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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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# Abstract
This project implements the NeurIPS 2019 paper:
q-means: A quantum algorithm for unsupervised machine learning
https://papers.nips.cc/paper/8667-q-means-a-quantum-algorithm-for-unsupervi…
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# Description
The aim is to optimize the quantum computing resource during the transmission of highly entangled states pertaining to a finite set. For this purpose, a quantum state classifier base…
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### Informations
- **Qiskit Aer 0.13.0**:
- **Python 3.10.13**:
- **Ubuntu 22.04.3 LTS**:
### What is the current behavior?
I am running Aer Sampler with qiskit-aer-gpu (0.13.0, same is…
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# Abstract
Financial wellbeing involves measuring people's financial comfort in the context of their day to day expenses, future savings (e.g., retirement) and their resilience to financial shocks.
…
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Hi,
I wanted to inquire about the availability of the code for training models in Paddle-quantum, specifically related to medical image classification as found in this link: https://github.com/Paddle…
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![image](https://user-images.githubusercontent.com/24667906/129554506-f27edf06-367b-4274-8042-d06c86edcc13.png)
![image](https://user-images.githubusercontent.com/24667906/129555691-7751c0c1-a031-4…
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![image](https://user-images.githubusercontent.com/104488540/172329963-dddb9c8f-0ff5-4fe9-b3bc-161e35a79f3c.png)
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import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
class Cell:
def __init__(self, cell_type, health=1.0, mutation_rate=0.001, efficacy=0.5):
self.cell_type = …
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It would be helpful if there would be some hint about in which context to configure what simulator option, since the resulting
performance is sometimes quite "surprizing".
Of course this is someh…