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20192022
most citedeXplainable AI for Quantum Machine Learning

9 citations · 14 across the 4 of their papers we have counts for

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5 papers · 1 filter

quant-ph20222 cited

Transversal Injection: A method for direct encoding of ancilla states for non-Clifford gates using stabiliser codes

Jason Gavriel, Daniel Herr, Alexis Shaw +3

Fault-tolerant, error-corrected quantum computation is commonly acknowledged to be crucial to the realisation of large-scale quantum algorithms that could lead to extremely impactf…

quant-ph20229 cited

eXplainable AI for Quantum Machine Learning

Patrick Steinmüller, Tobias Schulz, Ferdinand Graf +1

Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML). However, from a computational point of view they present a challenge to existing eXplainable A…

quant-ph2020

Anomaly detection with variational quantum generative adversarial networks

Daniel Herr, Benjamin Obert, Matthias Rosenkranz

Generative adversarial networks (GANs) are a machine learning framework comprising a generative model for sampling from a target distribution and a discriminative model for evaluat…

quant-ph20192 cited

Time versus Hardware: Reducing Qubit Counts with a (Surface Code) Data Bus

Daniel Herr, Alexandru Paler, Simon J. Devitt +1

We introduce a data bus, for reducing the qubit counts within quantum computations (protected by surface codes). For general computations, an automated trade-off analysis (software…

quant-ph20191 cited

Really Small Shoe Boxes - On Realistic Quantum Resource Estimation

Alexandru Paler, Daniel Herr, Simon J. Devitt

Reliable resource estimation and benchmarking of quantum algorithms is a critical component of the development cycle of viable quantum applications for quantum computers of all siz…