92 citations · 240 across the 12 of their papers we have counts for
13 papers
Logarithmic-Depth Fermion Sampling: Anticoncentration and Average-Case Hardness
Natansh Mathur, Iordanis Kerenidis
Sampling problems give some of the clearest conditional separations between quantum and classical computation. In Fermion Sampling, passive linear optics acts on a non-Gaussian mag…
"Train classical, deploy quantum" requires rethinking generalization
Snehal Raj, Natansh Mathur, Alejandro Perdomo-Ortiz
Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum generative models are considered…
Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada +2
Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circu…
Unsupervised Physics-Informed Operator Learning through Multi-Stage Curriculum Training
Paolo Marcandelli, Natansh Mathur, Stefano Markidis +2
Solving partial differential equations remains a central challenge in scientific machine learning. Neural operators offer a promising route by learning mappings between function sp…
Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
Natansh Mathur, Brian Coyle, Nishant Jain +4
Identification of defects or anomalies in 3D objects is a crucial task to ensure correct functionality. In this work, we combine Bayesian learning with recent developments in quant…
Training-efficient density quantum machine learning
Brian Coyle, Snehal Raj, Natansh Mathur +4
Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural ne…
Towards a Unified Quantum Protocol Framework: Classification, Implementation, and Use Cases
Shraddha Singh, Mina Doosti, Natansh Mathur +4
We present a framework for the unification and standardization of quantum network protocols, making their realization easier and expanding their use cases to a broader range of com…
Quantum Fourier Networks for Solving Parametric PDEs
Nishant Jain, Jonas Landman, Natansh Mathur +1
Many real-world problems, like modelling environment dynamics, physical processes, time series etc., involve solving Partial Differential Equations (PDEs) parameterised by problem-…
Improved Financial Forecasting via Quantum Machine Learning
Sohum Thakkar, Skander Kazdaghli, Natansh Mathur +3
Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to im…
Quantum Methods for Neural Networks and Application to Medical Image Classification
Jonas Landman, Natansh Mathur, Yun Yvonna Li +4
Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum met…
Quantum Vision Transformers
El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur +3
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very perform…
Medical image classification via quantum neural networks
Natansh Mathur, Jonas Landman, Yun Yvonna Li +4
Machine Learning provides powerful tools for a variety of applications, including disease diagnosis through medical image classification. In recent years, quantum machine learning…
Classical and Quantum Algorithms for Orthogonal Neural Networks
Iordanis Kerenidis, Jonas Landman, Natansh Mathur
Orthogonal neural networks have recently been introduced as a new type of neural networks imposing orthogonality on the weight matrices. They could achieve higher accuracy and avoi…