossi, Zoë Holmes

CoRR, 2023

2022

Exponential concentration and untrainability in quantum kernel methods.

Supanut Thanasilp, Samson Wang, Marco Cerezo, Zoë Holmes

CoRR, 2022

2021

Qubit-efficient encoding schemes for binary optimisation problems.

Benjamin Tan, Marc-Antoine Lemonde, Supanut Thanasilp, Jirawat Tangpanitanon, Dimitris G. Angelakis

Quantum, 2021