Variational Quantum-Neural Hybrid Error Mitigation
- URL: http://arxiv.org/abs/2112.10380v2
- Date: Fri, 25 Aug 2023 04:49:46 GMT
- Title: Variational Quantum-Neural Hybrid Error Mitigation
- Authors: Shi-Xin Zhang, Zhou-Quan Wan, Chang-Yu Hsieh, Hong Yao, Shengyu Zhang
- Abstract summary: Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers.
We show how variational quantum-neural hybrid eigensolver (VQNHE) algorithm is inherently noise resilient with a unique QEM capacity.
- Score: 6.555128824546528
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum error mitigation (QEM) is crucial for obtaining reliable results on
quantum computers by suppressing quantum noise with moderate resources. It is a
key factor for successful and practical quantum algorithm implementations in
the noisy intermediate scale quantum (NISQ) era. Since quantum-classical hybrid
algorithms can be executed with moderate and noisy quantum resources, combining
QEM with quantum-classical hybrid schemes is one of the most promising
directions toward practical quantum advantages. In this work, we show how the
variational quantum-neural hybrid eigensolver (VQNHE) algorithm, which
seamlessly combines the expressive power of a parameterized quantum circuit
with a neural network, is inherently noise resilient with a unique QEM
capacity, which is absent in vanilla variational quantum eigensolvers (VQE). We
carefully analyze and elucidate the asymptotic scaling of this unique QEM
capacity in VQNHE from both theoretical and experimental perspectives. Finally,
we propose a variational basis transformation for the Hamiltonian to be
measured under the VQNHE framework, yielding a powerful tri-optimization setup,
dubbed as VQNHE++. VQNHE++ can further enhance the quantum-neural hybrid
expressive power and error mitigation capacity.
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