Quantum State Discrimination Using Noisy Quantum Neural Networks

Abstract

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, applicable on near-term quantum devices as it fulfils the above criteria. We find that when simulating gradient calculation on a noisy device, a large number of parameters is disadvantageous. By introducing a new smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We also show that networks trained at higher noise levels can still converge to useful parameters. Our findings show that noisy quantum computers can be used in applications for state discrimination and for classifiers of the output of quantum generative adversarial networks.

arXiv comment8 pages, 9 figures

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Cited by in corpus (13)

• Non-trivial symmetries in quantum landscapes and their resilience to quantum noise
• Discriminating mixed qubit states with collective measurements
• Experimental quantum state discrimination using the optimal fixed rate of inconclusive outcomes strategy
• Variational quantum state discriminator for supervised machine learning
• Quantum-machine-learning channel discrimination
• Quantum Sensor Network Algorithms for Transmitter Localization
• Energy-dependent barren plateau in bosonic variational quantum circuits
• Hardware-efficient entangled measurements for variational quantum algorithms
• State Classification via a Random-Walk-Based Quantum Neural Network
• Training quantum measurement devices to discriminate unknown non-orthogonal quantum states
• Photonic variational quantum eigensolver for NISQ-compatible quantum technology
• Effects of underlying topology on quantum state discrimination
• Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision