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Ashwin Kumar Karnad

Publications and source records attributed to Ashwin Kumar Karnad.

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Quantum resources in resource management systems

Quantum computing resources are increasingly being incorporated into high-performance computing (HPC) environments as co-processors for hybrid workloads. To support this paradigm, quantum devices must be treated as schedulable first-class resources within existing HPC infrastructure. This enables consistent workload management, unified resource visibility, and support for hybrid quantum-classical job execution models. This paper presents a reference architecture and implementation for the integration of quantum computing resources, both on-premises and cloud-hosted into HPC centers via standard workload managers. We introduce a Slurm plugin designed to abstract and control quantum backends, enabling seamless resource scheduling, minimizing queue duplication, and supporting job co-scheduling with classical compute nodes. The architecture supports heterogeneous quantum resources and can be extended to any workload (and container) management systems.

quant-ph

Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits

We develop a parametric error model to construct a digital twin of a superconducting transmon qubit device. The model parameters are extracted from hardware calibration data and supplementary benchmarking circuits, providing a dynamic, system-specific representation of noise and gate imperfections. Given the strong dependence of qubit performance on calibration procedures, our approach captures real-time device fluctuations. By incorporating predominant noise sources derived from underlying physical processes, we enhance the emulation's accuracy while reducing the data required for model fitting. Finally, we validate our model by comparing its predictions with experimental results from a 5-qubit QPU, achieving a mean total variation distance of 0.15 between the shot distributions. This digital twin can be leveraged for predictive performance analysis, error mitigation strategies, and the optimization of quantum protocols, contributing to more reliable quantum computations.

quant-ph