ResQNets: A Residual Approach for Mitigating Barren Plateaus in Quantum Neural Networks

Abstract

The barren plateau problem in quantum neural networks (QNNs) is a significant challenge that hinders the practical success of QNNs. In this paper, we introduce residual quantum neural networks (ResQNets) as a solution to address this problem. ResQNets are inspired by classical residual neural networks and involve splitting the conventional QNN architecture into multiple quantum nodes, each containing its own parameterized quantum circuit, and introducing residual connections between these nodes. Our study demonstrates the efficacy of ResQNets by comparing their performance with that of conventional QNNs and plain quantum neural networks (PlainQNets) through multiple training experiments and analyzing the cost function landscapes. Our results show that the incorporation of residual connections results in improved training performance. Therefore, we conclude that ResQNets offer a promising solution to overcome the barren plateau problem in QNNs and provide a potential direction for future research in the field of quantum machine learning.

References in corpus (41)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• Supervised learning with quantum enhanced feature spaces
• Barren plateaus in quantum neural network training landscapes
• Quantum support vector machine for big data classification
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Quantum principal component analysis
• An introduction to quantum machine learning
• Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
• Evaluating analytic gradients on quantum hardware
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• Efficient Learning for Deep Quantum Neural Networks
• Quantum optimization using variational algorithms on near-term quantum devices
• A rigorous and robust quantum speed-up in supervised machine learning
• Quantum advantage in learning from experiments
• Quantum algorithms for supervised and unsupervised machine learning
• Quantum autoencoders for efficient compression of quantum data
• Generalization in quantum machine learning from few training data
• Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
• Transfer learning in hybrid classical-quantum neural networks
• Continuous-variable quantum neural networks
• Quantum Boltzmann Machine
• Quantum Error Correction: An Introductory Guide
• Robust data encodings for quantum classifiers
• Layerwise learning for quantum neural networks
• The Expressive Power of Parameterized Quantum Circuits
• Quantum generalisation of feedforward neural networks
• Fault-tolerant quantum error detection
• Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
• Equivalence of quantum barren plateaus to cost concentration and narrow gorges
• Quantum autoencoders to denoise quantum data
• Variational Quantum Boltzmann Machines
• Advances in Quantum Reinforcement Learning
• Quantum Natural Language Processing on Near-Term Quantum Computers
• Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations
• Completely Quantum Neural Networks
• Entangled Datasets for Quantum Machine Learning
• Quantum-enhanced barcode decoding and pattern recognition
• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs

Cited by in corpus (11)

• FedQNN: Federated Learning using Quantum Neural Networks
• Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies
• Neural network encoded variational quantum algorithms
• Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy
• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
• Non-Unitary Quantum Machine Learning
• Physical Realization of Measurement Based Quantum Computation
• Investigating the Effect of Noise on the Training Performance of Hybrid Quantum Neural Networks
• Framework for Learning and Control in the Classical and Quantum Domains
• ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks
• HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era