3 citations · 3 across the 2 of their papers we have counts for
12 papers
Double-bracket quantum algorithms for thermal state preparation
Andrew Wright, Reyhaneh Aghaei Saem, Supanut Thanasilp +2
We propose quantum algorithms for preparing thermal states via the simulation of the thermofield double states. The key idea is to leverage double-bracket quantum algorithms to imp…
Pitfalls when tackling the exponential concentration of parameterized quantum models
Reyhaneh Aghaei Saem, Behrang Tafreshi, Zoë Holmes +1
Identifying scalable circuit architectures remains a central challenge in variational quantum computing and quantum machine learning. Many approaches have been proposed to mitigate…
Quantifying Quantum Computational Advantage on a Processor of Ultracold Atoms
Yong-Guang Zheng, Ying-Chao Shen, Wei-Yong Zhang +15
Nonequilibrium dynamics of quantum many-body systems is challenging for classical computing, providing opportunities for demonstrating practical quantum computational advantage wit…
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp +5
Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the…
IQP Born Machines under Data-dependent and Agnostic Initialization Strategies
Sacha Lerch, Joseph Bowles, Ricard Puig +3
Quantum circuit Born machines based on instantaneous quantum polynomial-time (IQP) circuits are natural candidates for quantum generative modeling, both because of their probabilis…
Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms
Kasidit Srimahajariyapong, Supanut Thanasilp, Thiparat Chotibut
Variational quantum algorithms (VQAs) promise near-term quantum advantage, yet parametrized quantum states commonly built from the digital gate-based approach often suffer from sca…