Welcome and Introduction: Zhiding Liang (RPI)  8:00 am - 8:15 am 

Keynote Speaker: Dr. Marco Pistoia (JPMorgan Chase) 8:15 am - 9:00 am

Invited Speaker: Dr. Ji Liu (Argonne)  9:00 am - 9: 30 am

Title: Optimizing QAOA Circuits: Insights from Mixer Design and Optimal Solvers

Abstract: The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems. Recent studies have demonstrated scaling advantages in various settings. In QAOA, the cost layer encodes the problem while the mixer layer explores the solution space. It is essential to develop quantum circuits that implement QAOA effectively and adhere to hardware connectivity constraints. In this talk, I will present two separate research directions aimed at optimizing QAOA circuits. The first part discusses how modifications to the mixer design can improve circuit performance and reduce circuit size. The second part focuses on applying SAT solver techniques to synthesize optimal QAOA circuits and proposes a scalable design that leverages optimal synthesis. Together, these approaches yield circuits that are both efficient and capable of achieving high-quality solutions.

Invited Speaker: Dr. Wibe de Jong (LBNL)  9:30 am - 10: 00 am

Title: Driving Scientific Discovery with Hybrid Quantum Computing Technologies

Abstract: With quantum technologies rapidly advancing, the computing and science community have started to explore the role of high-performance computing (HPC) and networking in conjunction with quantum computing. In this talk I will explore potential pathways to integrate HPC and networking, and I will also discuss areas where this integration is not so obvious. 

Coffee Break  10:00 am - 10: 30 am

Invited Speaker: Dr. Teague Tomesh (Infleqtion)  10:30 am - 11: 00 am

Title: On the potential for quantum advantage in feature selection applications for multimodal cancer data

Abstract: Quantum computing presents a novel computational paradigm with immense potential, yet its practical applications remain limited due to current hardware constraints. In order to leverage quantum advantage in the near future, a codesign approach is crucial for optimizing the use of scarce quantum resources. In this paper, we investigate the application of hybrid quantum-classical algorithms to the identification of cancer biomarkers within multimodal patient data sets. Due to the computational difficulty of searching through combinatorially growing search spaces by classical means, current models for interactions between biomarkers simplistically ignore third- and higher-order interactions. However, the models we explore make no assumption as to the highest degree of interaction between biomarkers. To address the shortcoming in conventional models for cancer biomarkers, while accounting for the limitations of current quantum hardware, our approach encompasses (1) the classical preprocessing of data into a suitable form for efficient downstream processing, (2) recursive quantum optimization algorithms to heuristically solve constrained combinatorial optimization problems that better reflect the underlying biology, and (3) classical divide-and-conquer techniques that enable the quantum solver to tackle feature selection problems of arbitrary size. We demonstrate that our hybrid approach identifies smaller and more accurate feature sets compared to classical baseline comparisons evaluated on a benchmark data set and classification task. Furthermore, we argue for future explorations of quantum advantage and quantum-inspired algorithms within this application domain, paving the way for more effective and interpretable cancer biomarker discovery.

Accepted Full Paper: Dementor: Removing Dead Gates in Quantum Programs  11:00 am - 11:20 am

Author: Enhyeok Jang, Youngmin Kim, Hyungseok Kim (Yonsei University), Dongho Ha (MangoBoost Inc.), Yongju Lee, Jaewon Kwon, Jun Woo You, Jiho Park and Won Woo Ro (Yonsei University)

Abstract: The computational complexity of quantum programs is influenced by the limitations of the native gate set and the constraints imposed by qubit topology. These factors necessitate advanced compilation techniques for efficient execution. Our experimental data reveal that approximately 23.1\% of gates in quantum programs are \textit{dead gates}, which do not contribute to any meaningful alteration in the quantum state. Removing these dead gates would provide the potential opportunity to reduce the size and improve the accuracy of the quantum program. However, we observe that existing methods, including those integrated into Qiskit Transpiler, cannot adequately remove these unnecessary gates. In this work, we introduce Dementor (\underline{De}ad Quantu\underline{m} Gat\underline{e} Elimi\underline{n}a\underline{tor}), which efficiently detects and removes dead gates by considering a range of redundancy patterns. To evaluate the efficacy of Dementor, we conducted experiments on IBM quantum processors, which have two distinct native gate sets: Echoed Cross-Resonance (ECR)-based and Controlled-X (CX)-based. Our experiments show that Dementor achieves a reduction in the number of decomposed gates by an average of 46.4\% on ECR-based systems and by an average of 60.6\% on CX-based systems compared to Qiskit Transpiler with optimization level 3.

