模宇称量子近似优化
Modular Parity Quantum Approximate Optimization
摘要：
宇称变换编码自旋模型在一个更大的希尔伯特空间的低能量子空间与平面晶格上的约束。应用量子近似优化算法（QAOA），约束可以通过能量惩罚显式地强制执行，或者通过驱动器哈密顿量将动力学限制在低能量子空间来隐式地强制执行。虽然显式方法允许与系统大小无关的电路深度的并行化，隐式方法显示出更好的QAOA性能。在这里，我们结合这两种方法，以提高QAOA的性能，同时保持电路的并行化。特别是，我们引入了一个模块化的并行化方法，分区的电路成簇的子电路与固定的最大电路深度，相关的扩展到大的系统尺寸。
英文摘要：
The parity transformation encodes spin models in the low-energy subspace of a larger Hilbert-space with constraints on a planar lattice. Applying the Quantum Approximate Optimization Algorithm (QAOA), the constraints can either be enforced explicitly, by energy penalties, or implicitly, by restricting the dynamics to the low-energy subspace via the driver Hamiltonian. While the explicit approach allows for parallelization with a system-size-independent circuit depth, the implicit approach shows better QAOA performance. Here we combine the two approaches in order to improve the QAOA performance while keeping the circuit parallelizable. In particular, we introduce a modular parallelization method that partitions the circuit into clusters of subcircuits with fixed maximal circuit depth, relevant for scaling up to large system sizes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
