Swap Network Augmented Ans\"atze on Arbitrary Connectivity
Teodor Parella-Dilm\'e, Jakob S. Kottmann, Antonio Ac\'in

TL;DR
This paper introduces a swap network algorithm for arbitrary qubit connectivity, embedding it into ansätze to improve trainability and resource efficiency in quantum state parametrization for complex system simulations.
Contribution
It presents a novel algorithm for optimizing qubit routing and a co-designed ansatz that enhances trainability and reduces resource requirements on connectivity-limited quantum devices.
Findings
Lower energy errors with fewer gates and parameters.
Shallower circuits achieved compared to standard methods.
Consistent performance improvements across different connectivities.
Abstract
Efficient parametrizations of quantum states are essential for trainable hybrid classical-quantum algorithms. A key challenge in their design consists in adapting to the available qubit connectivity of the quantum processor, which limits the capacity to generate correlations between distant qubits in a resource-efficient and trainable manner. In this work we first introduce an algorithm that optimizes qubit routing for arbitrary connectivity graphs, resulting in a swap network that enables direct interactions between any pair of qubits. We then propose a co-design of circuit layers and qubit routing by embedding the derived swap networks within layered, connectivity-aware ans\"atze. This construction significantly improves the trainability of the ansatz, leading to enhanced performance with reduced resources. We showcase these improvements through ground-state simulations of strongly…
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