**Variational Entanglement Detection** is a variational quantum algorithm which uses criteria based on positive maps as a bridge and works as follows. Given an unknown target bipartite quantum state, it firstly decomposes the chosen positive map into a linear combination of NISQ implementable quantum operations. Then, it variationally estimates the minimal eigenvalue of the final state, obtained by executing these quantum operations on the target state and averaging the output states. Deterministic and probabilistic methods are proposed to compute the average. At last, it asserts that the target state is entangled if the optimized minimal eigenvalue is negative. VLNE builds upon a linear decomposition of the transpose map into Pauli terms and the recently proposed trace distance estimation algorithm. It variationally estimates the well-known logarithmic negativity entanglement measure and could be applied to quantify entanglement on near-term quantum devices.

The **Hard Sigmoid** is an activation function used for neural networks of the form:

$$f\left(x\right) = \max\left(0, \min\left(1,\frac{\left(x+1\right)}{2}\right)\right)$$

Image Source: [Rinat Maksutov](https://towardsdatascience.com/deep-study-of-a-not-very-deep-neural-network-part-2-activation-functions-fd9bd8d406fc)

Hard Sigmoid

BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Variational Entanglement Detection

Detecting and quantifying entanglement on near-term quantum devices

**Linear Warmup** is a learning rate schedule where we linearly increase the learning rate from a low rate to a constant rate thereafter. This reduces volatility in the early stages of training.

Image Credit: [Chengwei Zhang](https://www.dlology.com/about-me/)

Source	Detecting and quantifying entanglement on near-term quantum devices
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com