VQE-generated Quantum Circuit Dataset for Machine Learning
- URL: http://arxiv.org/abs/2302.09751v2
- Date: Thu, 1 Jun 2023 06:49:00 GMT
- Title: VQE-generated Quantum Circuit Dataset for Machine Learning
- Authors: Akimoto Nakayama, Kosuke Mitarai, Leonardo Placidi, Takanori Sugimoto,
Keisuke Fujii
- Abstract summary: We provide a dataset of quantum circuits optimized by variational quantum eigensolver.
We show that this dataset can be easily learned using quantum methods.
- Score: 0.5658123802733283
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum machine learning has the potential to computationally outperform
classical machine learning, but it is not yet clear whether it will actually be
valuable for practical problems. While some artificial scenarios have shown
that certain quantum machine learning techniques may be advantageous compared
to their classical counterpart, it is unlikely that quantum machine learning
will outclass traditional methods on popular classical datasets such as MNIST.
In contrast, dealing with quantum data, such as quantum states or circuits, may
be the task where we can benefit from quantum methods. Therefore, it is
important to develop practically meaningful quantum datasets for which we
expect quantum methods to be superior. In this paper, we propose a machine
learning task that is likely to soon arise in the real world: clustering and
classification of quantum circuits. We provide a dataset of quantum circuits
optimized by the variational quantum eigensolver. We utilized six common types
of Hamiltonians in condensed matter physics, with a range of 4 to 16 qubits,
and applied ten different ans\"{a}tze with varying depths (ranging from 3 to
32) to generate a quantum circuit dataset of six distinct classes, each
containing 300 samples. We show that this dataset can be easily learned using
quantum methods. In particular, we demonstrate a successful classification of
our dataset using real 4-qubit devices available through IBMQ. By providing a
setting and an elementary dataset where quantum machine learning is expected to
be beneficial, we hope to encourage and ease the advancement of the field.
Related papers
- The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for
Quantum Computers in the NISQ era [2.652805765181667]
MNISQ consists of 4,950,000 data points organized in 9 subdatasets.
We deliver a dataset in a dual form: in quantum form, as circuits, and in classical form, as quantum circuit descriptions.
In the quantum endeavor, we test our circuit dataset with Quantum Kernel methods, and we show excellent results up to $97%$ accuracy.
arXiv Detail & Related papers (2023-06-29T02:04:14Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
We focus on the case of learning with a single qubit, using data re-uploading techniques.
We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
arXiv Detail & Related papers (2022-11-23T18:25:32Z) - Entangled Datasets for Quantum Machine Learning [0.0]
We argue that one should instead employ quantum datasets composed of quantum states.
We show how a quantum neural network can be trained to generate the states in the NTangled dataset.
We also consider an alternative entanglement-based dataset, which is scalable and is composed of states prepared by quantum circuits.
arXiv Detail & Related papers (2021-09-08T02:20:13Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - VSQL: Variational Shadow Quantum Learning for Classification [6.90132007891849]
We propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning.
We first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task.
We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training.
arXiv Detail & Related papers (2020-12-15T13:51:01Z) - Power of data in quantum machine learning [2.1012068875084964]
We show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data.
We propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.
arXiv Detail & Related papers (2020-11-03T19:00:01Z)
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.