106 citations · 386 across the 19 of their papers we have counts for
23 papers
Implementation of the Density-functional Theory on Quantum Computers with Linear Scaling with respect to the Number of Atoms
Taehee Ko, Xiantao Li, Chunhao Wang
Density-functional theory (DFT) has revolutionized computer simulations in chemistry and material science. A faithful implementation of the theory requires self-consistent calculat…
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Ahmed W. Moawad, Anastasia Janas, Ujjwal Baid +229
The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
Maruf Adewole, Jeffrey D. Rudie, Anu Gbadamosi +48
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 ye…
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Dominic LaBella, Maruf Adewole, Michelle Alonso-Basanta +57
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists,…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +70
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
Hongwei Bran Li, Gian Marco Conte, Qingqiao Hu +62
Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input m…
Efficient Quantum Algorithms for Quantum Optimal Control
Xiantao Li, Chunhao Wang
In this paper, we present efficient quantum algorithms that are exponentially faster than classical algorithms for solving the quantum optimal control problem. This problem involve…
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation
Yang Chen, Zhenyu Yang, Jingtong Zhao +4
We developed a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). This model wa…
Simulating Markovian open quantum systems using higher-order series expansion
Xiantao Li, Chunhao Wang
We present an efficient quantum algorithm for simulating the dynamics of Markovian open quantum systems. The performance of our algorithm is similar to the previous state-of-the-ar…
Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants
Andrew M. Childs, Tongyang Li, Jin-Peng Liu +2
Given a convex function , the problem of sampling from a distribution is called log-concave sampling. This task has wide app…
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
Zhenyu Yang, Kyle Lafata, Eugene Vaios +4
The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image…
Thermal State Preparation via Rounding Promises
Patrick Rall, Chunhao Wang, Pawel Wocjan
A promising avenue for the preparation of Gibbs states on a quantum computer is to simulate the physical thermalization process. The Davies generator describes the dynamics of an o…
A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation
Zhenyu Yang, Zongsheng Hu, Hangjie Ji +4
Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) based glioma segment…
An interpretable planning bot for pancreas stereotactic body radiation therapy
Jiahan Zhang, Chunhao Wang, Yang Sheng +9
Pancreas stereotactic body radiotherapy treatment planning requires planners to make sequential, time consuming interactions with the treatment planning system (TPS) to reach the o…
An Artificial Intelligence-Driven Agent for Real-Time Head-and-Neck IMRT Plan Generation using Conditional Generative Adversarial Network (cGAN)
Xinyi Li, Yang Sheng, Jiahan Zhang +6
Purpose: To develop an Artificial Intelligence (AI) agent for fully-automated rapid head and neck (H&N) IMRT plan generation without time-consuming inverse planning.$$$$ Methods: T…
On the Quantum Complexity of Closest Pair and Related Problems
Scott Aaronson, Nai-Hui Chia, Han-Hsuan Lin +2
The closest pair problem is a fundamental problem of computational geometry: given a set of points in a -dimensional space, find a pair with the smallest distance. A classic…
Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning
Nai-Hui Chia, András Gilyén, Tongyang Li +3
We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang's breakthrough quant…
Quantum algorithm for estimating volumes of convex bodies
Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung +3
Estimating the volume of a convex body is a central problem in convex geometry and can be viewed as a continuous version of counting. We present a quantum algorithm that estimates…
Quantum-inspired sublinear algorithm for solving low-rank semidefinite programming
Nai-Hui Chia, Tongyang Li, Han-Hsuan Lin +1
Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. In this paper, we present a proof-of-principle subli…
Quantum-inspired sublinear classical algorithms for solving low-rank linear systems
Nai-Hui Chia, Han-Hsuan Lin, Chunhao Wang
We present classical sublinear-time algorithms for solving low-rank linear systems of equations. Our algorithms are inspired by the HHL quantum algorithm for solving linear systems…
A quantum algorithm for simulating non-sparse Hamiltonians
Chunhao Wang, Leonard Wossnig
We present a quantum algorithm for simulating the dynamics of Hamiltonians that are not necessarily sparse. Our algorithm is based on the input model where the entries of the Hamil…
Efficient Quantum Algorithms for Simulating Lindblad Evolution
Richard Cleve, Chunhao Wang
We consider the natural generalization of the Schrödinger equation to Markovian open system dynamics: the so-called the Lindblad equation. We give a quantum algorithm for simulatin…
Near-linear constructions of exact unitary 2-designs
Richard Cleve, Debbie Leung, Li Liu +1
A unitary 2-design can be viewed as a quantum analogue of a 2-universal hash function: it is indistinguishable from a truly random unitary by any procedure that queries it twice. W…