19 citations · 61 across the 14 of their papers we have counts for
14 papers
HATT: Hamiltonian Adaptive Ternary Tree for Optimizing Fermion-to-Qubit Mapping
Yuhao Liu, Kevin Yao, Jonathan Hong +5
This paper introduces the Hamiltonian-Adaptive Ternary Tree (HATT) framework to compile optimized Fermion-to-qubit mapping for specific Fermionic Hamiltonians. In the simulation of…
MarQSim: Reconciling Determinism and Randomness in Compiler Optimization for Quantum Simulation
Xiuqi Cao, Junyu Zhou, Yuhao Liu +2
Quantum simulation, fundamental in quantum algorithm design, extends far beyond its foundational roots, powering diverse quantum computing applications. However, optimizing the com…
Fermihedral: On the Optimal Compilation for Fermion-to-Qubit Encoding
Yuhao Liu, Shize Che, Junyu Zhou +2
This paper introduces Fermihedral, a compiler framework focusing on discovering the optimal Fermion-to-qubit encoding for targeted Fermionic Hamiltonians. Fermion-to-qubit encoding…
Bosehedral: Compiler Optimization for Bosonic Quantum Computing
Junyu Zhou, Yuhao Liu, Yunong Shi +2
Bosonic quantum computing, based on the infinite-dimensional qumodes, has shown promise for various practical applications that are classically hard. However, the lack of compiler…
QASMTrans: A QASM based Quantum Transpiler Framework for NISQ Devices
Fei Hua, Meng Wang, Gushu Li +8
The success of a quantum algorithm hinges on the ability to orchestrate a successful application induction. Detrimental overheads in mapping general quantum circuits to physically…
OneQ: A Compilation Framework for Photonic One-Way Quantum Computation
Hezi Zhang, Anbang Wu, Yuke Wang +4
In this paper, we propose OneQ, the first optimizing compilation framework for one-way quantum computation towards realistic photonic quantum architectures. Unlike previous compila…
AutoComm: A Framework for Enabling Efficient Communication in Distributed Quantum Programs
Anbang Wu, Hezi Zhang, Gushu Li +3
Distributed quantum computing (DQC) is a promising approach to extending the computational power of near-term quantum devices. However, the non-local quantum communication between…
Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
Anbang Wu, Gushu Li, Yuke Wang +3
Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algor…
Mapping Surface Code to Superconducting Quantum Processors
Anbang Wu, Gushu Li, Hezi Zhang +3
In this paper, we formally describe the three challenges of mapping surface code on superconducting devices, and present a comprehensive synthesis framework to overcome these chall…
QECV: Quantum Error Correction Verification
Anbang Wu, Gushu Li, Hezi Zhang +3
Quantum Error Correction (QEC) is essential for fault-tolerant quantum copmutation, and its implementation is a very sophisticated process involving both quantum and classical hard…
Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training
Anbang Wu, Gushu Li, Yufei Ding +1
Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algor…
Paulihedral: A Generalized Block-Wise Compiler Optimization Framework For Quantum Simulation Kernels
Gushu Li, Anbang Wu, Yunong Shi +3
The quantum simulation kernel is an important subroutine appearing as a very long gate sequence in many quantum programs. In this paper, we propose Paulihedral, a block-wise compil…
Software-Hardware Co-Optimization for Computational Chemistry on Superconducting Quantum Processors
Gushu Li, Yunong Shi, Ali Javadi-Abhari
Computational chemistry is the leading application to demonstrate the advantage of quantum computing in the near term. However, large-scale simulation of chemical systems on quantu…
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li +4
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the…