Dhirpal Shah

Dhirpal Shah

he/him

Quantum Computing PhD Student

Publications

You can also find my articles on my Google Scholar profile.

Publications and Preprints

Toward Quantum-Enabled Biomarker Discovery: An Outlook from Q4Bio

Published in arXiv (quant-ph), 2025

Outlook on hybrid quantum–classical pipelines for biomarker discovery; defines empirical quantum advantage and sketches a co-designed path to near-term feasibility.

Recommended citation: Shah, D., Teo, M., Robinett, R. A., Madejski, S., Morrell, Z., Ramesh, S., Campbell, C., Thotakura, B., Omole, V., Hall, B., Harrow, A. W., Tomesh, T., Pearson, A. T., Chong, F. T., & Riesenfeld, S. J. (2025). "Toward Quantum-Enabled Biomarker Discovery: An Outlook from Q4Bio." arXiv, arXiv:2509.25904.
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Noisy Quantum Simulation Using Tracking, Uncomputation and Sampling

Published in arXiv (quant-ph), 2025

Introduces TUSQ, a simulator that reduces overhead for noisy circuit emulation via tracking, uncomputation, and tree-based sampling.

Recommended citation: Dangwal, S., Oberoi, T., Sailopal, A., Shah, D., & Chong, F. T. (2025). "Noisy Quantum Simulation Using Tracking, Uncomputation and Sampling." arXiv, arXiv:2508.04880.
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Circuit decompositions and scheduling for neutral atom devices with limited local addressability

Published in IEEE International Conference on Quantum Computing and Engineering (QCE 2024), 2024

Compiler pipeline and scheduling that minimize global-rotation costs for neutral atom hardware, improving circuit duration and fidelity.

Recommended citation: Nottingham, N., Perlin, M. A., Shah, D., White, R., Bernien, H., Chong, F. T., & Baker, J. M. (2024). "Circuit decompositions and scheduling for neutral atom devices with limited local addressability." IEEE International Conference on Quantum Computing and Engineering (QCE).
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YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs

Published in Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction (CC 2024), 2024

Explores dataflow choices and code generation to accelerate CPU inference, achieving strong speedups across NN workloads.

Recommended citation: Zhou, C., Hassman, Z., Shah, D., Richard, V., & Li, Y. (2024). "YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs." CC 2024, pp. 212–226.
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