Accepted Full Paper: Peer-to-Peer Distribution of Graph States Across Spacetime Quantum Networks of Arbitrary Topology  11:20 am - 11:40 am

Author: Yuexun Huang (UChicago), Xiangyu Ren (Edinburgh), Bikun Li, Yat Wong (UChicago), Zhiding Liang (RPI) and Liang Jiang (UChicago)

Abstract: Graph states are a class of important multiparty entangled quantum states, of which Bell pairs are the special case. Realizing a robust and fast distribution of arbitrary graph states in the downstream layer of the quantum network is essential for enabling large-scale quantum networks. To address this, we propose a novel quantum network protocol, called P2PGSD, inspired by the classical Peer-to-Peer network. This protocol efficiently implements general graph state distribution in the network layer, demonstrating significant advantages in resource efficiency and scalability, particularly for sparse graph states. An explicit mathematical model for the general graph state distribution problem has also been constructed, above which the intractability for a wide class of resource minimization problems is proved and the optimality of the existing algorithms is discussed. Moreover, we leverage the space-time quantum network for memory management in network challenges, drawing inspiration from special relativity. We suggest a universal quantum distributed computation framework to exploit the strengths of our protocols, as confirmed by numerical simulations that reveal up to a 50% enhancement for general sparse graph states. This work marks a significant step toward resource-efficient multiparty entanglement distribution for diverse network topologies.

Accepted Short Paper: Enhancing Distribution Network Observability with Optimized Channel-Limited PMU placement: A Quantum Optimization Solution  11:40 am - 12:00 pm

Author: Yuqi Jiang and Yan Li (PSU)

Abstract: Optimizing the installation of Phasor Measurement Unit (PMU) is crucial to maximize the observability of the distribution network in the power system, as the significant amount of the integration of renewable energy resources. Due to the high cost of installing the PMU as well as its measurement channel, it is required to maximize the observability of the system via an optimized PMU installation. To solve this NP-hard binary optimization problem, QAOA is introduced and a tailored objective function is developed for the Maximize PMU Observability (MPO) problem. The developed quantum optimization method is also tested in the situation where PMUs have channel limitations. The case study on the IEEE-24 bus system of both the normal and the channel limitation scenarios demonstrates that even with the current Noisy Intermediate Scale Quantum simulators, QAOA can surpass traditional heuristic algorithms for small problem sizes. As quantum hardware continues to improve, QAOA’s potential becomes more significant for larger problem sizes.

Lunch  12:00 pm - 1:00 pm

Welcome back: Dr. Zichang He (JPMorganChase) 1:00 pm - 1 :10 pm

Invited Speaker: Dr. Matthew DeCross (Quantinuum)  1:10 pm - 1: 40 pm

Title: Classically simulating highly-connected random quantum circuits

Abstract: Quantum computers have recently developed to the extent that they can perform some contrived tasks with computational speedups relative to classical HPC. I will describe one such demonstration, sampling the output distribution of arbitrarily connected quantum circuits, as implemented on Quantinuum’s H2 quantum computer with 56 qubits. In particular, I will focus on the scaling advantages offered by the QCCD architecture in performing computationally difficult tasks and explicitly detail the implementation of best-known methods for random circuit sampling on world-leading HPC platforms. Building on this, I will discuss next steps for HPC integration with quantum computing on Quantinuum’s H-Series devices in pursuit of realistic applications that require both to accomplish.

Invited Speaker: Dr. Reza Nejabati (Cisco)  1:40 pm - 2: 10 pm

Title:  Building a Scalable Fabric for Quantum Data Centers

Invited Speaker: Dr. David Williams-Young (Microsoft)  2:10 pm - 2: 40 pm

Title:  Quantum, AI, and the path to Commercial Advantage 

The promise of quantum is real, and we are beginning to see where this technology can have the greatest impact. Decades of quantum research and development point to one primary class of practical application for quantum computing next to cryptanalysis: the simulation of quantum systems, especially for chemistry and materials science. As we continue to improve the fidelity and scale of quantum machines, we will first be able to unlock scientific quantum advantage, solving a growing class of scientifically interesting and classically intractable problems. As we scale to quantum supercomputers, we will be able to achieve commercial quantum advantage and solve the world’s most pressing challenges through quantum-enabled advances in chemistry, biochemistry, and materials science. However, scaled quantum systems won’t exist in isolation but will operate alongside AI and classical supercomputing. At Microsoft, we are engineering these hybrid classical-quantum supercomputing systems with Azure Quantum Elements with the goal of accelerating scientific discovery. Join us to learn more about the promising applications for quantum computing, our latest achievements and our path to scale.  

