Quantum Federated Gradient Aggregation Using the Parameter-Shift Rule

초록

Spurred by the limited availability of quantum resources, known as qubits, in recent quantum computers, quantum federated learning (QFL) is drawing attention. Due to its ability to fully utilize distributed qubits, QFL is suitable for developing quantum algorithms. QFL achieves local quantum gradients using the parameter-shift (P-S) rule and aggregates them, effectively coping with the limited number of qubits in each quantum computer. However, realizing QFL remains challenging due to the characteristics of the P-S rule, which requires two forward passes to compute the quantum gradients in each local quantum computer. These challenges become even more severe when the aggregation of the local quantum gradients occurs under heterogeneous channel conditions and data distributions. Motivated by this, this article proposes the joint P-S rule, which eliminates the aggregation process in QFL and instead directly achieves the global quantum gradients. Furthermore, this article proposes a joint efficient QFL (Joint EQFL) that leverages successive interference cancellation (SIC) and divergence-based clustering for achieving stability under heterogeneous channel conditions and robustness to heterogeneous data distributions. This article analyzes the convergence and corroborates the superiority of Joint EQFL.

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