An initialization strategy for addressing barren plateaus in parametrized quantum circuits

Edward Grant1, Leonard Wossnig1, Mateusz Ostaszewski2, and Marcello Benedetti3

1Rahko Limited & Department of Computer Science, University College London
2Institute of Theoretical and Applied Informatics, Polish Academy of Sciences
3Cambridge Quantum Computing Limited & Department of Computer Science, University College London

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Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of qubits. In this technical note we theoretically motivate and empirically validate an initialization strategy which can resolve the barren plateau problem for practical applications. The technique involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity. This initialization limits the effective depth of the circuits used to calculate the first parameter update so that they cannot be stuck in a barren plateau at the start of training. In turn, this makes some of the most compact ansätze usable in practice, which was not possible before even for rather basic problems. We show empirically that variational quantum eigensolvers and quantum neural networks initialized using this strategy can be trained using a gradient based method.

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