Barren plateaus in quantum neural network training landscapes

Understand

Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop.

• Such hybrid quantum-classical algorithms are popular for applications in quantum simulation, optimization, and machine learning.
• Due to its simplicity and hardware efficiency, random circuits are often proposed as initial guesses for exploring the space of quantum states.
• We show that the exponential dimension of Hilbert space and the gradient estimation complexity make this choice unsuitable for hybrid quantum-classical algorithms run on more than a few qubits.