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Modular Parity Quantum Approximate Optimization
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AI
DOI：10.1103/PRXQuantum.3.030304.png)
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Abstract
En 中文
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 paral-lelization with a system-size-independent circuit depth, we show that the implicit approach exhibits better QAOA performance. We propose a generalization of the two approaches in order to improve the QAOA performance while keeping the circuit parallelizable. Furthermore, 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.
Keywords:
SIMULATION
QUBITS
Journal
P
IF:
11
Papers:
919
Citations:
9.0K