Generic detection-based error-mitigation using quantum autoencoders

Abstract

Efficient error-mitigation techniques demanding minimal resources is key to quantum information processing. We propose a generic protocol to mitigate quantum errors using detection-based quantum autoencoders. In our protocol, the quantum data are compressed into a latent subspace while leaving errors outside, the latter of which is then removed by a measurement and post-selection. Compared to previously developed methods, our protocol on the one hand requires no extra qubits, and on the other hand has a near-optimal denoising power, in which under reasonable requirements all errors detected outside of the latent subspace can be removed, while those inside the subspace cannot be removed by any means. Our detection-based quantum autoencoders are therefore particularly useful for near-term quantum devices in which controllable qubits are limited while noise reduction is important.

arXiv comment6+13 pages, 4+10 figures

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Cited by in corpus (8)

• Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases
• Quantum Error Correction with Quantum Autoencoders
• Variational quantum one-class classifier
• On exploring the potential of quantum auto-encoder for learning quantum systems
• Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases
• Divide-and-conquer verification method for noisy intermediate-scale quantum computation
• Quantum autoencoders using mixed reference states
• Rigorous noise reduction with quantum autoencoders