Dariusz Lasecki

Implement quantum circuits for convolutional neural networks.

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The goal of this issue is to build quantum circuits that execute the quantum part of the processing.

TODO

QBMs consist of visible and hidden units which define a partition of an underlying Gibbs state used for sampling. The goal of this issue is to ignore hidden qubits in...

The goal of this issue is to prepare a proof of concept implementation of quantum convolutional neural networks. It might be used for benchmarking, performance comparisons etc.

This issue might imply some changes in the code design.

Implements VarQTE algorithm with primitives.

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### Summary This PR implements the VarQTE algorithm with primitives - it uses primitive-enabled QFI and gradients. Closes: #8485. ### Details and comments This PR depends on #8683 and #8688...

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.