1 citations · 1 across the 12 of their papers we have counts for
13 papers
EntangledEdge: A Distributed System for Orbital Quantum Sensing and Learning
Mohammad Abrarul Hasanat, Amit Samanta, Jason Ludmir +2
An orbital quantum sensor is more precise than classical and holds its information in a quantum state's superposition and entanglement, which classical measurement collapses. Learn…
BAHAMAS: A Control Plane for Optimization and Execution of Variational Quantum Circuits
Amit Samanta, Mohammad Abrarul Hasanat, Jason Ludmir +3
Variational quantum algorithms (VQAs) suffer from unstable optimization due to temporal noise drift and static qubit mappings that distort gradient signals across iterations. We pr…
Hardware-Aware Compilation and Execution of Bivariate Bicycle Codes on Neutral-Atom Systems
Jason Ludmir, Aditya Ranjan, Nicholas S. DiBrita +2
Quantum computers are noisy; without quantum error correction (QEC), deep programs fail as qubits lose information due to decoherence. Among QEC approaches, bivariate bicycle (BB)…
PaQit: Energy-Runtime-Fidelity Co-Optimization for Neutral Atom Quantum Computers
Jason Ludmir, Tirthak Patel
Neutral-atom quantum computers provide a scalable platform for large-scale quantum computation due to their all-optical control, room-temperature operation, and flexible lattice ge…
TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency
Mohammad Abrarul Hasanat, Jason Ludmir, Tirthak Patel +1
Quantum processors are being integrated into HPC ecosystems as co-processors, where compilation of quantum circuits into hardware-executable form determines both output fidelity an…
SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility
Jason Ludmir, Nicholas S. DiBrita, Jason Han +1
Emerging quantum sensors are increasingly envisioned as components of hybrid quantum-classical high-performance computing, enabling new capabilities in scientific, cyber-physical,…
Anchor: Reducing Temporal and Spatial Output Performance Variability on Quantum Computers
Yuqian Huo, Daniel Leeds, Jason Ludmir +2
Quantum computing, which has the power to accelerate many computing applications, is currently a technology under development. As a result, the existing noisy intermediate-scale qu…
Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus
Jason Han, Nicholas S. DiBrita, Daniel Leeds +3
Quantum machine learning (QML) holds the promise to solve classically intractable problems, but, as critical data can be fragmented across private clients, there is a need for dist…
QuFoundry: Generating Data with Quantum Properties for Quantum Machine Learning Utility
Jason Ludmir, Ian Martin, Nicholas S. DiBrita +1
Quantum machine learning (QML) promises significant speedups, particularly when operating on quantum datasets. However, its progress is hindered by the scarcity of suitable trainin…
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
Jason Zev Ludmir, Sophia Rebello, Jacob Ruiz +1
Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum computing has recently eme…
Modeling and Simulating Rydberg Atom Quantum Computers for Hardware-Software Co-design with PachinQo
Jason Zev Ludmir, Yuqian Huo, Nicholas S. DiBrita +1
Quantum computing has the potential to accelerate various domains: scientific computation, machine learning, and optimization. Recently, Rydberg atom quantum computing has emerged…
Parallax: A Compiler for Neutral Atom Quantum Computers under Hardware Constraints
Jason Ludmir, Tirthak Patel
Among different quantum computing technologies, neutral atom quantum computers have several advantageous features, such as multi-qubit gates, application-specific topologies, movab…
ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks
Nicholas S. DiBrita, Daniel Leeds, Yuqian Huo +2
Quantum computing has shown theoretical promise of speedup in several machine learning tasks, including generative tasks using generative adversarial networks (GANs). While quantum…