176 citations · 288 across the 12 of their papers we have counts for
13 papers
Exact Finite-Channel Schur Moments of the Wigner-Smith Matrix with a Tunnel Barrier
Israel F. Araujo
We consider the Wigner--Smith time-delay matrix of a chaotic cavity with broken time-reversal symmetry, a finite number of equivalent open channels, and a uniform tunnel barrie…
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…
Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
Tak Hur, Israel F. Araujo, Daniel K. Park
Quantum embedding is a fundamental prerequisite for applying quantum machine learning techniques to classical data, and has substantial impacts on performance outcomes. In this stu…
Linear decomposition of approximate multi-controlled single qubit gates
Jefferson D. S. Silva, Thiago Melo D. Azevedo, Israel F. Araujo +1
We provide a method for compiling approximate multi-controlled single qubit gates into quantum circuits without ancilla qubits. The total number of elementary gates to decompose an…
Decomposition of Multi-controlled Special Unitary Single-Qubit Gates
Rafaella Vale, Thiago Melo D. Azevedo, Ismael C. S. Araújo +2
Multi-controlled unitary gates have been a subject of interest in quantum computing since its inception, and are widely used in quantum algorithms. The current state-of-the-art app…
Low-rank quantum state preparation
Israel F. Araujo, Carsten Blank, Ismael C. S. Araújo +1
Ubiquitous in quantum computing is the step to encode data into a quantum state. This process is called quantum state preparation, and its complexity for non-structured data is exp…
Configurable sublinear circuits for quantum state preparation
Israel F. Araujo, Daniel K. Park, Teresa B. Ludermir +3
The theory of quantum algorithms promises unprecedented benefits of harnessing the laws of quantum mechanics for solving certain computational problems. A persistent obstacle to us…
A divide-and-conquer algorithm for quantum state preparation
Israel F. Araujo, Daniel K. Park, Francesco Petruccione +1
Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers c…