332 citations · 2.8k across the 55 of their papers we have counts for
15 papers · 1 filter
Quantum Power Flows: From Theory to Practice
Junyu Liu, Han Zheng, Masanori Hanada +2
Climate change is becoming one of the greatest challenges to the sustainable development of modern society. Renewable energies with low density greatly complicate the online optimi…
SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture
Han Zheng, Christopher Kang, Gokul Subramanian Ravi +4
We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries…
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Zimu Li, Zihan Pengmei, Han Zheng +3
Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general…
Stochastic noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele +3
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochas…
Quantum Computing Methods for Supply Chain Management
Hansheng Jiang, Zuo-Jun Max Shen, Junyu Liu
Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum…
Symmetric Pruning in Quantum Neural Networks
Xinbiao Wang, Junyu Liu, Tongliang Liu +3
Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance of ground states preparation (GSP), this task is classica…
Data centers with quantum random access memory and quantum networks
Junyu Liu, Connor T. Hann, Liang Jiang
In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, an…
Toward Super-polynomial Quantum Speedup of Equivariant Quantum Algorithms with SU() Symmetry
Han Zheng, Zimu Li, Sergii Strelchuk +2
We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU sym…
Laziness, Barren Plateau, and Noise in Machine Learning
Junyu Liu, Zexi Lin, Liang Jiang
We define \emph{laziness} to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is expone…
Existence and non-degeneracy of positive multi-bubbling solutions to critical elliptic systems of Hamiltonian type
Qing Guo, Junyuan Liu, Shuangjie Peng
This paper deals with the following critical elliptic systems of Hamiltonian type, which are variants of the critical Lane-Emden systems and analogous to the prescribed curvature p…
Quantum Kerr Learning
Junyu Liu, Changchun Zhong, Matthew Otten +5
Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven scienc…
Estimating the randomness of quantum circuit ensembles up to 50 qubits
Minzhao Liu, Junyu Liu, Yuri Alexeev +1
Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole inf…
Quantum Simulation for High Energy Physics
Christian W. Bauer, Zohreh Davoudi, A. Baha Balantekin +28
It is for the first time that Quantum Simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this…
Embedding Learning in Hybrid Quantum-Classical Neural Networks
Minzhao Liu, Junyu Liu, Rui Liu +4
Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning par…
Analytic theory for the dynamics of wide quantum neural networks
Junyu Liu, Khadijeh Najafi, Kunal Sharma +3
Parameterized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problem…