387 citations · 1.3k across the 27 of their papers we have counts for
28 papers · 1 filter
Scalable quantum measurement error mitigation via conditional independence and transfer learning
ChangWon Lee, Daniel K. Park
Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep lea…
Variational quantum state discriminator for supervised machine learning
Dongkeun Lee, Kyunghyun Baek, Joonsuk Huh +1
Quantum state discrimination (QSD) is a fundamental task in quantum information processing with numerous applications. We present a variational quantum algorithm that performs the…
Variational quantum one-class classifier
Gunhee Park, Joonsuk Huh, Daniel K. Park
One-class classification is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm…
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.…
Classical-to-quantum convolutional neural network transfer learning
Juhyeon Kim, Joonsuk Huh, Daniel K. Park
Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained…
Variational Quantum Approximate Support Vector Machine with Inference Transfer
Siheon Park, Daniel K. Park, June-Koo Kevin Rhee
A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variati…
Linear-depth quantum circuits for multiqubit controlled gates
Adenilton J. da Silva, Daniel K. Park
Quantum circuit depth minimization is critical for practical applications of circuit-based quantum computation. In this work, we present a systematic procedure to decompose multiqu…
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…
Quantum readout error mitigation via deep learning
Jihye Kim, Byungdu Oh, Yonuk Chong +2
Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols…
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…
Quantum convolutional neural network for classical data classification
Tak Hur, Leeseok Kim, Daniel K. Park
With the rapid advance of quantum machine learning, several proposals for the quantum-analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark fully…
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…
Circuit-based quantum random access memory for classical data with continuous amplitudes
Tiago M. L. de Veras, Ismael C. S. de Araujo, Daniel K. Park +1
Loading data in a quantum device is required in several quantum computing applications. Without an efficient loading procedure, the cost to initialize the algorithms can dominate t…
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…
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…
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…
Parallel quantum trajectories via forking for sampling without redundancy
Daniel K. Park, Ilya Sinayskiy, Mark Fingerhuth +2
The computational cost of preparing a quantum state can be substantial depending on the structure of data to be encoded. Many quantum algorithms require repeated sampling to find t…
Circuit-Based Quantum Random Access Memory for Classical Data
Daniel K. Park, Francesco Petruccione, June-Koo Kevin Rhee
A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition state…
Efficient continuous wave noise spectroscopy beyond weak coupling
Kyle Willick, Daniel K. Park, Jonathan Baugh
The optimization of quantum control for physical qubits relies on accurate noise characterization. Probing the spectral density of semi-classical phase noise using a spin in…
Noise-tolerant parity learning with one quantum bit
Daniel K. Park, June-Koo K. Rhee, Soonchil Lee
Demonstrating quantum advantage with less powerful but more realistic devices is of great importance in modern quantum information science. Recently, a significant quantum speedup…
Estimating the coherence of noise in quantum control of a solid-state qubit
Guanru Feng, Joel J. Wallman, Brandon Buonacorsi +6
To exploit a given physical system for quantum information processing, it is critical to understand the different types of noise affecting quantum control. Distinguishing coherent…
Randomized benchmarking of quantum gates implemented by electron spin resonance
Daniel K. Park, Guanru Feng, Robabeh Rahimi +2
Spin systems controlled and probed by magnetic resonance have been valuable for testing the ideas of quantum control and quantum error correction. This paper introduces an X-band p…