233 citations · 386 across the 10 of their papers we have counts for
12 papers
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…
Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networks
Chia-Tso Lai, Carsten Blank, Peter Schmelcher +1
Quadratic Unconstrained Binary Optimization (QUBO) sits at the heart of many industries and academic fields such as logistics, supply chain, finance, pharmaceutical science, chemis…
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…
Hierarchical quantum circuit representations for neural architecture search
Matt Lourens, Ilya Sinayskiy, Daniel K. Park +2
Machine learning with hierarchical quantum circuits, usually referred to as Quantum Convolutional Neural Networks (QCNNs), is a promising prospect for near-term quantum computing.…
Compact quantum kernel-based binary classifier
Carsten Blank, Adenilton J. da Silva, Lucas P. de Albuquerque +2
Quantum computing opens exciting opportunities for kernel-based machine learning methods, which have broad applications in data analysis. Recent works show that quantum computers c…
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…
Robust quantum classifier with minimal overhead
Daniel K. Park, Carsten Blank, Francesco Petruccione
To witness quantum advantages in practical settings, substantial efforts are required not only at the hardware level but also on theoretical research to reduce the computational co…
Quantum State Discrimination for Supervised Classification
Roberto Giuntini, Hector Freytes, Daniel K. Park +4
In this paper we investigate the connection between quantum information theory and machine learning. In particular, we show how quantum state discrimination can represent a useful…
Quantum-enhanced analysis of discrete stochastic processes
Carsten Blank, Daniel K. Park, Francesco Petruccione
Discrete stochastic processes (DSP) are instrumental for modelling the dynamics of probabilistic systems and have a wide spectrum of applications in science and engineering. DSPs a…
The theory of the quantum kernel-based binary classifier
Daniel K. Park, Carsten Blank, Francesco Petruccione
Binary classification is a fundamental problem in machine learning. Recent development of quantum similarity-based binary classifiers and kernel method that exploit quantum interfe…
Quantum classifier with tailored quantum kernel
Carsten Blank, Daniel K. Park, June-Koo Kevin Rhee +1
Kernel methods have a wide spectrum of applications in machine learning. Recently, a link between quantum computing and kernel theory has been formally established, opening up oppo…
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Ville Bergholm, Josh Izaac, Maria Schuld +65
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices,…