Hybrid Quantum Variational Autoencoders for Representation Learning

One of our recent papers introduces a novel hybrid quantum machine learning approach to unsupervised representation learning by using a quantum variational circuit that is trainable with traditional gradient descent techniques. Access it here: [ bib | .pdf ]

Much of the work related to quantum machine learning has been popularized in recent years. Some of the most notable efforts involve variational approaches (Cerezo 2021, Khoshaman 2018, Yuan 2019). Researchers have shown that these models are effective in complex tasks that grant further studies and open new doors for applied quantum machine learning research. Another popular approach is to perform kernel learning using a quantum approach (Blank 2020, Schuld 2019, Rebentrost 2014). In this case the kernel-based projection of data \mathbf{x} produces a easible linear mapping to the desired target y as follows:

(1)   \begin{equation*}     y(\mathbf{x})=\operatorname{sign}\left(\sum_{j=1}^{M} \alpha_{j} k\left(\mathbf{x}_{j}, \mathbf{x}\right)+b\right) \end{equation*}

for hyper parameters b,\alpha that need to be provided or learned. This enables the creation of some types of support vector machines whose kernels are calculated such that the data \mathbf{x} is processed in the quantum realm. That is \left|\mathbf{x}_{j}\right\rangle=1 /\left|\mathbf{x}_{j}\right| \sum_{k=1}^{N}\left(\mathbf{x}_{j}\right)_{k}|k\rangle. The work of Schuld et al., expands the theory behind this idea an show that all kernel methods can be quantum machine learning methods. Recently, in 2020, Mari et al., worked on variational models that are hybrid in format. Particularly, the authors focused on transfer learning, i.e., the idea of bringing a pre-trained model (or a piece of it) to be part of another model. In the case of Mari the larger model is a computer vision model, e.g., ResNet, which is part of a variational quantum circuit that performs classification. The work we present here follows a similar idea, but we focus in the autoencoder architecture, rather than a classification model, and we focus on learning representations in comparison between a classic and a variational quantum fine-tuned model.

Evaluating Accuracy and Adversarial Robustness of Quanvolutional Neural Networks

A combination of a quantum circuit and a convolutional neural network (CNN) can have better results over a classic CNN in some cases. In our recent article, we show an example of such a case, using accuracy and adversarial examples as measures of performance and robustness. Check it out: [ bib | pdf ]

Artificial Intelligence Computing at the Quantum Level

A unique feature of a quantum computer in comparison to a classical computer is that the bit (often referred to as qubit can be in one of two states (0 or 1) and possibly a superposition of the two states (a linear combination of 0 and 1) per time. The most common mathematical representation of a qubit is

    \[ |{\psi\rangle} = \alpha|{0\rangle} + \beta|{1\rangle},  \]

which denotes a superposition state, where \alpha, \beta are complex numbers and |{0\rangle}, |{1\rangle} are computational basis states that form an orthonormal basis in this vector space.

To know more about how quantum machine learning takes advantage of this, check our newest article here.

Performance Analysis of Quantum Machine Learning Classifiers

Quantum mechanics has advanced analytical studies beyond the constraints of classical mechanics in recent years. Quantum Machine Learning  (QML) is one of these fields of study. QML currently offers working solutions comparable to (traditional) machine learning, such as classification and prediction tasks utilizing quantum classifiers (q-classifiers).

We investigated a variety of these q-classifiers and reported the outcomes of our research in comparison to their classical counterparts. Paper: [ bib | .pdf ]

International Conference on Emergent and Quantum Technologies (ICEQT’22)

July 25-28, 2022 — Las Vegas, NV

Dear Colleagues,

Quantum computing is an emerging interdisciplinary research area at the intersection of mathematics, physics, and engineering. Quantum computing requires experts, and specialists from STEM areas to assure scientific rigor and to keep up with technological advances.

The main goal of organizing ICEQT’22 is to share knowledge about the recent advancements in the field of QML and build a forum for discussions on this topic for researchers working in this field as well as machine learning researchers, attending The 2022 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE’22), who are interested in applying AI to enhance quantum computing algorithms.

In recent years, we have observed a significant amount of published research papers in the quantum machine learning domain. There is an increasing interest from machine learning researchers to apply AI to the quantum computing domain (and vice versa). Therefore, we invite all contributions in the following areas:

AI for Quantum
* Machine learning for improved quantum algorithm performance
* Machine learning for quantum control
* Machine learning for building better quantum hardware
Quantum technologies and applications
* Quantum computing: models and paradigms
* Fairness/ethics with quantum machine learning
* Quantum algorithms for hyperparameter tuning (Quantum computing for AutoML)
* Theory of Quantum-enhanced Machine Learning
* Quantum Machine Learning Algorithms based on Grover search
* Quantum-enhanced Reinforcement Learning
* Quantum computing, models and paradigms such as Quantum Annealing,
* Quantum Sampling
Quantum computing foundations
* Quantum computing: models and paradigms
* Applications of Quantum Machine Learning
* Quantum Tensor Networks and their Applications in QML
* Quantum algorithms for Linear Systems of Equations, and other algorithms such as Quantum Neural Networks, Quantum Hidden Markov Models, Quantum PCA, Quantum SVM, Quantum Autoencoders, Quantum Transfer Learning, Quantum Boltzmann machines, Grover, Shor, and others.

You are invited to submit a paper for consideration. ALL ACCEPTED PAPERS will be published in the corresponding proceedings by Publisher:
Springer Nature – Book Series: Transactions on Computational Science & Computational Intelligence
https://www.springer.com/series/11769

Prospective authors are invited to submit their papers by uploading them to the evaluation website at:
https://american-cse.org/drafts/

For more information, visit our website:
https://baylor.ai/iceqt/

Important Deadlines

March 31, 2022: Submission of papers: https://american-cse.org/drafts/
– Full/Regular Research Papers (maximum of 10 pages)
– Short Research Papers (maximum of 6 pages)
– Abstract/Poster Papers (maximum of 3 pages)

April 18, 2022: Notification of acceptance (+/- two days)

May 12, 2022: Final papers + Registration

June 22, 2022: Hotel Room reservation (for those who are physically attending the conference).

July 25-28, 2022: The 2022 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE’22: USA)
Which includes the International Conference on Emergent and Quantum Technologies (ICEQT’22)

Chairs:
Dr. Javier Orduz, Baylor University
Dr. Pablo Rivas, Baylor University