Accepted Short Paper: QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation  2:40 pm - 3:00 pm

Author: Kangyu Zheng, Tianfan Fu and Zhiding Liang (RPI)

Abstract: The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, and excretion) properties, which are essential in drug evaluation. However, ADME tasks pose unique challenges for existing quantum computing systems (QCS) frameworks, as they involve both classification with unbalanced dataset and regression problems. These dual requirements make it necessary to adapt and refine current QCS frameworks to effectively address the complexities of ADME predictions. We propose a novel training-free scoring mechanism to evaluate QML circuit performance on imbalanced classification and regression tasks. Our mechanism demonstrates significant correlation between scoring metrics and test performance on imbalanced classification tasks. Additionally, we develop methods to quantify continuous similarity relationships between quantum states, enabling performance prediction for regression tasks. This represents the first comprehensive approach to searching and evaluating QCS circuits specifically for regression applications. Validation on representative ADME tasks—one imbalanced classification and one regression—demonstrates moderate positive correlation between our scoring metrics and circuit performance, significantly outperforming baseline scoring methods that show negligible correlation.

Coffee Break 3:00 pm - 3:30 pm

Accepted Full Paper: DyQNet: Optimizing Dynamic Entanglement Routing with Online Request in Quantum Network   3:50 pm - 4:10 pm

Author: Tianyao Chu, Liqiang Lu, Shiyu Li, Xinghui Jia (Zhejiang U), Chenren Xu (Peking U) and Jianwei Yin (Zhejiang U)

Abstract: The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, and excretion) properties, which are essential in drug evaluation. However, ADME tasks pose unique challenges for existing quantum computing systems (QCS) frameworks, as they involve both classification with unbalanced dataset and regression problems. These dual requirements make it necessary to adapt and refine current QCS frameworks to effectively address the complexities of ADME predictions. We propose a novel training-free scoring mechanism to evaluate QML circuit performance on imbalanced classification and regression tasks. Our mechanism demonstrates significant correlation between scoring metrics and test performance on imbalanced classification tasks. Additionally, we develop methods to quantify continuous similarity relationships between quantum states, enabling performance prediction for regression tasks. This represents the first comprehensive approach to searching and evaluating QCS circuits specifically for regression applications. Validation on representative ADME tasks—one imbalanced classification and one regression—demonstrates moderate positive correlation between our scoring metrics and circuit performance, significantly outperforming baseline scoring methods that show negligible correlation.

Accepted Full Paper: Quantum-classical hybrid approach to temporal-probabilistic semi-structured search problems   4:10 pm - 4:30 pm

Author: Zhemin Zhang, Michael Roberts and Zhiding Liang (RPI)

Abstract: We present a novel hybrid quantum-classical approach to solving semi-structured search problems with evolving constraints and partial feedback, using three case studies of problems with increasing complexity - Wordle, warehouse logistics, and robotic maze navigation. While classical methods effectively process feedback and quantum algorithms excel at unstructured search, neither optimally handle problems where the search space evolves through iterative temporal feedback. For Wordle, our hybrid approach combines classical dictionary pruning with Grover's algorithm-based quantum position optimization. Through experimental validation, we show competitive performance against classical methods at various scales with significantly reduced quantum resource requirements. Finally, by formulating and analyzing the latter two case studies, our framework demonstrates broader applications in real-world problems such as logistics, robotics, and other systems where search spaces evolve through progressive feedback.

Accepted Full Paper: Toward a Novel Application-Specific Job Mapping and Scheduling on Quantum-Centric Computing 4:30 pm - 4:50 pm

Author: Jinyang Li, Yuhong Song, Yipei Liu, Jianli Pan, Lei Yang (GMU), Travis Humble (ORNL) and Weiwen Jiang (GMU)

Abstract: With the progression into the quantum utility era, computing is shifting toward quantum-centric architectures, where multiple quantum processors collaborate with classical computing resources. Platforms such as IBM Quantum and Amazon Braket exemplify this trend, enabling access to diverse quantum backends. However, efficient resource management remains a challenge, as quantum processors are highly susceptible to noise, which significantly impacts computation fidelity. Additionally, the heterogeneous noise characteristics across different processors add further complexity to scheduling and resource allocation. Existing scheduling strategies typically focus on mapping and scheduling jobs to these heterogeneous backends, which leads to some jobs suffering extremely low fidelity. Targeting quantum optimization jobs (e.g., VQC, VQE, QAOA) — among the most promising quantum applications in the NISQ era — we hypothesize that executing the later stages of a job on a high-fidelity quantum processor can significantly improve overall fidelity. To verify this, we use VQE as a case study and develop a Genetic Algorithm-based scheduling framework that incorporates job splitting to optimize fidelity and throughput. Experimental results demonstrate that our approach consistently maintains high fidelity across all jobs while significantly enhancing system throughput. Furthermore, the proposed algorithm exhibits excellent scalability in handling an increasing number of quantum processors and larger workloads, making it a robust and practical solution for emerging quantum computing platforms. To further substantiate its effectiveness, we conduct experiments on a real quantum processor, IBM Strasbourg, which confirm that job splitting improves fidelity and reduces the number of iterations required for convergence.

Close: Dr. Yufei Ding (UCSD) 4:50 pm - 5:00 pm