141 citations · 261 across the 45 of their papers we have counts for
13 papers · 1 filter
Neural Network-Based Frequency Optimization for Superconducting Quantum Chips
Bin-Han Lu, Peng Wang, Qing-Song Li +3
Optimizing the frequency configuration of qubits and quantum gates in superconducting quantum chips presents a complex NP-complete optimization challenge. This process is critical…
Quantum homotopy analysis method with quantum-compatible linearization for nonlinear partial differential equations
Cheng Xue, Xiao-Fan Xu, Xi-Ning Zhuang +8
Nonlinear partial differential equations (PDEs) are crucial for modeling complex fluid dynamics and are foundational to many computational fluid dynamics (CFD) applications. Howeve…
Quantum Computational Insurance and Actuarial Science
Huan-Yu Liu, Xi-Ning Zhuang, Chao Wang +7
In recent years, quantum computation has been rapidly advancing, driving a technological revolution with significant potential across various sectors, particularly in finance. Desp…
Scalable Constant-Time Logical Gates for Large-Scale Quantum Computation Using Window-Based Correlated Decoding
Jiaxuan Zhang, Zhao-Yun Chen, Jia-Ning Li +6
Large-scale quantum computation requires to be performed in the fault-tolerant manner. One crucial challenge of fault-tolerant quantum computing (FTQC) is reducing the overhead of…
HiMA: Hierarchical Quantum Microarchitecture for Qubit-Scaling and Quantum Process-Level Parallelism
Qi Zhou, Zi-Hao Mei, Han-Qing Shi +10
Quantum computing holds immense potential for addressing a myriad of intricate challenges, which is significantly amplified when scaled to thousands of qubits. However, a major cha…
Realizing Scalable Conditional Operations through Auxiliary Energy Levels
Sheng Zhang, Peng Duan, Yun-Jie Wang +19
In the noisy intermediate-scale quantum (NISQ) era, flexible quantum operations are essential for advancing large-scale quantum computing, as they enable shorter circuits that miti…
Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation
Shengbin Wang, Guihui Li, Zhimin Wang +5
Solving combinatorial optimization problems using variational quantum algorithms (VQAs) might be a promise application in the NISQ era. However, the limited trainability of VQAs co…
A hybrid quantum-classical framework for computational fluid dynamics
Chuang-Chao Ye, Ning-Bo An, Teng-Yang Ma +4
Great progress has been made in quantum computing in recent years, providing opportunities to overcome computation resource poverty in many scientific computations like computation…
Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers
Zhao-Yun Chen, Teng-Yang Ma, Chuang-Chao Ye +29
Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. T…
Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance
Xi-Ning Zhuang, Zhao-Yun Chen, Cheng Xue +7
Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, c…
Simulation of open quantum systems on universal quantum computers
Huan-Yu Liu, Xiaoshui Lin, Zhao-Yun Chen +8
The rapid development of quantum computers has enabled demonstrations of quantum advantages on various tasks. However, real quantum systems are always dissipative due to their inev…
Demonstrating a universal logical gate set in error-detecting surface codes on a superconducting quantum processor
Jiaxuan Zhang, Zhao-Yun Chen, Yun-Jie Wang +6
Fault-tolerant quantum computing (FTQC) is essential for achieving large-scale practical quantum computation. Implementing arbitrary FTQC requires the execution of a universal gate…
Towards Fault-Tolerant Quantum Deep Learning: Designing and Analyzing Quantum ResNet and Transformer with Quantum Arithmetic and Linear Algebra Primitives
Xiao-Fan Xu, Cheng Xue, Xi-Ning Zhuang +8
Achieving a practical quantum speedup for deep neural networks (DNNs) remains a central yet elusive goal, hindered by the dual challenges of constructing deep architectures and the…