18 citations · 41 across the 9 of their papers we have counts for
12 papers
Dyadic-Order Quantum Fractional Transforms: Circuit Constructions and Applications to Hartley and Cosine Transform Families
Matheus J. A. Oliveira, Israel F. Araujo, José R. de Oliveira Neto +1
This paper presents a generalized circuit framework for constructing Shih-type fractionalizations of unitary operators of dyadic order, i.e., operators satisfying .…
Noise-adaptive hybrid quantum convolutional neural networks based on depth-stratified feature extraction
Taehyun Kim, Israel F. Araujo, Daniel K. Park
Hierarchical quantum classifiers, such as quantum convolutional neural networks (QCNNs), represent recent progress toward designing effective and feasible architectures for quantum…
Tucker iterative quantum state preparation
Carsten Blank, Israel F. Araujo
Quantum state preparation is a fundamental component of quantum algorithms, particularly in quantum machine learning and data processing, where classical data must be encoded effic…
Quantum Multiplexer Simplification for State Preparation
José A. de Carvalho, Carlos A. Batista, Tiago M. L. de Veras +2
The initialization of quantum states or Quantum State Preparation (QSP) is a basic subroutine in quantum algorithms. In the worst case, general QSP algorithms are expensive due to…
Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
Changwon Lee, Israel F. Araujo, Dongha Kim +4
Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning, paving new directions for both quantum and classical data analysis. This a…
Quantum-inspired classification via efficient simulation of Helstrom measurement
Wooseop Hwang, Daniel K. Park, Israel F. Araujo +1
The Helstrom measurement (HM) is known to be the optimal strategy for distinguishing non-orthogonal quantum states with minimum error. Previously, a binary classifier based on clas…