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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…
SynDebug: A Framework to Debug Logic Bugs in Fault-Tolerant Quantum Computing Programs
Nicholas S. DiBrita, Jack Lange, Tirthak Patel
Fault-tolerant quantum computing (FTQC) introduces substantial systems-level complexity through quantum error correction (QEC), making debugging FTQC programs challenging. Existing…
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…
Identity-Paired Progressive Depth Training: When Trainability Persists Beyond Expressibility
Athanasios Hadjidimoulas, Tirthak Patel, Anastasios Kyrillidis
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, yet their training suffers from sensitivity to circuit depth, initialization, and land…
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,…
Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest
Yuqian Huo, David Quiroga, Anastasios Kyrillidis +1
Variational quantum algorithms (VQAs) have the potential to demonstrate quantum utility on near-term quantum computers. However, these algorithms often get executed on the highest-…
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…
HattriQ: Designing Integrated Gradients for Feature Attribution in Quantum Machine Learning
Nicholas S. DiBrita, Jason Han, Younghyun Cho +2
Quantum machine learning (QML) algorithms have demonstrated early promise across hardware platforms, but remain difficult to interpret due to the inherent opacity of quantum state…
Domain-Aware Probability Sampling for Hybrid Quantum Systems using Bayesian Optimization
Nicholas S. DiBrita, Jason Han, Krishna Bhatia +3
We study the problem of probability distribution matching and sampling on near-term quantum computers, aiming to construct parameterized circuits that generate samples from a targe…
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…
Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?
Yuqian Huo, Jinbiao Wei, Christopher Kverne +3
Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The c…
ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers
Nicholas S. DiBrita, Jason Han, Tirthak Patel
Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not ye…
EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
Jason Han, Nicholas S. DiBrita, Younghyun Cho +2
Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variab…
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…