Improving qubit mapping for complex dynamic quantum circuits
Mapping Dynamic, Hierarchical Quantum Circuits
Programming Languages
Summary
Mapping qubits effectively is important to run quantum programs efficiently, especially when circuits are complex and change based on the data they process. The authors noticed that most existing methods only work well for simple, flat circuits and cannot handle circuits with layers or changing paths. They designed a new approach that breaks down these complicated circuits into smaller parts and uses special techniques to keep everything in sync as the program runs. Their method reduces the number of extra steps needed and improves overall performance and reliability on various large quantum processors.
Qubit mappingDynamic circuitsControl-flowQuantum compilationSWAP gatesCircuit depthCircuit latencyError rateHierarchical circuitsQuantum Processing Unit (QPU)
Authors
Marouane Benbetka, Merwan Bekkar, Bokyeong Yoon, Riyadh Baghdadi, Martin Kong
Abstract
Qubit mapping is a critical pass in quantum compilation. Despite various advances, dynamic circuits, those exhibiting data dependent control-flow, often resulting from qubit measurements, are not yet supported by the vast majority of available qubit mappers. The crucial limitation to overcome is the dependence on flat, one-dimensional representations of circuits. Further, qubit mappers currently lack compiler abstractions that capture the hierarchical nature of circuits, hindering the qubit mapping process. In this paper, 1 we introduce a new qubit mapping method and analyses to tackle hierarchical dynamic circuits. Our novelty resides in four key aspects: modeling (statically) sub-circuits in disjoint control-flow paths, introducing a novel Qubit Reconciliation pass to maintain consistency between sub-circuit and control-flow boundaries, a loop-entry remapping pass, and a refined cost function enhanced for SWAP count, circuit depth, circuit latency and error. We demonstrate the efficiency of our approach on a wide range of dynamic circuits on two monolithic Quantum Processing Units of 127 and 156 qubits, and on chiplet hexagon-based QPUs. On monolithic QPUs, our qubit mapper improves the SWAP count by up to 52%, depth by up to 18%, latency by up to 18.6%, and error by up to 40%. On chiplet architectures, we achieve improvements of up to 36% on SWAP count, 8.7% on depth, 15% on latency, and 15% of error.