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An initialization strategy for addressing barren plateaus in parametrized quantum circuits
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An initialization strategy for addressing barren plateaus in parametrized quantum circuits
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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 ans\"atze 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.
Forward citations
Cited by 4 Pith papers
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Trainability Beyond Linearity in Variational Quantum Objectives
The trainability boundary for variational quantum objectives is the affine regime; non-affine amplification-capable losses can mitigate barren plateaus when using coarse-grained statistics at polynomial widths.
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Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
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Classical residual weights can be compiled into subspace quantum generators with a two-term error bound, enabling zero-shot transfer and initialization-time barren-plateau mitigation.
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Quantum computation at the edge of chaos
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.
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