Dynamic Resource Allocation with Quantum Error Detection

Quantum processing units (QPUs) are highly heterogeneous in terms of physical qubit performance. To add even more complexity, drift in quantum noise landscapes has been well-documented. This makes resource allocation a challenging problem whenever a quantum program must be mapped to hardware. As a solution, we propose a novel resource allocation framework that applies Pauli checks. Pauli checks have demonstrated their efficacy at error mitigation in prior work, and in this paper, we highlight their potential to infer the noise characteristics of a quantum system. Circuits with embedded Pauli checks can be executed on different regions of qubits, and the syndrome data created by error-detecting Pauli checks can be leveraged to guide quantum program outcomes toward regions that produce higher-fidelity final distributions. Using noisy simulation and a real QPU testbed, we show that dynamic quantum resource allocation with Pauli checks can outperform state-of-art mapping techniques, such as those that are noise-aware. Further, when applied toward the Quantum Approximate Optimization Algorithm, techniques guided by Pauli checks demonstrate the ability to increase circuit fidelity 11% on average, and up to 33%.