Vinayak Sharma

2025

1. LUCI: Lightweight UI Command Interface

   Lagudu, Guna and Sharma, Vinayak and Shrivastava, Aviral

   BibTex

   @inproceedings{LUCI2025,
     author = {Lagudu, Guna and Sharma, Vinayak and Shrivastava, Aviral},
     title = {LUCI: Lightweight UI Command Interface},
     year = {2025},
     isbn = {9798400719219},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3735452.3735536},
     doi = {10.1145/3735452.3735536},
     booktitle = {Proceedings of the 26th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems},
     pages = {182-191},
     numpages = {10},
     keywords = {Embedded Systems, LLM, System Automation},
     location = {Seoul, Republic of Korea},
     series = {LCTES '25}
   }
   

2024

1. Primer on Data in Quantum Machine Learning

   Shrivastava, Aviral and Sharma, Vinayak

   BibTex

   @inproceedings{QuantumPrimer2024,
     author = {Shrivastava, Aviral and Sharma, Vinayak},
     booktitle = {2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)},
     title = {Primer on Data in Quantum Machine Learning},
     year = {2024},
     volume = {},
     number = {},
     pages = {19-20},
     keywords = {Performance evaluation;Quantum system;Machine learning algorithms;Quantum algorithm;Embedded systems;Computer aided software engineering;Machine learning;Computer architecture;Quantum state;Machine Learning;Quantum Information;Quantum Computing},
     doi = {10.1109/CASES60062.2024.00010}
   }
   

2023

1. Quantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings Pre-Print

   Sharma, Vinayak and Shrivastava, Aviral

   BibTex

   @misc{sharma2023quantum,
     title = {Quantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings},
     author = {Sharma, Vinayak and Shrivastava, Aviral},
     year = {2023},
     eprint = {2312.01655},
     archiveprefix = {arXiv},
     primaryclass = {quant-ph},
     url = {https://mpslab-asu.github.io/QPMeL/},
     preprint = {true}
   }