Neural network encoded variational quantum algorithms
- URL: http://arxiv.org/abs/2308.01068v1
- Date: Wed, 2 Aug 2023 10:32:57 GMT
- Title: Neural network encoded variational quantum algorithms
- Authors: Jiaqi Miao, Chang-Yu Hsieh and Shi-Xin Zhang
- Abstract summary: We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs)
NN-VQA feeds input (such as parameters of a Hamiltonian) from a given problem to a neural network and uses its outputs to parameterize an ansatz circuit for the standard VQA.
We present results on NN-variational quantum eigensolver (VQE) for solving the ground state of parameterized XXZ spin models.
- Score: 0.241710192205034
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a general framework called neural network (NN) encoded
variational quantum algorithms (VQAs), or NN-VQA for short, to address the
challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ)
computers. Specifically, NN-VQA feeds input (such as parameters of a
Hamiltonian) from a given problem to a neural network and uses its outputs to
parameterize an ansatz circuit for the standard VQA. Combining the strengths of
NN and parameterized quantum circuits, NN-VQA can dramatically accelerate the
training process of VQAs and handle a broad family of related problems with
varying input parameters with the pre-trained NN. To concretely illustrate the
merits of NN-VQA, we present results on NN-variational quantum eigensolver
(VQE) for solving the ground state of parameterized XXZ spin models. Our
results demonstrate that NN-VQE is able to estimate the ground-state energies
of parameterized Hamiltonians with high precision without fine-tuning, and
significantly reduce the overall training cost to estimate ground-state
properties across the phases of XXZ Hamiltonian. We also employ an
active-learning strategy to further increase the training efficiency while
maintaining prediction accuracy. These encouraging results demonstrate that
NN-VQAs offer a new hybrid quantum-classical paradigm to utilize NISQ resources
for solving more realistic and challenging computational problems.
Related papers
- Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation
via Neural Networks [0.4779196219827508]
Variational Quantum Eigensolver (VQE) is a promising algorithm for solving complex quantum problems.
The ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes.
This research introduces a novel approach by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations.
arXiv Detail & Related papers (2024-03-10T15:35:41Z) - A joint optimization approach of parameterized quantum circuits with a
tensor network [0.0]
Current intermediate-scale quantum (NISQ) devices remain limited in their capabilities.
We propose the use of parameterized Networks (TNs) to attempt an improved performance of the Variational Quantum Eigensolver (VQE) algorithm.
arXiv Detail & Related papers (2024-02-19T12:53:52Z) - ResQNets: A Residual Approach for Mitigating Barren Plateaus in Quantum
Neural Networks [0.0]
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.
arXiv Detail & Related papers (2023-05-05T13:33:43Z) - 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) - Symmetric Pruning in Quantum Neural Networks [111.438286016951]
Quantum neural networks (QNNs) exert the power of modern quantum machines.
QNNs with handcraft symmetric ansatzes generally experience better trainability than those with asymmetric ansatzes.
We propose the effective quantum neural tangent kernel (EQNTK) to quantify the convergence of QNNs towards the global optima.
arXiv Detail & Related papers (2022-08-30T08:17:55Z) - Theoretical Error Performance Analysis for Variational Quantum Circuit
Based Functional Regression [83.79664725059877]
In this work, we put forth an end-to-end quantum neural network, namely, TTN-VQC, for dimensionality reduction and functional regression.
We also characterize the optimization properties of TTN-VQC by leveraging the Polyak-Lojasiewicz (PL) condition.
arXiv Detail & Related papers (2022-06-08T06:54:07Z) - QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks [71.14713348443465]
We introduce a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC)
QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement.
Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.
arXiv Detail & Related papers (2021-10-06T14:44:51Z) - Exponentially Many Local Minima in Quantum Neural Networks [9.442139459221785]
Quantum Neural Networks (QNNs) are important quantum applications because of their similar promises as classical neural networks.
We conduct a quantitative investigation on the landscape of loss functions of QNNs and identify a class of simple yet extremely hard QNN instances for training.
We empirically confirm that our constructions can indeed be hard instances in practice with typical gradient-based circuits.
arXiv Detail & Related papers (2021-10-06T03:23:44Z) - Toward Trainability of Quantum Neural Networks [87.04438831673063]
Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up.
Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number.
We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
arXiv Detail & Related papers (2020-11-12T08:32:04Z) - On the learnability of quantum neural networks [132.1981461292324]
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme.
We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
arXiv Detail & Related papers (2020-07-24T06:34:34Z)
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.