We explore the problem of barren plateaus [1] in Quantum Neural Networks: a certain large family of random quantum circuits have gradients that vanish almost everywhere.

Also, we explore the identity heuristic [2] initialization strategy as possible solution to overcome this problem.

This project follows the TensorFlow Quantum tutorial Barren plateaus [3].

• tensorflow 2.1.0
• tensorflow-quantum 0.3.1

Install them with:

$ pip install -r requirements.txt

A quantum hardware is not required. The circuits are simulated using the Cirq library [4].

Run the experiments:

$ python3 barren.py

For the oral exam of Quantum Information, I presented the experiments made alongside the theory from the papers using this presentation.

[1] J.R. McClean, S. Boixo, V.N. Smelyanskiy et al. Barren plateaus in quantum neural network training landscapes. (2018)

[2] E. Grant, L. Wossnig, M. Ostaszewski and M. Benedetti. An initialization strategy for addressing barren plateaus in parametrized quantum circuits. (2019)

[3] TensorFlow Quantum tutorials. Barren plateaus https://www.tensorflow.org/quantum/tutorials/barren_plateaus?hl=en

[4] Cirq library. https://quantumai.google/cirq

The directory cirq_examples contains some examples about using the Cirq library. They have been useful to understand how the library works.