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As we are entering the era of constructing practical quantum computers,suppressing the inevitable noise to accomplish reliable computational taskswill be the primary goal. Leakage noise, as the amplitude population leakingoutside the qubit subspace, is a particularly damaging source of error thaterror correction approaches cannot handle. However, the impact of this noise onthe performance of variational quantum algorithms (VQAs), a type of near-termquantum algorithms that is naturally resistant to a variety of noises, is yetunknown. Here, {we consider a typical scenario with the widely usedhardware-efficient ansatz and the emergence of leakage in two-qubit gates},observing that leakage noise generally reduces the expressive power of VQAs.Furthermore, we benchmark the influence of leakage noise on VQAs in real-worldlearning tasks. Results show that, both for data fitting and dataclassification, leakage noise generally has a negative impact on the trainingprocess and final outcomes. Our findings give strong evidence that VQAs arevulnerable to leakage noise in most cases, implying that leakage noise must beeffectively suppressed in order to achieve practical quantum computingapplications, whether for near-term quantum algorithms and long-termerror-correcting quantum computing.

Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise