Now this example is simple and has no QNNs. In general, steps of building and training QNNs would be simmilar to the building standard neural networks. Here they are: 1. Prepare Quantum Dataset - As with regular neural networks, we need to build dataset first. This is done by creating unparameterized cirq.Circuit objects and then injecting them into computation graph with tfq.convert_to_tensor. 2. Evaluate Quantum Model - In this step, we avaluate how well our quantum model is performing. It’s main goal is to perform a quantum computation in order to extract information hidden in a quantum subspace. 3. Sample or Average - In this step we extract classical information in the forme of samples from a classical random variable. Quantum state and measured observable impact distribution values of this random variable. 4. Evaluate Classical Model - We use deep neural networks to distill correlations between measured expectations. 5. Evaluate Cost Function - Cost function is calculated and evaluated based on the results from the previous step. 6. Evaluate Gradients & Update Parameters - After evaluation