4 citations · 4 across the 6 of their papers we have counts for
6 papers
Stacking the Deck: Tunable Trainability in Stacked LCUs
Nikhil Khatri, Stefan Zohren, Gabriel Matos
Variational quantum circuits have been central to many proposed near-term applications of quantum computing, but a growing body of evidence suggests that trainability and quantum a…
Graphical einops: bridging tensor networks and computation graphs
Vincent Wang-Maścianica, Nikhil Khatri
Architecture diagrams are ubiquitous in deep learning, but they are usually only representational: the tensor-program identities they suggest are still proved by prose and tensor-a…
Trainability of Parametrised Linear Combinations of Unitaries
Nikhil Khatri, Stefan Zohren, Gabriel Matos
A principal concern in the optimisation of parametrised quantum circuits is the presence of barren plateaus, which present fundamental challenges to the scalability of applications…
Efficient Generation of Parameterised Quantum Circuits from Large Texts
Colin Krawchuk, Nikhil Khatri, Neil John Ortega +1
Quantum approaches to natural language processing (NLP) are redefining how linguistic information is represented and processed. While traditional hybrid quantum-classical models re…
On the Anatomy of Attention
Nikhil Khatri, Tuomas Laakkonen, Jonathon Liu +1
We introduce a category-theoretic diagrammatic formalism in order to systematically relate and reason about machine learning models. Our diagrams present architectures intuitively…
Quixer: A Quantum Transformer Model
Nikhil Khatri, Gabriel Matos, Luuk Coopmans +1
Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum…