News

Student-led team won the VentureWell E-Team Pioneer award (link). We are exploring Sustainable and Energy-efficient Quantum Computing with Circuit Cutting techniques;

Two papers accepted to IEEE Quantum Week 2024 (QCE’24), (1) Scalable quantum circuit cutting (up to 2000x cost reduction when compared with IBM Qiskit Circuit Knitting Toolbox, Arxiv link); (2) Benchmarking Optimizers for Qumode State Preparation (Arxiv link);

Dr. Mao has been selected as a 2024 Google Cloud Research Innovator, the fourth cohort.

Our project on Quantum Data Science and Resilient Quantum Learning System was funded by National Science Foundation (NSF Link).

Our paper A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity was accepted by IEEE Transactions on Quantum Engineering (TQE).

Our paper Privacy and Integrity Protection for IoT Multimodal Data using Machine Learning and Blockchain was accepted by ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM).

Two papers on Quantum Distributed Learning and Dependency-Aware Resource Management were accepted by IEEE Big Data 2023.

After five years at Fordham University, Dr. Mao has been promoted to the tenured Associate Professor and Associate Chair for Undergraduate Studies at the Computer and Information Science Department.

Our Experiential Learning Platform and Curricular Modules for Quantum Computing Security and Privacy Education project was funded by National Science Foundation. The NSF grant will support us explore the security and privacy research and education of quantum computing in the system level. NSF Link.

Our Collaborative Optimization and Management for Quantum Computing project was funded by National Science Foundation. The NSF grant will support us develop a quantum-classical system that can collaboratively optimize iterative and parallel quantum applications. NSF Link.

Our paper Cost-aware scheduling systems for real-time workflows in cloud: An approach based on Genetic Algorithm and Deep Reinforcement Learning was accepted by the ELSEVIER Expert Systems with Applications.

Our Quantum-Classical Edge Computing project was funded by National Science Foundation. The NSF grant will support us develop a quantum-equipped Cloud-Edge collaborative computing framework for resource-constrained devices in cyber-physical systems. NSF Link.

Our paper VENUS: A Geometrical Representation for Quantum State Visualization was accepted by the EuroVis 2023.

Our paper Towards Network-aware Query Execution Systems in Large Datacenters was accepted by the IEEE Transactions on Network and Service Management.

Our paper QuCNN: A Quantum Convolutional Neural Network with Entanglement Based Backpropagation was accepted by 2022 IEEE/ACM 7th Symposium on Edge Computing.

Our paper Iterative Qubits Management for Quantum Index Searching in a Hybrid System was accepted by 41st IEEE International Performance Computing and Communications Conference (IPCCC’22).

Our paper VACSEN: A Visualization Approach for Noise Awareness in Quantum Computing was accepted by IEEE Visualization (VIS’22) and will be published on IEEE Transactions on Visualization and Computer Graphics.

Our paper Pinpointing the System Reliability Degradation in NISQ Machines was accepted by 2022 IEEE International Conference on Quantum Computing and Engineering (QCE’22).

Our paper Differentiate Quality of Experience Scheduling for Deep Learning Inferences with Docker Containers in the Cloud was accepted by IEEE Transactions on Cloud Computing.

Our paper QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity was accepted by Fifth Conference on Machine Learning and Systems (MLSys’2022).

Our paper Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster was accepted by IEEE Systems Journal.

Our paper Deep Reinforcement Learning for Load-Balancing Aware Network Control in IoT Edge Systems was accepted by IEEE Transactions on Parallel and Distributed Systems.

Our quantum learning framework paper A hybrid system for learning classical data in quantum states was accepted by the 40th IEEE International Performance Computing and Communications Conference (IPCCC’21).

Our quantum deep learning paper QuGAN: A Quantum State Fidelity based Generative Adversarial Network was accepted by IEEE International Conference on Quantum Computing and Engineering (QCE’21).

The paper An Improved Wolf Pack Algorithm for Optimization Problems: Design and Evaluation was accepted by PLOS ONE.

The paper Optimizing Internal Overlaps by Self-Adjusting Resource Allocation in Multi-Stage Computing Systems was accepted by The IEEE ACCESS.

The paper TQEA: Temporal Quantum Error Analysis was accepted by The 17th IEEE Workshop on Silicon Errors in Logic System Effects (SELSE 2021).

Tri-State ExploreCSR website was online, link

The paper Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers was accepted by IEEE Transactions on Parallel and Distributed Systems.

Tri-State ExploreCSR project was funded by Google Research.

The paper Quantum-Inspired Classical Algorithm for Principal Component Regression was accepted by The 2020 International Conference on Quantum Techniques in Machine Learning.

Quantum machine learning project was funded by Google Cloud Platform.