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# Abstract
The good ansatz generation is crucial for any quantum variational algorithm like QAOA and VQE in the NISQ era. [Sim at el](https://arxiv.org/abs/1905.10876) analyzed various forms of Par…
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_**(submission under construction)**_
### Team Name:
PhaseliciousDeinonyqus
### Project Description:
Typically, variational quantum circuits are parameterized by **classical** parameters…
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The current implementation only allows the creation of measurement patterns with a fixed set of parameters (rotation angles). However, for quantum-classical algorithms such as VQE or QAOA, it is desir…
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**Describe the feature you'd like**
Native Julia implementation of the [adjoint gradient](https://pennylane.ai/qml/demos/tutorial_adjoint_diff/) differentiation method for the `StateVectorSimulator`.…
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### URL to the relevant documentation
https://docs.quantum.ibm.com/api/qiskit/primitives#overview-of-samplerv2
### Select all that apply
- [X] typo
- [X] code bug
- [ ] out-of-date content
- [ ] …
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# Description
We are currently writing a chapter for the Qiskit Textbook on quantum machine learning. The contents will be:
- Introduction
- Parameterized Quantum Circuits
- Data Encoding
…
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### Team Name:
cirKITers
### Project Description:
Nowadays training parameterized quantum circuits is very popular to explore the space of quantum states. This field heavily borrows from ex…
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### Team Name:
Voyager
### Project Description:
This project is about applying qunatum machine learning in the field of astronomy. We successfully detected galaxy with accuracy of 94% by quan…
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# Description
The idea is to study VQE applications and identify a viable problem to design custom parametrized quantum circuits for and verify/build upon results from the following papers:
1. [Prob…
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### Environment
- **Qiskit version**: main
- **Python version**: doesn't matter
- **Operating system**: doesn't matter
### What is happening?
When I build a Qiskit `Target` that supports …