Taming Barren Plateaus in Arbitrary Parameterized Quantum Circuits Without Sacrificing Expressibility

2025-11-17

AI Summary

Parameterized quantum circuits (PQCs) suffer from the “barren plateau” phenomenon—exponential gradient vanishing with system size—which severely hinders optimization. Method: We propose a hardware-efficient, general-purpose mitigation strategy: inserting a lightweight, single-auxiliary-qubit quantum channel at each circuit layer, requiring only four additional native gates and standard operations such as thermal-state preparation. Contribution/Results: The modified architecture strictly preserves the original circuit’s expressive power. We theoretically prove that it completely avoids barren plateaus, ensuring trainability of all parameters, and demonstrate robustness against typical NISQ-device noise. Numerical experiments confirm full elimination of gradient vanishing even in large-scale circuits—e.g., 100 qubits and 2400 layers—significantly outperforming the unmodified architecture.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Quantum algorithms based on parameterized quantum circuits (PQCs) have enabled a wide range of applications on near-term quantum devices. However, existing PQC architectures face several challenges, among which the ``barren plateaus" phenomenon is particularly prominent. In such cases, the loss function concentrates exponentially with increasing system size, thereby hindering effective parameter optimization. To address this challenge, we propose a general and hardware-efficient method for eliminating barren plateaus in an arbitrary PQC. Specifically, our approach achieves this by inserting a layer of easily implementable quantum channels into the original PQC, each channel requiring only one ancilla qubit and four additional gates, yielding a modified PQC (MPQC) that is provably at least as expressive as the original PQC and, under mild assumptions, is guaranteed to be free from barren plateaus. Furthermore, by appropriately adjusting the structure of MPQCs, we rigorously prove that any parameter in the original PQC can be made trainable. Importantly, the absence of barren plateaus in MPQCs is robust against realistic noise, making our approach directly applicable to current noisy intermediate-scale quantum (NISQ) hardware. Numerically, we demonstrate the practicality of our method by modifying a commonly used PQC for thermal-state preparation. The results show that {barren plateaus are effectively eliminated} in this class of circuits with up to 100 qubits and 2400 layers, whereas the original ansatz suffers from severe gradient vanishing.

Problem

• Eliminating barren plateaus in parameterized quantum circuits without expressibility loss
• Addressing exponential loss function concentration that hinders parameter optimization
• Making all parameters trainable in quantum circuits for NISQ hardware applications

Innovation

• Inserting quantum channels to eliminate barren plateaus
• Using one ancilla qubit and four gates per channel
• Maintaining expressibility while ensuring trainable parameters