Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Daniel K, Park"'
Autor:
Aidan Pellow-Jarman, Shane McFarthing, Ilya Sinayskiy, Daniel K. Park, Anban Pillay, Francesco Petruccione
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimization problems. The QAOA utilizes a q
Externí odkaz:
https://doaj.org/article/4f12781da6c949c9a140e085e0b99760
Autor:
Anthony Russo, Daniel K Park, Todd Lansford, Pierce Nunley, Timothy A Peppers, Joshua J Wind, Hamid Hassanzadeh, Joseph Sembrano, Jung Yoo, Jonathan Sales
Publikováno v:
BMC Musculoskeletal Disorders, Vol 25, Iss 1, Pp 1-9 (2024)
Abstract Background The current report investigates fusion rates and patient-reported outcomes following lumbar spinal surgery using cellular bone allograft (CBA) in patients with risk factors for non-union. Methods A prospective, open label study wa
Externí odkaz:
https://doaj.org/article/d7d0434def4642619f542d5253bdff44
Publikováno v:
North American Spine Society Journal, Vol 17, Iss , Pp 100314- (2024)
ABSTRACT: Background: There is growing interest in transitioning various surgical procedures to the outpatient care setting. However, for Medicare patients, the site of service for surgical procedures is influenced by regulations within the Inpatient
Externí odkaz:
https://doaj.org/article/00a4f4b04c204827abcea430e4976276
Autor:
Daniel K. Park, Joshua J. Wind, Todd Lansford, Pierce Nunley, Timothy A. Peppers, Anthony Russo, Hamid Hassanzadeh, Jonathan Sembrano, Jung Yoo, Jonathan Sales
Publikováno v:
BMC Musculoskeletal Disorders, Vol 24, Iss 1, Pp 1-7 (2023)
Abstract Background Autologous bone grafts are the gold standard for spinal fusion; however, harvesting autologous bone can result in donor site infection, hematomas, increased operative time, and prolonged pain. Cellular bone allografts (CBAs) are a
Externí odkaz:
https://doaj.org/article/d16056a80b91468ca2ea0f7b0214ea67
Publikováno v:
npj Quantum Information, Vol 9, Iss 1, Pp 1-15 (2023)
Abstract Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns. Neural Architecture Search (NAS)
Externí odkaz:
https://doaj.org/article/bdb291692fe34be3985c07dd27e3a2b0
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract 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 Variational Quantum Approximate Support Vector Machine (VQASVM) algo
Externí odkaz:
https://doaj.org/article/db6bdd6e9da44f36920e9feb1a7cbcf6
Autor:
Hyeondo Oh, Daniel K Park
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035052 (2024)
Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection
Externí odkaz:
https://doaj.org/article/804d35ab11d84723a8c379a68c46e543
Publikováno v:
npj Quantum Information, Vol 7, Iss 1, Pp 1-9 (2021)
Abstract Discrete stochastic processes (DSP) are instrumental for modeling the dynamics of probabilistic systems and have a wide spectrum of applications in science and engineering. DSPs are usually analyzed via Monte-Carlo methods since the number o
Externí odkaz:
https://doaj.org/article/70ee040d9b934c0c89b96c9c4ff0fd91
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract 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 can impose restrictions on possible quantum speedups. Known al
Externí odkaz:
https://doaj.org/article/a136e05410b94f9092cfe162388ad960
Autor:
Changwon Lee, Daniel K Park
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045051 (2023)
Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep learning-based quantum measurement error mitigation (QMEM) has exhibited
Externí odkaz:
https://doaj.org/article/a7616ca19e034ebdbd43c043bf1e70c5