Polyadic Quantum Classifier
- URL: http://arxiv.org/abs/2007.14044v1
- Date: Tue, 28 Jul 2020 08:00:12 GMT
- Title: Polyadic Quantum Classifier
- Authors: William Cappelletti, Rebecca Erbanni and Joaqu\'in Keller
- Abstract summary: We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures.
A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint.
We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce here a supervised quantum machine learning algorithm for
multi-class classification on NISQ architectures. A parametric quantum circuit
is trained to output a specific bit string corresponding to the class of the
input datapoint. We train and test it on an IBMq 5-qubit quantum computer and
the algorithm shows good accuracy --compared to a classical machine learning
model-- for ternary classification of the Iris dataset and an extension of the
XOR problem. Furthermore, we evaluate with simulations how the algorithm fares
for a binary and a quaternary classification on resp. a known binary dataset
and a synthetic dataset.
Related papers
- Hybrid Quantum-Classical Machine Learning with String Diagrams [49.1574468325115]
This paper develops a formal framework for describing hybrid algorithms in terms of string diagrams.
A notable feature of our string diagrams is the use of functor boxes, which correspond to a quantum-classical interfaces.
arXiv Detail & Related papers (2024-07-04T06:37:16Z) - Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
We show that a quantum processor can correctly solve the basic classification task considered.
With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
arXiv Detail & Related papers (2024-06-17T18:20:51Z) - 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) - Analog quantum variational embedding classifier [8.445680783099196]
We propose a gate-based variational embedding classifier based on an analog quantum computer.
We find the performance of our classifier can be increased by increasing the number of qubits until the performance saturates and fluctuates.
Our algorithm presents the possibility of using current quantum annealers for solving practical machine-learning problems.
arXiv Detail & Related papers (2022-11-04T20:58:48Z) - Variational Quantum Approximate Support Vector Machine With Inference
Transfer [0.8057006406834467]
A kernel-based quantum machine learning technique for hyperlinear classification of complex data is presented.
A support vector machine can be realized inherently and explicitly on quantum circuits.
The accuracy of iris data classification reached 98.8%.
arXiv Detail & Related papers (2022-06-29T09:56:59Z) - When BERT Meets Quantum Temporal Convolution Learning for Text
Classification in Heterogeneous Computing [75.75419308975746]
This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification.
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
arXiv Detail & Related papers (2022-02-17T09:55:21Z) - Investigation of Quantum Support Vector Machine for Classification in
NISQ era [0.0]
We investigate quantum support vector machine (QSVM) algorithm and its circuit version on present quantum computers.
We compute the efficiency of the QSVM circuit implementation method by encoding training and testing data sample in quantum circuits.
We highlight the technical difficulties one would face while applying the QSVM algorithm on current NISQ era devices.
arXiv Detail & Related papers (2021-12-13T18:59:39Z) - Binary classifiers for noisy datasets: a comparative study of existing
quantum machine learning frameworks and some new approaches [0.0]
We apply Quantum Machine Learning frameworks to improve binary classification.
noisy datasets are in financial datasets.
New models exhibit better learning characteristics to asymmetrical noise in the dataset.
arXiv Detail & Related papers (2021-11-05T10:29:05Z) - Quantum Machine Learning with SQUID [64.53556573827525]
We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems.
We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
arXiv Detail & Related papers (2021-04-30T21:34:11Z) - A quantum binary classifier based on cosine similarity [0.0]
The proposed quantum algorithm evaluates the classifier on a set of data vectors with time complexity that is logarithmic in the product of the set cardinality and the dimension of the vectors.
We present a simple implementation of the considered classifier on the IBM quantum processor ibmq_16_melbourne.
arXiv Detail & Related papers (2021-04-07T07:55:49Z) - Facial Expression Recognition on a Quantum Computer [68.8204255655161]
We show a possible solution to facial expression recognition using a quantum machine learning approach.
We define a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states.
arXiv Detail & Related papers (2021-02-09T13:48:00Z)
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.