# Quantum state discrimination using noisy quantum neural networks

@article{Patterson2021QuantumSD, title={Quantum state discrimination using noisy quantum neural networks}, author={Andrew D. Patterson and Hongxiang Chen and Leonard Wossnig and Simone Severini and Dan E. Browne and Ivan Rungger}, journal={Physical Review Research}, year={2021} }

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…

## 9 Citations

Experimental multi-state quantum discrimination through optical networks

- Computer ScienceQuantum Science and Technology
- 2022

Two discrimination schemes are experimentally implemented in a minimum-error scenario based on a receiver featured by a network structure and a dynamical processing of information, achieving binary optimal discrimination and a novel approach to multi-state quantum discrimination.

Hardware-efficient entangled measurements for variational quantum algorithms

- Physics
- 2022

Variational algorithms have received significant attention in recent years due to their potential to solve practical problems in noisy intermediate-scale quantum (NISQ) devices. A fundamental step of…

Systematic Literature Review: Quantum Machine Learning and its applications

- Computer Science, Physics
- 2022

A review of the literature published between 2017 and 2021 to identify, analyze and classify the different types of algorithms used in quantum machine learning and their applications and shows their implementation using computational quantum circuits or ansatzs.

Toward Physically Realizable Quantum Neural Networks

- Computer ScienceArXiv
- 2022

A new model for QNNs that relies on band-limited Fourier expansions of transfer functions of quantum perceptrons (QPs) to design scalable training procedures and converges to the true minima in expectation, even in the presence of non-determinism due to quantum measurement.

Experimental multi-state quantum discrimination through a Quantum network

- Computer Science
- 2021

Two discrimination schemes in a minimum-error scenario based on a receiver featured by a network structure and a dynamical processing of information are implemented, achieving binary optimal discrimination and a novel approach to multi-state quantum discrimination.

Fast suppression of classification error in variational quantum circuits

- Physics
- 2021

Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and depth, is not…

VSQL: Variational Shadow Quantum Learning for Classification

- Computer ScienceAAAI
- 2021

This paper utilizes the classical shadows of quantum data to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task and shows that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training.

Optimizing parametrized quantum circuits via noise-induced breaking of symmetries

- Computer Science, PhysicsArXiv
- 2020

An optimization method called Symmetry-based Minima Hopping (SYMH), which exploits the underlying symmetries in PQCs to hop between local minima in the cost landscape and shows that SYMH improves the overall optimizer performance.

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