42 citations · 106 across the 19 of their papers we have counts for
19 papers
Phase Transitions as the Breakdown of Statistical Indistinguishability
Taiyo Narita, Hideyuki Miyahara
We introduce a novel characterization of phase transitions based on hypothesis testing. In our formulation, a phase transition is defined as the breakdown of statistical indistingu…
Phase transition on a context-sensitive random language model with short range interactions
Yuma Toji, Jun Takahashi, Vwani Roychowdhury +1
Since the random language model was proposed by E. DeGiuli [Phys. Rev. Lett. 122, 128301], language models have been investigated intensively from the viewpoint of statistical mech…
Absence of symmetry in the Vicsek model
Yushin Takahashi, Kota Mitsui, Tsuyoshi Mizohata +1
The phase transition in the Vicsek model is widely believed to be associated with spontaneous symmetry breaking of the two-dimensional rotational symmetry . In this…
Berezinskii--Kosterlitz--Thouless transition in a context-sensitive random language model
Yuma Toji, Jun Takahashi, Vwani Roychowdhury +1
Several power-law critical properties involving different statistics in natural languages -- reminiscent of scaling properties of physical systems at or near phase transitions -- h…
Vicsek Model Meets DBSCAN: Cluster Phases in the Vicsek Model
Hideyuki Miyahara, Hyu Yoneki, Tsuyoshi Mizohata +1
The Vicsek model, which was originally proposed to explain the dynamics of bird flocking, exhibits a phase transition with respect to the absolute value of the mean velocity. Altho…
Emergent invariance and scaling properties in the collective return dynamics of a stock market
Hideyuki Miyahara, Hai Qian, Pavan Holur +1
Several works have observed heavy-tailed behavior in the distributions of returns in different markets, which are observable indicators of underlying complex dynamics. Such prior w…
Quantum Advantage in Variational Bayes Inference
Hideyuki Miyahara, Vwani Roychowdhury
Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models. The algorithm -- ins…
Quantum Approximation of Normalized Schatten Norms and Applications to Learning
Yiyou Chen, Hideyuki Miyahara, Louis-S. Bouchard +1
Efficient measures to determine similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity…
Decoherence Mitigation by Embedding a Logical Qubit in a Qudit
Hideyuki Miyahara, Yiyou Chen, Vwani Roychowdhury +1
Quantum information stored in a qubit is rapidly lost to the environment. The realization of robust qubits is one of the most important challenges in quantum computing. Herein we p…
Steady-state thermodynamics for population dynamics in fluctuating environments with side information
Hideyuki Miyahara
Steady-state thermodynamics (SST) is a relatively newly emerging subfield of physics, which deals with transitions between steady states. In this paper, we find an SST-like structu…
Ansatz-Independent Variational Quantum Classifier
Hideyuki Miyahara, Vwani Roychowdhury
The paradigm of variational quantum classifiers (VQCs) encodes \textit{classical information} as quantum states, followed by quantum processing and then measurements to generate cl…
Quantum Expectation-Maximization Algorithm
Hideyuki Miyahara, Kazuyuki Aihara, Wolfgang Lechner
Clustering algorithms are a cornerstone of machine learning applications. Recently, a quantum algorithm for clustering based on the k-means algorithm has been proposed by Kerenidis…
Many-body perturbation theory and fluctuation relations for interacting population dynamics
Hideyuki Miyahara
Population dynamics deals with the collective phenomena of living organisms, and it has attracted much attention since it is expected to explain how not only living organisms but a…
Work relations with measurement and feedback control on nonuniform temperature systems
Hideyuki Miyahara, Kazuyuki Aihara
The relation between the work performed to a system and the change of its free energy during a certain process is important in nonequilibrium statistical mechanics. In particular,…
A Quantum Extension of Variational Bayes Inference
Hideyuki Miyahara, Yuki Sughiyama
Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry. However, VB is known to suffer from the pr…
Deterministic Quantum Annealing Expectation-Maximization Algorithm
Hideyuki Miyahara, Koji Tsumura, Yuki Sughiyama
Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likeli…
Relaxation of the EM Algorithm via Quantum Annealing for Gaussian Mixture Models
Hideyuki Miyahara, Koji Tsumura, Yuki Sughiyama
We propose a modified expectation-maximization algorithm by introducing the concept of quantum annealing, which we call the deterministic quantum annealing expectation-maximization…
Relaxation of the EM Algorithm via Quantum Annealing
Hideyuki Miyahara, Koji Tsumura
The EM algorithm is a novel numerical method to obtain maximum likelihood estimates and is often used for practical calculations. However, many of maximum likelihood estimation pro…
Development of a two-particle self-consistent method for multi-orbital systems and its application to unconventional superconductors
Hideyuki Miyahara, Ryotaro Arita, Hiroaki Ikeda
We extend the two-particle self-consistent method proposed by Vilk and Tremblay (J. Phys. I France 7, 1309-1368 (1997)) to study superconductivity in multi-orbital systems. Startin…