Yen-chi Chen, Wells Fargo


- Variational Quantum Circuits
- Parameterized Quantum Circuit (PQC) - "The other PQC"





|0> --- encoding gate --- parameterized -----measurement 

Rz = encoding gate
Ry = parameterized gate 


[ sin(x)] $$ [[\sin x]] \sqrt{ y }$$

## Quantum gradients =
- the gradient of with respect to the parameter theta - the value can be calculated by running two quantum circuits with parameterized circuits - called parameter shift 
- f (x,y,z) = z x Q(x + y) 


Quantum CNN - 
- use image classification to QCNN wireachhigh accuraccy wuth smaller number of epochs 
- quantum federarted learning - they don't share the dataset 
- they will train on their own and they only share the models 


## Quantum Fedrated ML with Differential Privacy 
- During our training we get some noise to get our gradient 
- to ensure that our data is differential and private 
- Watkins, Yoo -- *Differential machine learning with differential privacy*


## Quantum LSTM 
- Quantum long short-term memory 
- replace the classical neural networks with variational quantum circuits (VQC_)
- QLSTM can show ---- read the paper 

Quantum RL 
- quantum agents in the Gym - a vriational quantum algorithm 
- quantum rl is similar to classical rl except replace 


Paper : *Efficient quantum recurrent reinforcdement learning via quantum reservoir computing*, 2024


When BERT meets quantum - paper 


1. H QC P > using classical computer to optimize ]
2. Variational quantum circuits aka paramtererized quantum circuits 
3. can be used with classical components and whole model improved with gradient descent methods
4.

Paper outline for structure: 
- Motivation 
- Challenge: 
- Approach 



- Create benchmarks for quantum machine learning - very hard to convince classical  machine learning people that quantum is useful.. 
- how to mitigate barren plateau