Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor
arXiv:2212.10742 · doi:10.1038/s41534-023-00779-5
Abstract
Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits. In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. These neurons constitute elementary building blocks that can be arranged in a variety of layouts to carry out deep learning tasks quantum coherently. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions by optimization of network activation parameters within the supervised-learning paradigm. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.
arXiv comment24 pages, 22 figures, 3 tables
References in corpus (28)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- Quantum algorithm for solving linear systems of equations
- Variational Quantum Algorithms
- Supervised learning with quantum enhanced feature spaces
- Barren plateaus in quantum neural network training landscapes
- Quantum support vector machine for big data classification
- Quantum Convolutional Neural Networks
- The power of quantum neural networks
- Quantum advantage in learning from experiments
- Quantum autoencoders for efficient compression of quantum data
- Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
- A simple all-microwave entangling gate for fixed-frequency superconducting qubits
- Exponential suppression of bit or phase flip errors with repetitive error correction
- Continuous-variable quantum neural networks
- Absence of Barren Plateaus in Quantum Convolutional Neural Networks
- Quantum generalisation of feedforward neural networks
- An Artificial Neuron Implemented on an Actual Quantum Processor
- Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
- Scalable quantum circuit and control for a superconducting surface code
- Quantum Hopfield neural network
- A fast, low-leakage, high-fidelity two-qubit gate for a programmable superconducting quantum computer
- Simulating a perceptron on a quantum computer
- Efficient synthesis of universal Repeat-Until-Success circuits
- Entanglement Stabilization using Parity Detection and Real-Time Feedback in Superconducting Circuits
- Building quantum neural networks based on swap test
- Floating Point Representations in Quantum Circuit Synthesis
- Repeat-Until-Success circuits with fixed-point oblivious amplitude amplification