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20182025
most citedDiffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling

1 citations · 1 across the 7 of their papers we have counts for

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quant-ph2025

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…

quant-ph2025

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…

quant-ph2025

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…

quant-ph2025

Dissipation alters modes of information encoding in small quantum reservoirs near criticality

Krai Cheamsawat, Thiparat Chotibut

Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet identifying the mechan…

quant-ph2025

Practical Quantum Circuit Implementation for Simulating Coupled Classical Oscillators

Natt Luangsirapornchai, Peeranat Sanglaor, Apimuk Sornsaeng +4

Simulating large-scale coupled-oscillator systems presents substantial computational challenges for classical algorithms, particularly when pursuing first-principles analyses in th…

quant-ph2023

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…