1 citations · 1 across the 7 of their papers we have counts for
10 papers
Quantifying Teleportation Overhead in Distributed Unitary Coupled-Cluster Ansätze
Grier M. Jones, Hassan Tariq Shafi, Zixuan Wang +3
Distributed quantum computing (DQC) has been proposed as a way to scale quantum algorithms for practical applications beyond monolithic quantum processor architectures. Among these…
REMOP: REmote-Memory-aware OPerator Optimization
Shiquan Zhang, Yunhao Mao, Yuqiu Zhang +3
Remote and disaggregated memory tiers expand the effective memory capacity of analytical database engines, but they also reshape the cost structure of out-of-memory query processin…
Towards an Optimally Distributed Quantum Fourier Transform Circuit
Zachary Vernec, Michael Silver, Hans-Arno Jacobsen
A promising avenue for scaling quantum computing is to connect quantum processing units (QPUs) by generating entanglement between them. This requires circuit partitioning: partiall…
SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System
Seyed Salar Ghazi, Kaiwen Zhang, Mehdi feizi +1
Hierarchical Federated Learning (HFL) enables scalable collaborative model training across distributed devices while preserving data privacy. However, existing HFL client selection…
Q-DICE: Quantum Distributed Interconnect Compiler and Emulator
Michael Silver, Zachary Vernec, Hans-Arno Jacobsen
As distributed quantum computing (DQC) offers a leading path towards scalable quantum computation, the ability to benchmark distributed algorithms under realistic conditions become…
Quantum Hypergraph Partitioning
Yiran Li, Y. Batuhan Yilmaz, Michael Silver +2
Hypergraph partitioning is a fundamental optimization problem with applications in data management and other domains involving higher-order relations. In this paper, we study balan…
MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System
Felix Mulitze, Herbert Woisetschläger, Hans Arno Jacobsen
The convergence of next-generation wireless systems and distributed Machine Learning (ML) demands Federated Learning (FL) methods that remain efficient and robust with wireless con…
Adversarial Robustness in Distributed Quantum Machine Learning
Pouya Kananian, Hans-Arno Jacobsen
Studying adversarial robustness of quantum machine learning (QML) models is essential in order to understand their potential advantages over classical models and build trustworthy…
A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning
Shivesh Prakash, Nandan Patel, Hans-Arno Jacobsen +1
We introduce MHNpath, a machine learning-driven retrosynthetic tool designed for computer-aided synthesis planning. Leveraging modern Hopfield networks and novel comparative metric…
Adversarial Robustness of Partitioned Quantum Classifiers
Pouya Kananian, Hans-Arno Jacobsen
Adversarial robustness in quantum classifiers is a critical area of study, providing insights into their performance compared to classical models and uncovering potential advantage…