Compilation for Dynamically Field-Programmable Qubit Arrays with Efficient and Provably Near-Optimal Scheduling

TL;DR

This paper introduces Enola, a compiler for dynamically field-programmable qubit arrays that optimizes scheduling, placement, and routing, significantly reducing gate stages and improving fidelity for large-scale quantum circuits.

Contribution

It presents a near-optimal scheduling algorithm based on graph edge-coloring and a scalable compilation framework for complex quantum hardware.

Findings

Reduces two-qubit gate stages by 3.7x compared to previous methods.

Improves quantum circuit fidelity by 5.9x.

Successfully compiles circuits with up to 10,000 qubits within 30 minutes.

Abstract

Dynamically field-programmable qubit arrays based on neutral atoms feature high fidelity and highly parallel gates for quantum computing. However, it is challenging for compilers to fully leverage the novel flexibility offered by such hardware while respecting its various constraints. In this study, we break down the compilation for this architecture into three tasks: scheduling, placement, and routing. We formulate these three problems and present efficient solutions to them. Notably, our scheduling based on graph edge-coloring is provably near-optimal in terms of the number of two-qubit gate stages (at most one more than the optimum). As a result, our compiler, Enola, reduces this number of stages by 3.7x and improves the fidelity by 5.9x compared to OLSQ-DPQA, the current state of the art. Additionally, Enola is highly scalable, e.g., within 30 minutes, it can compile circuits with 10,000 qubits, a scale sufficient for the current era of quantum computing. Enola is open source at https://github.com/UCLA-VAST/Enola