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This work amortizes a major bottleneck in QAOA by an up-front data generation and training cost and can be amortized over time in settings where many similar problems are optimized on a regular basis, as is often the case in applications for industry.
This work proposes a transpilation scheme for circuits consisting of blocks of commuting two-qubit gates on two-dimensional lattices that alternates between constructing problem-dependent SWAP-layer sequences and updating the qubit layout, yielding significantly shorter circuits for graphs with few edges.
A noise-aware adaptive approach to quantum approximate optimization, Noise-Directed Adaptive Warm-Starting (ND-AWS), that builds on recent concepts such as Warm-Start QAOA and Noise-Directed Adaptive Remapping by leveraging bitflip gauge transformations, and exploits amplitude-damping-like noise components.
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