Snehal Raj

2025

1. npj QI

   Training-Efficient Density Quantum Machine Learning

   Brian Coyle, Snehal Raj, Natansh Mathur, and 4 more authors

   npj Quantum Information, 2025

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   We introduce a novel approach to quantum machine learning that leverages density matrices for more efficient training. Our method demonstrates improved sample complexity and training efficiency compared to traditional quantum neural network approaches.

   @article{coyle2025training,
     title = {Training-Efficient Density Quantum Machine Learning},
     author = {Coyle, Brian and Raj, Snehal and Mathur, Natansh and Cherrat, El Amine and Jain, Nishant and Kazdaghli, Sofiene and Kerenidis, Iordanis},
     journal = {npj Quantum Information},
     volume = {11},
     number = {1},
     pages = {172},
     year = {2025},
     publisher = {Nature Publishing Group},
   }

2. arXiv

   QuIC: Quantum-Inspired Compound Adapters for Parameter Efficient Fine-Tuning

   Snehal Raj, and Brian Coyle

   arXiv preprint arXiv:2502.06916, 2025

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   We introduce QuIC, quantum-inspired adapter modules that operate with less than 0.02% memory footprint while maintaining pretrained representations through orthogonal weight parameters. Evaluated on LLaMA and vision transformers, our advanced configurations achieve over 40x compression compared to LoRA.

   @article{raj2025quic,
     title = {QuIC: Quantum-Inspired Compound Adapters for Parameter Efficient Fine-Tuning},
     author = {Raj, Snehal and Coyle, Brian},
     journal = {arXiv preprint arXiv:2502.06916},
     year = {2025},
   }

3. arXiv

   Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection

   Natansh Mathur, Brian Coyle, Nishant Jain, and 4 more authors

   arXiv preprint arXiv:2504.18103, 2025

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   We propose a Bayesian approach to quantum orthogonal neural networks for anomaly detection tasks. Our method combines the expressiveness of quantum neural networks with Bayesian uncertainty quantification.

   @article{mathur2025bayesian,
     title = {Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection},
     author = {Mathur, Natansh and Coyle, Brian and Jain, Nishant and Raj, Snehal and Tandon, Akshat and Krauser, Jasper Simon and Stoessel, Rainer},
     journal = {arXiv preprint arXiv:2504.18103},
     year = {2025},
   }

2023

1. Quantum

   Quantum Deep Hedging

   El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, and 12 more authors

   Quantum, 2023

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   Quantum machine learning techniques are being explored for a variety of financial applications. In this work, we develop a quantum approach for the problem of deep hedging, a machine learning approach for managing the risk associated with derivatives trading.

   @article{cherrat2023quantum,
     title = {Quantum Deep Hedging},
     author = {Cherrat, El Amine and Raj, Snehal and Kerenidis, Iordanis and Shekhar, Abhishek and Wood, Ben and Dee, Jon and Chakrabarti, Shouvanik and Chen, Richard and Herman, Dylan and Hu, Shaohan and Minssen, Pierre and Shaydulin, Ruslan and Sun, Yue and Yalovetzky, Romina and Pistoia, Marco},
     journal = {Quantum},
     volume = {7},
     pages = {1191},
     year = {2023},
     doi = {10.22331/q-2023-11-29-1191},
   }

2021

1. EMNLP

   AUTOSUMM: Automatic Model Creation for Text Summarization

   Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, and 4 more authors

   In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

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   We present AutoSumm, a system for automatic model creation for text summarization. Our approach enables the automated development of customized summarization models without extensive manual intervention.

   @inproceedings{nangi2021autosumm,
     title = {AUTOSUMM: Automatic Model Creation for Text Summarization},
     author = {Nangi, Sharmila Reddy and Tyagi, Atharv and Mundra, Jay and Mukherjee, Sagnik and Snehal, Raj and Chhaya, Niyati and Garimella, Aparna},
     booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
     pages = {9981--9991},
     year = {2021},
     organization = {Association for Computational Linguistics},
   }