AutoQuREO automates full-stack quantum resource estimation by learning surrogate models from small compilations and using them for scalable multi-objective co-design, demonstrated on Trotter simulation, iQPE with error correction, and QAOA.
Design and efficiency in graph-state computation
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abstract
The algorithm-specific graph and circuit etching are two strategies for compiling a graph state to implement quantum computation. Benchmark testing exposed limitations to the proto-compiler, Jabalizer giving rise to Etch (https://github.com/QSI-BAQS/Etch), an open-source, circuit-etching tool for transpiling a quantum circuit to a graph state. The viability of circuit etching is evaluated, both as a resource allocation strategy for distilling magic states and as an alternative to the algorithm-specific graph strategy as realised in Jabalizer. Experiments using Etch to transpile IQP circuits to an equivalent graph state resulted in higher ratios of Pauli qubits to non-Pauli qubit than required for efficient magic state distillation. Future research directions for the algorithm-specific graph and circuit-etching strategies are proposed.
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AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization
AutoQuREO automates full-stack quantum resource estimation by learning surrogate models from small compilations and using them for scalable multi-objective co-design, demonstrated on Trotter simulation, iQPE with error correction, and QAOA.