1.8 KiB
1.8 KiB
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
- H QC P > using classical computer to optimize ]
- Variational quantum circuits aka paramtererized quantum circuits
- can be used with classical components and whole model improved with gradient descent methods
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