14 citations · 26 across the 5 of their papers we have counts for
5 papers
SMT-AD: a scalable quantum-inspired anomaly detection approach
Apimuk Sornsaeng, Si Min Chan, Wenxuan Zhang +4
Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, we propose a highly paral…
Exploring the performance of superposition of product states: from 1D to 3D quantum spin systems
Apimuk Sornsaeng, Itai Arad, Dario Poletti
Tensor networks (TNs) are one of the best available tools to study many-body quantum systems. TNs are particularly suitable for one-dimensional local Hamiltonians, while their perf…
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…
Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
Apimuk Sornsaeng, Ninnat Dangniam, Thiparat Chotibut
Next Generation Reservoir Computing (NG-RC) is a modern class of model-free machine learning that enables an accurate forecasting of time series data generated by dynamical systems…
Quantum diffusion map for nonlinear dimensionality reduction
Apimuk Sornsaeng, Ninnat Dangniam, Pantita Palittapongarnpim +1
Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a…