International Conference on Emergent and Quantum Technologies (ICEQT’24)

July 22-25, 2024 — Las Vegas, NV

Dear Esteemed Colleagues,

Quantum computing is an expeditiously evolving field of interdisciplinary research, drawing upon fundamental principles from mathematics, physics, and engineering. To maintain scientific rigor and foster advancement, this domain necessitates a collaborative effort across various STEM disciplines.

We are delighted to announce the International Conference on Emergent and Quantum Technologies (ICEQT’24), scheduled for July 22-25, 2024, in Las Vegas, NV. The conference is designed to serve as a platform for researchers specializing in quantum machine learning and machine learning professionals exploring the application of AI in enhancing quantum computing algorithms. It aims to facilitate the exchange of insights and developments within these dynamic areas of study.

The burgeoning interest among machine learning practitioners in leveraging AI for quantum computing endeavors, and vice versa, underscores the relevance of this conference. Thus, we warmly welcome the submission of original research papers that contribute novel insights and state-of-the-art developments in the following areas of interest:

Foundations of Quantum Computing and Quantum Machine Learning

Quantum computing models and paradigms, e.g., Grover, Shor, and others
Quantum algorithms for Linear Systems of Equations
Quantum Tensor Networks and their Applications in QML

Quantum Machine Learning Algorithms

Quantum Neural Networks
Quantum Hidden Markov Models
Quantum PCA
Quantum SVM
Quantum Autoencoders
Quantum Transfer Learning
Quantum Boltzmann machines
Theory of Quantum-enhanced Machine Learning

AI for Quantum Computing

Machine learning for improved quantum algorithm performance
Machine learning for quantum control
Machine learning for building better quantum hardware

Quantum Algorithms and Applications

Quantum computing: models and paradigms
Quantum algorithms for hyperparameter tuning (Quantum computing for AutoML)
Quantum-enhanced Reinforcement Learning
Quantum Annealing
Quantum Sampling
Applications of Quantum Machine Learning

Fairness and Ethics in Quantum Machine Learning

We look forward to receiving your submissions and to welcoming you to ICEQT’24.

All submissions that are accepted for presentation will be included in the proceedings published by IEEE CPS. To ensure consistency in formatting, authors should follow the general typesetting instructions available on the IEEE’s website, including single-line spacing and a 2-column format. Additionally, authors of accepted papers must agree to the IEEE CPS standard statement regarding copyrights and policies on electronic dissemination.

Prospective authors are encouraged to submit their papers through the conference’s evaluation website at CMT. More information about the conference, including submission guidelines, can be found on our website at https://baylor.ai/iceqt/.

Important Deadlines

March 22, 2024: Submission of papers: https://cmt3.research.microsoft.com/ICEQT2024
– Full/Regular Research Papers (maximum of 8 pages)
– Short Research Papers (maximum of 5 pages)
– Abstract/Poster Papers (maximum of 3 pages)

April 15, 2024: Notification of acceptance (+/- two days)

May 1, 2024: Final papers + Registration

June 21, 2024: Last day for hotel room reservation at a discounted price.

July 22-25, 2024: The 2024 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE’24: USA)
Which includes the International Conference on Emergent and Quantum Technologies (ICEQT’24)

Chairs:
Pablo Rivas, PhD, Baylor University
Bikram Khanal, PhD Candidate, Baylor University

International Conference on Emergent and Quantum Technologies (ICEQT’23)

July 24-27, 2023 — Las Vegas, NV

Dear Esteemed Colleagues,

Quantum computing is a rapidly emerging interdisciplinary research area that integrates concepts from mathematics, physics, and engineering. For scientific rigor and successful progress in the field, it demands contributions from various STEM areas.

In this context, we are pleased to announce the International Conference on Emergent and Quantum Technologies (ICEQT’23), to be held on July 24-27, 2023, in Las Vegas, NV. The conference aims to provide an opportunity for researchers in the field of quantum machine learning and machine learning researchers interested in applying AI to enhance quantum computing algorithms, to present and discuss recent advancements in their areas of expertise.

