activity
20192025
most citedUnleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design

12 citations · 43 across the 12 of their papers we have counts for

collaborators

12 papers

quant-ph2025

Hadamard Random Forest: Reconstructing real-valued quantum states with exponential reduction in measurement settings

Zhixin Song, Hang Ren, Melody Lee +3

Quantum tomography is a crucial tool for characterizing quantum states and devices and estimating nonlinear properties of the systems. Performing full quantum state tomography on a…

quant-ph2024

EPOC: A Novel Pulse Generation Framework Incorporating Advanced Synthesis Techniques for Quantum Circuits

Jinglei Cheng, Yuchen Zhu, Yidong Zhou +3

In this paper we propose EPOC, an efficient pulse generation framework for quantum circuits that combines ZX-Calculus, circuit partitioning, and circuit synthesis to accelerate pul…

quant-ph2024★ 3 cited

Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm

Zhiding Liang, Gang Liu, Zheyuan Liu +8

In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that hav…

quant-ph2023★ 2 cited

SpacePulse: Combining Parameterized Pulses and Contextual Subspace for More Practical VQE

Zhiding Liang, Zhixin Song, Jinglei Cheng +5

In this paper, we explore the integration of parameterized quantum pulses with the contextual subspace method. The advent of parameterized quantum pulses marks a transition from tr…

quant-ph2023

Error Mitigated Metasurface-Based Randomized Measurement Schemes

Hang Ren, Yipei Zhang, Ze Zheng +4

Estimating properties of quantum states via randomized measurements has become a significant part of quantum information science. In this paper, we design an innovative approach le…

quant-ph2023★ 12 cited

Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design

Zhiding Liang, Jinglei Cheng, Rui Yang +6

Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This pape…

quant-ph2023

Fidelity estimator, randomized benchmarking and ZNE for quantum pulses

Jinglei Cheng, Zhiding Liang, Rui Yang +4

Most previous research focused on designing pulse programs without considering the performance of individual elements or the final fidelity. To evaluate the performance of quantum…

quant-ph2023★ 1 cited

Towards Advantages of Parameterized Quantum Pulses

Zhiding Liang, Jinglei Cheng, Zhixin Song +7

The advantages of quantum pulses over quantum gates have attracted increasing attention from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalabil…

quant-ph2022★ 7 cited

NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms

Zhiding Liang, Jinglei Cheng, Hang Ren +8

Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are it…

quant-ph2022★ 11 cited

Sample-efficient verification of continuously-parameterized quantum gates for small quantum processors

Ryan Shaffer, Hang Ren, Emiliia Dyrenkova +7

Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum cir…

quant-ph2022★ 1 cited

Variational Quantum Pulse Learning

Zhiding Liang, Hanrui Wang, Jinglei Cheng +9

Quantum computing is among the most promising emerging techniques to solve problems that are computationally intractable on classical hardware. A large body of existing works focus…

quant-ph2019★ 6 cited

Modelling Quantum Devices and the Reconstruction of Physics in Practical Systems

Hang Ren, Ying Li

Modelling quantum devices is to find a model according to quantum theory that can explain the result of experiments in a quantum device. We find that usually we cannot correctly id…