221 citations · 602 across the 23 of their papers we have counts for
23 papers
The cost of simulating classically tractable quantum circuits and dynamics
Su Yeon Chang, Supanut Thanasilp, Zoë Holmes +1
Determining whether a quantum evolution can be efficiently simulated classically is central to understanding the boundary between classical and quantum computation. However, polyno…
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…
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…
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…
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…
Role of scrambling and noise in temporal information processing with quantum systems
Weijie Xiong, Zoë Holmes, Armando Angrisani +3
Scrambling quantum systems have attracted attention as effective substrates for temporal information processing. Here we consider a quantum reservoir processing framework that capt…
A unifying account of warm start guarantees for patches of quantum landscapes
Hela Mhiri, Ricard Puig, Sacha Lerch +4
Barren plateaus are fundamentally a statement about quantum loss landscapes on average but there can, and generally will, exist patches of barren plateau landscapes with substantia…
Efficient quantum-enhanced classical simulation for patches of quantum landscapes
Sacha Lerch, Ricard Puig, Manuel S. Rudolph +5
Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous. Not only does this ensure that quantum computers are…
Latent Style-based Quantum GAN for high-quality Image Generation
Su Yeon Chang, Supanut Thanasilp, Bertrand Le Saux +2
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images…
Barren Plateaus in Variational Quantum Computing
Martin Larocca, Supanut Thanasilp, Samson Wang +7
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Platea…
Variational quantum simulation: a case study for understanding warm starts
Ricard Puig, Marc Drudis, Supanut Thanasilp +1
The barren plateau phenomenon, characterized by loss gradients that vanish exponentially with system size, poses a challenge to scaling variational quantum algorithms. Here we expl…
On fundamental aspects of quantum extreme learning machines
Weijie Xiong, Giorgio Facelli, Mehrad Sahebi +4
Quantum Extreme Learning Machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a…
Does provable absence of barren plateaus imply classical simulability?
M. Cerezo, Martin Larocca, Diego García-Martín +9
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and…
A Unified Framework for Trace-induced Quantum Kernels
Beng Yee Gan, Daniel Leykam, Supanut Thanasilp
Quantum kernel methods are promising candidates for achieving a practical quantum advantage for certain machine learning tasks. Similar to classical machine learning, an exact form…
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…
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…
Exponential concentration in quantum kernel methods
Supanut Thanasilp, Samson Wang, M. Cerezo +1
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other…
Subtleties in the trainability of quantum machine learning models
Supanut Thanasilp, Samson Wang, Nhat A. Nghiem +2
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called Quantum Machine Learning (QML), aims to achiev…
Quantum supremacy and quantum phase transitions
Supanut Thanasilp, Jirawat Tangpanitanon, Marc-Antoine Lemonde +2
Demonstrating the ability of existing quantum platforms to perform certain computational tasks intractable to classical computers represents a cornerstone in quantum computing. Des…
Qubit-efficient encoding schemes for binary optimisation problems
Benjamin Tan, Marc-Antoine Lemonde, Supanut Thanasilp +2
We propose and analyze a set of variational quantum algorithms for solving quadratic unconstrained binary optimization problems where a problem consisting of classical variab…
Expressibility and trainability of parameterized analog quantum systems for machine learning applications
Jirawat Tangpanitanon, Supanut Thanasilp, Ninnat Dangniam +2
Parameterized quantum evolution is the main ingredient in variational quantum algorithms for near-term quantum devices. In digital quantum computing, it has been shown that random…
Quantum supremacy in driven quantum many-body systems
Jirawat Tangpanitanon, Supanut Thanasilp, Marc-Antoine Lemonde +2
A crucial milestone in the field of quantum simulation and computation is to demonstrate that a quantum device can compute certain tasks that are impossible to reproduce by a class…
Quantum supremacy with analog quantum processors for material science and machine learning
Jirawat Tangpanitanon, Supanut Thanasilp, Marc-Antoine Lemonde +1
Quantum supremacy is the ability of quantum processors to outperform classical computers at certain tasks. In digital random quantum circuit approaches for supremacy, the output di…