Evaluating the Convergence Limit of Quantum Neural Tangent Kernel
- URL: http://arxiv.org/abs/2312.02451v1
- Date: Tue, 5 Dec 2023 03:04:26 GMT
- Title: Evaluating the Convergence Limit of Quantum Neural Tangent Kernel
- Authors: Trong Duong
- Abstract summary: We construct the kernel for two models, Quantun Ensemble and Quantum Neural Network, and show the convergence of these models in the limit of infinitely many qubits.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum variational algorithms have been one of major applications of quantum
computing with current quantum devices. There are recent attempts to establish
the foundation for these algorithms. A possible approach is to characterize the
training dynamics with quantum neural tangent kernel. In this work, we
construct the kernel for two models, Quantun Ensemble and Quantum Neural
Network, and show the convergence of these models in the limit of infinitely
many qubits. We also show applications of the kernel limit in regression tasks.
Related papers
- Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization [0.6512657417859998]
We introduce a quantum diagonalization algorithm which combines two key ideas on quantum subspaces.
We prove that our algorithm converges under working assumptions of Krylov quantum diagonalization and sparseness of ground state.
We then show numerical investigations of lattice Hamiltonians, which indicate that our method can outperform existing Krylov quantum diagonalization in presence of shot noise.
arXiv Detail & Related papers (2025-01-16T17:56:19Z) - In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods [30.565491081930997]
We develop an approach for estimating desired precision of kernel values, which is translated into the number of circuit runs.
We stress that quantum kernel methods should not only be considered from the machine learning performance perspective, but also from the context of the resource consumption.
arXiv Detail & Related papers (2024-07-22T16:29:35Z) - Quantum computing topological invariants of two-dimensional quantum matter [0.0]
We present two quantum circuits for calculating Chern numbers of two-dimensional quantum matter on quantum computers.
First algorithm uses many qubits, and we analyze it using a tensor-network simulator of quantum circuits.
Second circuit uses fewer qubits, and we implement it experimentally on a quantum computer based on superconducting qubits.
arXiv Detail & Related papers (2024-04-09T06:22:50Z) - Scalable Quantum Algorithms for Noisy Quantum Computers [0.0]
This thesis develops two main techniques to reduce the quantum computational resource requirements.
The aim is to scale up application sizes on current quantum processors.
While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning.
arXiv Detail & Related papers (2024-03-01T19:36:35Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - Power Characterization of Noisy Quantum Kernels [52.47151453259434]
We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small.
We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
arXiv Detail & Related papers (2024-01-31T01:02:16Z) - Neural quantum kernels: training quantum kernels with quantum neural networks [0.0]
We propose using the training of a quantum neural network to construct neural quantum kernels.
We present several strategies for constructing neural quantum kernels and propose a scalable method to train an $n$-qubit data re-uploading quantum neural network (QNN)
arXiv Detail & Related papers (2024-01-09T16:08:32Z) - The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for
Deep Quantum Machine Learning [52.77024349608834]
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing.
Key issue is how to address the inherent non-linearity of classical deep learning.
We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
arXiv Detail & Related papers (2022-12-22T16:06:24Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Quantum Neural Architecture Search with Quantum Circuits Metric and
Bayesian Optimization [2.20200533591633]
We propose a new quantum gates distance that characterizes the gates' action over every quantum state.
Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
arXiv Detail & Related papers (2022-06-28T16:23:24Z) - Quantum Alphatron: quantum advantage for learning with kernels and noise [2.94944680995069]
We provide quantum versions of the Alphatron in the fault-tolerant setting.
We discuss the quantum advantage in the context of learning of two-layer neural networks.
arXiv Detail & Related papers (2021-08-26T09:36:20Z) - The Hintons in your Neural Network: a Quantum Field Theory View of Deep
Learning [84.33745072274942]
We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles.
On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
arXiv Detail & Related papers (2021-03-08T17:24:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.