Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
This addresses a critical trainability issue for quantum computing practitioners, offering a theoretical foundation to revise cost functions in existing algorithms, though it is incremental in refining known bottlenecks.
The paper tackles the problem of vanishing gradients (barren plateaus) in variational quantum algorithms by proving that global observables cause exponential gradient decay even in shallow circuits, while local observables allow polynomial decay with logarithmic depth, and validates this with simulations up to 100 qubits.
Variational quantum algorithms (VQAs) optimize the parameters $\vecθ$ of a parametrized quantum circuit $V(\vecθ)$ to minimize a cost function $C$. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming $V(\vecθ)$ is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining $C$ in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when $V(\vecθ)$ is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining $C$ with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of $V(\vecθ)$ is $\mathcal{O}(\log n)$. Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.