most citedQuantum Machine Learning using the ZXW-Calculus

7 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023

GraphRNN Revisited: An Ablation Study and Extensions for Directed Acyclic Graphs

Taniya Das, Mark Koch, Maya Ravichandran +1

GraphRNN is a deep learning-based architecture proposed by You et al. for learning generative models for graphs. We replicate the results of You et al. using a reproduced implement…

quant-ph2023★ 6 cited

Speedy Contraction of ZX Diagrams with Triangles via Stabiliser Decompositions

Mark Koch, Richie Yeung, Quanlong Wang

Recent advances in classical simulation of Clifford+T circuits make use of the ZX calculus to iteratively decompose and simplify magic states into stabiliser terms. We improve on t…

cs.CV2023

Paint it Black: Generating paintings from text descriptions

Mahnoor Shahid, Mark Koch, Niklas Schneider

Two distinct tasks - generating photorealistic pictures from given text prompts and transferring the style of a painting to a real image to make it appear as though it were done by…

quant-ph2022★ 7 cited

Quantum Machine Learning using the ZXW-Calculus

Mark Koch

The field of quantum machine learning (QML) explores how quantum computers can be used to more efficiently solve machine learning problems. As an application of hybrid quantum-clas…

quant-ph2022★ 4 cited

Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning

Quanlong Wang, Richie Yeung, Mark Koch

ZX-calculus has proved to be a useful tool for quantum technology with a wide range of successful applications. Most of these applications are of an algebraic nature. However, othe…