Notably, there has been an increasing interest from machine learning researchers to apply AI to the quantum computing domain, and vice versa. As a result, we cordially invite submissions of original research papers that present state-of-the-art contributions in the following areas:

Foundations of Quantum Computing and Quantum Machine Learning

Quantum computing models and paradigms, e.g., Grover, Shor, and others
Quantum algorithms for Linear Systems of Equations
Quantum Tensor Networks and their Applications in QML

Quantum Machine Learning Algorithms

Quantum Neural Networks
Quantum Hidden Markov Models
Quantum PCA
Quantum SVM
Quantum Autoencoders
Quantum Transfer Learning
Quantum Boltzmann machines
Theory of Quantum-enhanced Machine Learning

AI for Quantum Computing

Machine learning for improved quantum algorithm performance
Machine learning for quantum control
Machine learning for building better quantum hardware

Quantum Algorithms and Applications

Quantum computing: models and paradigms
Quantum algorithms for hyperparameter tuning (Quantum computing for AutoML)
Quantum-enhanced Reinforcement Learning
Quantum Annealing
Quantum Sampling
Applications of Quantum Machine Learning

Fairness and Ethics in Quantum Machine Learning

We look forward to receiving your submissions and to welcoming you to ICEQT’23.

Prospective authors are encouraged to submit their papers through the conference’s evaluation website at https://american-cse.org/drafts/. More information about the conference, including submission guidelines, can be found on our website at https://baylor.ai/iceqt/.

Important Deadlines

April 12, 2023: Submission of papers: https://american-cse.org/drafts/
– Full/Regular Research Papers (maximum of 8 pages)
– Short Research Papers (maximum of 5 pages)
– Abstract/Poster Papers (maximum of 3 pages)

May 1, 2023: Notification of acceptance (+/- two days)

May 16, 2023: Final papers + Registration

June 21, 2023: Last day for hotel room reservation at a discounted price.

July 24-27, 2023: The 2023 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE’23: USA)
Which includes the International Conference on Emergent and Quantum Technologies (ICEQT’23)

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

Power of Data In Quantum Machine Learning

This week at the lab, we read the following paper, and here is our summary:

Huang, Hsin-Yuan, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R. McClean. “Power of data in quantum machine learning.” Nature communications 12, no. 1 (2021): 2631.

Summary

This work focuses on the advancement of quantum technologies and their impact on machine learning. The two paths towards the quantum enhancement of machine learning include using the power of quantum computing to improve the training process of existing classical models and using quantum models to generate correlations between variables that are inefficient to represent through classical computation. The authors show that this picture is incomplete in machine learning problems where some training data are provided, as the provided data can elevate classical models to rival quantum models. The authors present a flowchart for testing potential quantum prediction advantage based on prediction error bounds for training classical and quantum ML methods based on kernel functions. This elevation of classical models through some training samples is illustrative of the power of data. The authors also show that, “training a specific classical ML model on a collection of $N$ training examples $(\mathbf{x}, y = f(\mathbf{x}))$ would give rise to a prediction model $h(\mathbf{x})$ with

(1) $\begin{equation*} \mathbb{E}_\mathbf{x}|h(\mathbf{x})-f(\mathbf{x})|\leq c \sqrt{p^2/N} \end{equation*}$

for a constant $c > 0$ . Hence, with $N \approx p^2/\epsilon^2$ training data, one can train a classical ML model to predict the function $f(\mathbf{x})$ up to an additive prediction error $\epsilon$ .” They also show that a slight geometric difference between kernel functions defined by classical and quantum ML guarantees similar or better performance in prediction by classical ML. On the other hand, a sizeable geometric difference indicates the possibility of a large prediction advantage using the quantum ML model.

Additionally, the authors introduced ”projected quantum kernels” and demonstrated, through empirical results, that these outperformed all tested classical models in prediction error. This work provides a guidebook for generating ML problems that showcase the separation between quantum and classical models.

Intellectual Merit

This work provides a theoretical and computational framework for comparing classical and quantum ML models. The authors develop prediction error bounds for training classical and quantum ML methods based on kernel functions, which provide provable guarantees and are very flexible in the functions they can learn. The authors also develop a flowchart for testing potential quantum prediction advantage, a function-independent prescreening that allows one to evaluate the possibility of better performance. The authors provide a constructive example of a discrete log feature map, which gives a provable separation for their kernel. They rule out many existing models in the literature, providing a powerful sieve for focusing the development of new data encodings.

