Distributed Quantum Neural Networks via Partitioned Features Encoding
- URL: http://arxiv.org/abs/2312.13650v2
- Date: Mon, 8 Jan 2024 15:30:41 GMT
- Title: Distributed Quantum Neural Networks via Partitioned Features Encoding
- Authors: Yoshiaki Kawase
- Abstract summary: Quantum neural networks are expected to be a promising application in near-term quantum computing.
We propose to make a prediction by approximating outputs of a large circuit using multiple small circuits.
Our proposed method not only achieved highly accurate predictions for a large dataset but also reduced the hardware requirements for each quantum neural network.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum neural networks are expected to be a promising application in
near-term quantum computing, but face challenges such as vanishing gradients
during optimization and limited expressibility by a limited number of qubits
and shallow circuits. To mitigate these challenges, an approach using
distributed quantum neural networks has been proposed to make a prediction by
approximating outputs of a large circuit using multiple small circuits.
However, the approximation of a large circuit requires an exponential number of
small circuit evaluations. Here, we instead propose to distribute partitioned
features over multiple small quantum neural networks and use the ensemble of
their expectation values to generate predictions. To verify our distributed
approach, we demonstrate ten class classification of the Semeion and MNIST
handwritten digit datasets. The results of the Semeion dataset imply that while
our distributed approach may outperform a single quantum neural network in
classification performance, excessive partitioning reduces performance.
Nevertheless, for the MNIST dataset, we succeeded in ten class classification
with exceeding 96\% accuracy. Our proposed method not only achieved highly
accurate predictions for a large dataset but also reduced the hardware
requirements for each quantum neural network compared to a large single quantum
neural network. Our results highlight distributed quantum neural networks as a
promising direction for practical quantum machine learning algorithms
compatible with near-term quantum devices. We hope that our approach is useful
for exploring quantum machine learning applications.
Related papers
- 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) - Enhancing the expressivity of quantum neural networks with residual
connections [0.0]
We propose a quantum circuit-based algorithm to implement quantum residual neural networks (QResNets)
Our work lays the foundation for a complete quantum implementation of the classical residual neural networks.
arXiv Detail & Related papers (2024-01-29T04:00:51Z) - An Invitation to Distributed Quantum Neural Networks [0.0]
We review the current state of the art in distributed quantum neural networks.
We find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models.
arXiv Detail & Related papers (2022-11-14T00:27:01Z) - 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) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Cluster-Promoting Quantization with Bit-Drop for Minimizing Network
Quantization Loss [61.26793005355441]
Cluster-Promoting Quantization (CPQ) finds the optimal quantization grids for neural networks.
DropBits is a new bit-drop technique that revises the standard dropout regularization to randomly drop bits instead of neurons.
We experimentally validate our method on various benchmark datasets and network architectures.
arXiv Detail & Related papers (2021-09-05T15:15:07Z) - A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems.
We numerically demonstrate that the MNIST image dataset satisfies such conditions.
We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
arXiv Detail & Related papers (2021-07-19T23:41:03Z) - Quantum neural networks with multi-qubit potentials [0.0]
We show that the presence of multi-qubit potentials in the quantum perceptrons enables more efficient information processing tasks.
This simplification in the network architecture paves the way to address the connectivity challenge to scale up a quantum neural network.
arXiv Detail & Related papers (2021-05-06T15:30:06Z) - Entangling Quantum Generative Adversarial Networks [53.25397072813582]
We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN)
We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
arXiv Detail & Related papers (2021-04-30T20:38:41Z) - Variational learning for quantum artificial neural networks [0.0]
We first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors.
We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols.
While keeping full compatibility with the overall memory-efficient feed-forward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons.
arXiv Detail & Related papers (2021-03-03T16:10:15Z) - Probing Criticality in Quantum Spin Chains with Neural Networks [0.0]
We show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases.
Our results extend to a wide class of interacting quantum many-body systems and illustrate the wide applicability of neural networks to many-body quantum physics.
arXiv Detail & Related papers (2020-05-05T12:34:50Z)
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.