readme for QPUs

quantum_processors/README.md

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+## High-resolution image classification with real quantum computers.
+This folder contains the code that has been used in the paper [1] to experimentally classify high-resolution images of _ants_ and _bees_ with real quantum processors provided by IBM and Rigetti.
+
+The model is a hybrid neural network composed of a classical block (ResNet18) and a variational "dressed quantum circuit" [1], which has been numerically optimized according to the classical-to-quantum (CQ) transfer learning paradigm. 
+
+This code can be used to test the optimized model with two alternative quantum processing units (QPUs):
+1. Rigetti Aspen-4-4Q-A.
+2. IBM Q 5, Tenerife (ibmq4x).
+
+![Figure](static/realQPUs.png)
+
+## Contents
+* `test_on_QPU.py`: Python script to test the hybrid classical-quantum image classifier with IBM or Rigetti quantum processors. 
+
+* `quantum_weights.pt`: pre-trained model loaded by `test_on_QPU.py` to initialize the variational parameters of the hybrid network.
+
+## Usage and examples
+
+To test the model with the Rigetti backend:
+
+1. Make sure to satisfy all requirements (see next section).
+2. Login to the a QVM provided by Rigetti.
+3. Download or clone the repository in the QVM.
+4. Download the _Hymenoptera_ dataset as described in the main README file of the repo. 
+5. Edit the file `test_on_QPU.py` and set `backend = 'rigetti'`.
+6. Run the command `python3 test_on_QPU.py`.
+
+To test the model with the IBM backend:
+
+1. Make sure to satisfy all requirements (see next section).
+2. Download or clone the repository.
+3. Download the _Hymenoptera_ dataset as described in the main README file the repo.
+4. Edit the local file `test_on_QPU.py` and set `backend = 'ibm'`.
+4. Moreover, set your personal IBM token by editing `token = '<your token>'`.
+5. Run the command `python3 test_on_QPU.py`.
+
+## Requirements
+
+Testing the model with the Rigetti backend requires having a Rigetti QCS account with access to a remote quantum virtual machine (QVM). In the QVM, the Python library PennyLane should be installed together with the associated PyTorch and Rigetti plugins. 
+
+Testing the model with the IBM backend requires having an IBM Q Experience account with access to a remote quantum virtual machine (QVM). In the QVM, the Python library PennyLane should be installed together with the associated PyTorch and Rigetti plugins.