Vinooth Kulkarni
PhD Student
QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting
2026Presents QuMod, a parallel quantum job scheduling framework for modular QPUs leveraging circuit cutting to improve throughput on heterogeneous quantum hardware.
Efficient Transpilation of OpenQASM 3.0 Dynamic Circuits to CUDAQ: Performance and Expressiveness Advantages
2026Presents an efficient transpilation approach for converting OpenQASM 3.0 dynamic circuits to CUDAQ, demonstrating performance and expressiveness advantages.
QuFlex: Parallel Quantum Job Scheduling Using Adaptive Circuit-Cutting
2025Parallel quantum job scheduling across multiple QPUs is critical for maximizing throughput in heterogeneous quantum computing environments. QuFlex introduces an adaptive circuit-cutting approach that dynamically partitions quantum circuits based on available QPU resources, enabling efficient parallel scheduling across heterogeneous quantum hardware. The framework demonstrates improved QPU utilization and reduced job completion times compared to static partitioning approaches.
Efficient Circuit Wire Cutting Based on Commuting Groups
2024Current quantum devices face challenges with large circuits due to increasing error rates as circuit size and qubit count grow. Inspired by ancilla-assisted quantum process tomography and MUBs-based grouping for simultaneous measurement, this paper proposes a new circuit wire cutting approach that uses ancillary qubits to transform quantum input initializations into quantum output measurements, allowing multiple measurements to be grouped and executed simultaneously. The technique significantly reduces subcircuit execution overhead and classical reconstruction complexity compared to standard wire cutting.
Online Detection of Golden Circuit Cutting Points
2023Quantum circuit cutting enables large circuits to run on small quantum devices, but reconstructing measurement statistics requires computational resources that grow exponentially with the number of cuts. This paper introduces the concept of a golden cutting point—circuit structures that induce negligible basis components during reconstruction, allowing those downstream computations to be avoided entirely. A hypothesis-testing scheme is proposed for online detection of golden cutting points, with robustness results for low-probability test failures, and demonstrated applicability on Qiskit's Aer simulator achieving reduced wall time from identifying and avoiding obsolete measurements.
Practical Implications of Dequantization on Machine Learning Algorithms
2022Quantum computing algorithms offer theoretical speedups for certain machine learning tasks, but dequantization results show that classical algorithms can sometimes achieve comparable performance. This paper examines the practical implications of dequantization on machine learning algorithms, providing a systematic analysis of when quantum approaches offer genuine advantages versus when classical alternatives are sufficient. The work offers guidance for practitioners on determining which ML tasks are promising candidates for quantum speedup versus those where dequantization renders quantum approaches redundant.
