2 citations · 6 across the 13 of their papers we have counts for
13 papers
Efficient Transpilation of OpenQASM 3.0 Dynamic Circuits to CUDA-Q: Performance and Expressiveness Advantages
Vinooth Kulkarni, Jaehyun Lee, Adam Hutchings +4
Dynamic quantum circuits with mid-circuit measurement and classical feedforward are essential for near-term algorithms such as error mitigation, adaptive phase estimation, and Vari…
QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting
Vinooth Kulkarni, Aaron Orenstein, Xinpeng Li +3
The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world…
HOPPS: Hardware-Aware Optimal Phase Polynomial Synthesis with Blockwise Optimization for Quantum Circuits
Xinpeng Li, Ji Liu, Shuai Xu +2
Blocks composed of {CNOT, Rz} are ubiquitous in modern quantum applications, notably in circuits such as QAOA ansatzes and quantum adders. After compilation, many of them exhibit l…
State Dependent Optimization with Quantum Circuit Cutting
Xinpeng Li, Ji Liu, Jeffrey M. Larson +4
Quantum circuits can be reduced through optimization to better fit the constraints of quantum hardware. One such method, initial-state dependent optimization (ISDO), reduces gate c…
Approaches to Simultaneously Solving Variational Quantum Eigensolver Problems
Adam Hutchings, Eric Yarnot, Xinpeng Li +4
The variational quantum eigensolver (VQE), a type of variational quantum algorithm, is a hybrid quantum-classical algorithm to find the lowest-energy eigenstate of a particular Ham…
Efficient Circuit Wire Cutting Based on Commuting Groups
Xinpeng Li, Vinooth Kulkarni, Daniel T. Chen +5
Current quantum devices face challenges when dealing with large circuits due to error rates as circuit size and the number of qubits increase. The circuit wire-cutting technique ad…
Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang +8
Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns…
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches
Jiayi Yuan, Hongyi Liu, Shaochen Zhong +9
Long context capability is a crucial competency for large language models (LLMs) as it mitigates the human struggle to digest long-form texts. This capability enables complex task-…
Learning the Market: Sentiment-Based Ensemble Trading Agents
Andrew Ye, James Xu, Vidyut Veedgav +6
We propose and study the integration of sentiment analysis and deep reinforcement learning ensemble algorithms for stock trading by evaluating strategies capable of dynamically alt…
Online Detection of Golden Circuit Cutting Points
Daniel T. Chen, Ethan H. Hansen, Xinpeng Li +7
Quantum circuit cutting has emerged as a promising method for simulating large quantum circuits using a collection of small quantum machines. Running low-qubit "circuit fragments"…
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
Zirui Liu, Guanchu Wang, Shaochen Zhong +8
With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually fo…
Efficient Quantum Circuit Cutting by Neglecting Basis Elements
Daniel T. Chen, Ethan H. Hansen, Xinpeng Li +7
Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classic…
Approximate Quantum Circuit Cutting
Daniel Chen, Betis Baheri, Vipin Chaudhary +3
Current and imminent quantum hardware lacks reliability and applicability due to noise and limited qubit counts. Quantum circuit cutting -- a technique dividing large quantum circu…