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 near-term quantum hardware. Numerical simulations demonstrate that MPQC effectively eliminates barren plateaus in PQCs for preparing thermal states of systems with up to 100 qubits and 2400 layers. Furthermore, in end-to-end simulations, MPQC significantly outperforms PQC in finding the ground-state energy of a complex Hamiltonian.

翻译：基于参数化量子电路（PQCs）的量子算法已在近期量子设备上实现了广泛的应用。然而，现有的PQC架构面临若干挑战，其中“贫瘠高原”现象尤为突出。在此类情况下，损失函数随系统规模增大呈指数级集中，从而阻碍了有效的参数优化。为应对这一挑战，我们提出了一种通用且硬件高效的方法，用于消除任意PQC中的贫瘠高原。具体而言，我们的方法通过在原始PQC中插入一层易于实现的量子信道来实现这一目标，每个信道仅需一个辅助量子比特和四个额外门，从而得到一个改进的PQC（MPQC）。该MPQC被证明至少与原始PQC具有同等表达能力，并且在温和假设下可确保不存在贫瘠高原。此外，通过适当调整MPQC的结构，我们严格证明了原始PQC中的任何参数均可变得可训练。重要的是，MPQC中贫瘠高原的缺失对实际噪声具有鲁棒性，这使得我们的方法可直接应用于近期量子硬件。数值模拟表明，在制备多达100个量子比特和2400层的系统热态时，MPQC能有效消除PQC中的贫瘠高原。此外，在端到端模拟中，MPQC在寻找复杂哈密顿量的基态能量方面显著优于PQC。