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I think that a function to generate parameterized GGM matrices will be useful especially on TFQ for the PQCs (they are used in [Quantum Convolutional Neural Networks (arXiv:1810.03787v2)](https://arxi…
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Project Name:
Quantum Neural Network Autoencoder
Team Name:
Quantum Synapse
Which challenges would you like to submit your project for?
Hybrid Quantum-Classical Computing Challenge
TBD
Pr…
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# Abstract
Using quantum generative neural networks for estimating the probability of being admitted to university based on CSAT(Korean SAT) score.
# Description
South Korea has the two typ…
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I am using `TFQ` for executing of quantum neural networks, but as I need it to be faster, I am trying to run it on multiple GPUs. Has `tensorflow-quantum` team did something regarding parallel computi…
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## Issue Description
I think it will be great if we have blocks to easily construct QCNN, which is a special but important category of quantum neural networks requiring mid-circuit measurements.
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### Description
The qiskit-neko project: https://github.com/mtreinish/qiskit-neko is a new effort to add a proper integration test suite to the overall qiskit project. It's designed to be run in CI t…
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### Team Name:
Hooked on Photonics
### Project Description:
One of the most promising applications of variational quantum algorithms is the study of condensed matter phenomena, such as quan…
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# Abstract
The analysis of over-parameterised Artificial Neural Networks (ANNs) reveal that the optimisation (training) process only slightly changes the parameters of the model. This allows one to a…
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## 🚀 Feature
Currently you cannot use digits in names of tensors. Please allow digits, so that `torch.randn(64, 64, 4, 4, names=("height", "width", "channel_0", "channel_1"))` works rather than thr…
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Some types of circuits rely on tuning parameters but keeping the overall architecture intact, for large circuits it would be more convenient to have ops which can read from a RWLock protected variable…