Impact of the form of weighted networks on the quantum extreme reservoir computation
arXiv:2211.07841 · doi:10.1103/PhysRevA.108.042609
Abstract
The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically-driven system Hamiltonian dynamics was employed as the quantum feature map. In this work we capture how the quantum feature map is generated as the number of time-steps of the dynamics increases by a method to characterize unitary matrices in the form of weighted networks. Furthermore, to identify the key properties of the feature map that has sufficiently grown, we evaluate it with various weighted network models that could be used for the quantum reservoir in image classification situations. At last, we show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly-optimal performance while removing the necessity of programming of the quantum processor gate by gate.
arXiv comment9 pages, 5 figures
References in corpus (29)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Supervised learning with quantum enhanced feature spaces
- Quantum computational advantage using photons
- Barren plateaus in quantum neural network training landscapes
- Noisy intermediate-scale quantum (NISQ) algorithms
- Quantum machine learning in feature Hilbert spaces
- Characterizing Quantum Supremacy in Near-Term Devices
- Observation of a Discrete Time Crystal
- Benchmarking an 11-qubit quantum computer
- A blueprint for demonstrating quantum supremacy with superconducting qubits
- The Bloch Vector for N-Level Systems
- Training variational quantum algorithms is NP-hard
- Observation of Entangled States of a Fully Controlled 20-Qubit System
- Random Quantum Circuits are Approximate 2-designs
- Harnessing disordered quantum dynamics for machine learning
- Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
- An Artificial Neuron Implemented on an Actual Quantum Processor
- Graphical calculus for Gaussian pure states
- Quantum reservoir processing
- Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
- Dynamical phase transitions in quantum reservoir computing
- Convergence Conditions for Random Quantum Circuits
- Generalized coherent states for SU(n) systems
- Edge of chaos and avalanches in neural networks with heavy-tailed synaptic weight distribution
- Quantifying Complexity in Quantum Phase Transitions via Mutual Information Complex Networks
- Simulating complex quantum networks with time crystals
- Parameters of Pseudo-Random Quantum Circuits
- Quantum reservoir computation utilising scale-free networks
- Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features