17 citations · 76 across the 21 of their papers we have counts for
21 papers
CaliScalpel: In-Situ and Fine-Grained Qubit Calibration Integrated with Surface Code Quantum Error Correction
Xiang Fang, Keyi Yin, Yuchen Zhu +8
Quantum Error Correction (QEC) is a cornerstone of fault-tolerant, large-scale quantum computing. However, qubit error drift significantly degrades QEC performance over time, neces…
Leveraging Hardware Power through Optimal Pulse Profiling for Each Qubit Pair
Yuchen Zhu, Jinglei Cheng, Boxi Li +3
In the scaling development of quantum computers, the calibration process emerges as a critical challenge. Existing calibration methods, utilizing the same pulse waveform for two-qu…
ECDQC: Efficient Compilation for Distributed Quantum Computing with Linear Layout
Kecheng Liu, Yidong Zhou, Haochen Luo +6
In this paper, we propose an efficient compilation method for distributed quantum computing (DQC) using the Linear Nearest Neighbor (LNN) architecture. By exploiting the LNN topolo…
Coqa: Blazing Fast Compiler Optimizations for QAOA
Yuchen Zhu, Yidong Zhou, Jinglei Cheng +4
The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage over classical computers. However, existing compilers…
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…
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…
VIOLET: Visual Analytics for Explainable Quantum Neural Networks
Shaolun Ruan, Zhiding Liang, Qiang Guan +4
With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum…
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…
RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training
Hanrui Wang, Yilian Liu, Pengyu Liu +10
Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Arbitrary state preparation algorithms can…
QuCS: A Lecture Series on Quantum Computer Software and System
Zhiding Liang, Hanrui Wang
In this era of incessant advancements in quantum computing, bridging the gap between quantum algorithms' hardware requisites and available devices has become crucial. A prime focus…
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…
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…
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…
Hybrid Gate-Pulse Model for Variational Quantum Algorithms
Zhiding Liang, Zhixin Song, Jinglei Cheng +8
Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces s…
TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits
Jinglei Cheng, Hanrui Wang, Zhiding Liang +3
Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of p…
QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
Hanrui Wang, Pengyu Liu, Jinglei Cheng +10
Among different quantum algorithms, PQC for QML show promises on near-term devices. To facilitate the QML and PQC research, a recent python library called TorchQuantum has been rel…
Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
Ruiyang Qin, Zhiding Liang, Jinglei Cheng +2
Due to the immense potential of quantum computers and the significant computing overhead required in machine learning applications, the variational quantum classifier (VQC) has rec…
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…
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…
Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow
Zhiding Liang, Zhepeng Wang, Junhuan Yang +4
In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error corr…
Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs
Zhepeng Wang, Zhiding Liang, Shanglin Zhou +3
With the constant increase of the number of quantum bits (qubits) in the actual quantum computers, implementing and accelerating the prevalent deep learning on quantum computers ar…