qiskit-app-benchmarks
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Qiskit Application Benchmarks
### Environment - **Qiskit Finance version**: - **Qiskit Machine Learning version**: - **Qiskit Nature version**: - **Qiskit Optimization version**: - **Python version**: - **Operating system**: ### What is happening? The...
### Summary Implementation of the benchmarks methods for `QuantumKernel` and `QuantumKernelTrainer`. ### Details and comments These codes are developed in the project of QAMP22 (mentor @a-matsuo, mentee Leonardo Placidi). ----We...
### What is the expected enhancement? Yesterday, Qiskit Nature merged a major code restructuring PR (https://github.com/Qiskit/qiskit-nature/pull/746). In order to be able to track the performance improvement during active development, the...
### What is the expected enhancement? Currently, there are no benchmarks for Pegasos Quantum Support Vector Classifier. The implementation is quite similar to the QSVC.
### What is the expected enhancement? Currently, only binary classification benchmarks are implemented for `VQC`. Since a few bugs have been fixed in QML multiclass classification benchmarks can be added.
According to this announcement, there will be a new backend powered by NVIDIA available through Qiskit as an Aer backend: https://blogs.nvidia.com/blog/2021/11/09/google-quantum-computing/. The new backend is claimed to be very efficient....
The goal of this epic is to implement benchmarks for Qiskit Nature that would be representative of the module's overall performance. The list of ideas for benchmarks is maintained internally.
The goal of this epic is to implement benchmarks for Qiskit ML that would be representative of the module's overall performance. The list of ideas for benchmarks is maintained internally.