Protocol for secure quantum machine learning at a distant place

Abstract

The application of machine learning to quantum information processing has recently attracted keen interest, particularly for the optimization of control parameters in quantum tasks without any pre-programmed knowledge. By adapting the machine learning technique, we present a novel protocol in which an arbitrarily initialized device at a learner's location is taught by a provider located at a distant place. The protocol is designed such that any external learner who attempts to participate in or disrupt the learning process can be prohibited or noticed. We numerically demonstrate that our protocol works faithfully for single-qubit operation devices. A trade-off between the inaccuracy and the learning time is also analyzed.

6 pages, 6 figures (to be published in Quantum Information Processing)

References in corpus (4)

• Device-independent security of quantum cryptography against collective attacks
• Linear optics quantum Toffoli and Fredkin gates
• Adaptive quantum computation in changing environments using projective simulation
• Implementation of controlled SWAP gates for quantum fingerprinting and photonic quantum computation

Cited by in corpus (4)

• Learning unknown pure quantum states
• Photonic kernel machine learning for ultrafast spectral analysis
• Quantum secure learning with classical samples
• Ensuring superior learning outcomes and data security for authorized learner