Broader Impact

The authors’ contributions to the field of quantum technologies and machine learning have significant broader impacts. The development of a flowchart for testing potential quantum prediction advantage provides a tool for researchers and practitioners to determine the possibility of better performance using quantum ML models. The authors’ framework can also be used to compare and construct hard classical models, such as hash functions, which have applications in cryptography and secure communication. The authors’ work has the potential to accelerate the development of new data encodings, leading to more efficient and accurate machine learning models. This has far-reaching implications for various applications, including image recognition, text translation, and even physics applications, where machine learning can revolutionize how we analyze and interpret data. The paper was organized and written by collaborating with three famous quantum institutes: Google Quantum AI, the Institute for Quantum Information and Matter at Caltech, and the Department of Computing and Mathematical Sciences at Caltech.

Supercomputing Leverages Quantum Machine Learning and Grover’s Algorithm

Khanal, B., Orduz, J., Rivas, P. et al. Supercomputing leverages quantum machine learning and Grover’s algorithm. J Supercomput 79, 6918–6940 (2023). https://doi.org/10.1007/s11227-022-04923-4. [ bib | .pdf ]

Quantum computing, a field that has drawn significant attention recently, promises to revolutionize how we approach computational problems. In Bikram Khanal’s paper titled “Supercomputing leverages quantum machine learning and Grover’s algorithm,” we delve deep into the intricacies of quantum computing, with a particular focus on Grover’s algorithm and its potential applications in quantum machine learning. This journal article is an extended version of an earlier conference paper found here.

Understanding Quantum Computing and Grover’s Algorithm

At the heart of our paper is a comprehensive discussion of the basics of quantum computing. We shed light on Grover’s quantum algorithm, which promises faster search capabilities compared to classical algorithms. Our team conducted an experiment simulating classical logical circulation by exploiting the power of amplitude amplification, a core principle behind Grover’s algorithm.

The Quantum Advantage

One of the primary takeaways from our research is the potential advantage quantum computing and quantum machine learning can offer over classical counterparts. By harnessing the efficiency of quantum computers, we can significantly reduce the reliance on supercomputing power for executing complex programs. This is particularly relevant for problems that pose challenges for classical computing methods.

Exploring Quantum Machine Learning

Our paper also delves into two promising approaches in quantum machine learning:

Variational Quantum Circuits: These are quantum circuits that can be tuned variably, allowing for optimization in quantum computations.
Kernel-based Quantum Machine Learning: This approach leverages the concept of kernels, which are used in classical machine learning for various tasks, and adapts them for quantum computations.

We believe the intersection of quantum algorithms and machine learning is ripe for exploration. While there’s a lot of potential, it’s also a domain that requires rigorous research to unearth solutions that can genuinely outperform classical machine learning methods. Kernel-based approaches, in particular, hold significant promise in the near future of quantum machine learning and supercomputing.

Looking Ahead

Our journey into the world of quantum computing doesn’t end here. We have charted out an exciting roadmap for our future research:

Real-world Data Testing: We aim to test our algorithm using actual data from classical circuits. This will involve parsing the circuit and feeding it into our algorithm.
Optimizing Computational Complexity: Our goal is to enhance the efficiency of our approach, especially when compared to classical methods.
Leveraging Grover’s Algorithm for Machine Learning: We believe many classification problems in machine learning can be reframed as search problems, making them ideal candidates for Grover’s algorithm.
Kernel Methods and Grover’s Algorithm: In our future work, we plan to reformulate machine learning problems using kernel methods, aiming to solve them efficiently as search problems using Grover’s algorithm.

Finally, we are at the cusp of some groundbreaking discoveries that can redefine the supercomputing landscape. We invite readers and fellow researchers to join us on this exciting journey and keep a close watch on the advancements in this domain. Read the paper here [ bib | .pdf ].

Quantum circuit for Grover algorithm with user-defined oracle on clauses |q0⟩ AND |q3⟩ , |q1⟩ XOR |q2⟩, and |q2⟩ AND |q3⟩. Note that we need seven qubits in total. For c clauses, three in this case, we require c additional qubits and one output qubit, resulting (n + c + 1) qubits quantum circuit.

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

Quantum Machine Learning: A Case Study of Grover’s Algorithm

Interested in how quantum algorithms work, or what does the quantum circuit looks like? Read our recent paper on Grover’s algorithm covering rudimentary to intermediate quantum computation with machine learning descriptions. [pdf, bib]