HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
arxiv(2024)
摘要
This paper delves into the intricate dynamics of quantum noise and its
influence on the onset and mitigation of barren plateaus (BPs) - a phenomenon
that critically impedes the scalability of QNNs. We find that BPs appear
earlier in noisy quantum environments compared to ideal, noise-free
conditions.However, strategic selection of qubit measurement observables can
effectively tackle this issue. To this end, we examine a variety of
observables, such as PauliZ,PauliX, PauliY, and a specially designed arbitrary
Hermitian observable, tailored to the requirements of the cost function and the
desired outputs of quantum circuits. Our analysis encompasses both global and
local cost function definitions, with the former involving measurements across
all qubits and the latter focusing on single-qubit measurements within the QNN
framework. Our findings indicate that in a global cost function scenario,
PauliX and PauliY observables lead to flatter optimization landscapes,
signaling BPs with increasing qubits, especially in noisy conditions.
Conversely, the PauliZ observable maintains trainability up to 8 qubits but
encounters BPs at 10 qubits. Notably, the arbitrary Hermitian observable, when
used with a global cost function, shows a unique advantage as it benefits from
noise, facilitating effective training up to 10 qubits. Furthermore, with a
local cost function, out of the three conventional observables (PauliX, PauliY
and PauliZ), PauliZ is more effective, sustaining training efficiency under
noisy conditions for up to 10 qubits, while PauliX and PauliY do not show
similar benefits and remain susceptible to BPs. Our results highlight the
importance of noise consideration in QNN training and propose a strategic
approach to observable selection to improve QNN performance in noisy quantum
computing environments thus contributing to the advancement of quantum machine
learning research.
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