35 citations · 80 across the 17 of their papers we have counts for
17 papers
Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines
Payal D. Solanki, Anh Pham
Quantum Extreme Learning Machine (QELM) is an emerging hybrid quantum machine learning framework that leverages quantum system dynamics to enhance classical models. However, QELM c…
Towards structure-preserving quantum encodings
Arthur J. Parzygnat, Tai-Danae Bradley, Andrew Vlasic +1
Harnessing the potential computational advantage of quantum computers for machine learning tasks relies on the uploading of classical data onto quantum computers through what are c…
Robust Quantum Reservoir Computing for Molecular Property Prediction
Daniel Beaulieu, Milan Kornjaca, Zoran Krunic +4
Machine learning has been increasingly utilized in the field of biomedical research to accelerate the drug discovery process. In recent years, the emergence of quantum computing ha…
Quantum Circuits, Feature Maps, and Expanded Pseudo-Entropy: Analysis of Encoding Real-World Data into a Quantum Computer
Andrew Vlasic, Payal Solanki, Anh Pham
This manuscript introduces a computationally efficient method to calculate the nonlinearity of a quantum feature map, as well as a method for determining whether a quantum feature…
Empirical Power of Quantum Encoding Methods for Binary Classification
Gennaro De Luca, Andrew Vlasic, Michael Vitz +1
Quantum machine learning is one of the many potential applications of quantum computing, each of which is hoped to provide some novel computational advantage. However, quantum mach…
Investigate the Performance of Distribution Loading with Conditional Quantum Generative Adversarial Network Algorithm on Quantum Hardware with Error Suppression
Anh Pham, Andrew Vlasic
The study examines the efficacy of the Fire Opal error suppression and AI circuit optimization system integrated with IBM's quantum computing platform for a multi-modal distributio…
Hybrid Quantum Graph Neural Network for Molecular Property Prediction
Michael Vitz, Hamed Mohammadbagherpoor, Samarth Sandeep +3
To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learnin…
Towards quantum computing for clinical trial design and optimization: A perspective on new opportunities and challenges
Hakan Doga, M. Emre Sahin, Joao Bettencourt-Silva +10
Clinical trials are pivotal in the drug discovery process to determine the safety and efficacy of a drug candidate. The high failure rates of these trials are attributed to deficie…
Scoring Anomalous Vertices Through Quantum Walks
Andrew Vlasic, Anh Pham
With the constant flow of data from vast sources over the past decades, a plethora of advanced analytical techniques have been developed to extract relevant information from differ…
Conditional Generative Models for Learning Stochastic Processes
Salvatore Certo, Anh Pham, Nicolas Robles +1
A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly wi…
Quantum Kernel for Image Classification of Real World Manufacturing Defects
Daniel Beaulieu, Dylan Miracle, Anh Pham +1
The quantum kernel method results clearly outperformed a classical SVM when analyzing low-resolution images with minimal feature selection on the quantum simulator, with inconsiste…
Complement Grover's Search Algorithm: An Amplitude Suppression Implementation
Andrew Vlasic, Salvatore Certo, Anh Pham
Grover's search algorithm was a groundbreaking advancement in quantum algorithms, displaying a quadratic speed-up of querying for items. Since the creation of this algorithm it has…
Comparing Classical-Quantum Portfolio Optimization with Enhanced Constraints
Salvatore Certo, Anh Dung Pham, Daniel Beaulieu
One of the problems frequently mentioned as a candidate for quantum advantage is that of selecting a portfolio of financial assets to maximize returns while minimizing risk. In thi…
Benchmarking Amplitude Estimation on a Superconducting Quantum Computer
Salvatore Certo, Anh Dung Pham, Daniel Beaulieu
Amplitude Estimation (AE) is a critical subroutine in many quantum algorithms, allowing for a quadratic speedup in various applications like those involving estimating statistics o…
Where are the hidden-charm hexaquarks?
Zhe Liu, Hong-Tao An, Zhan-Wei Liu +1
In this work, we carry out the study of hidden-charm hexaquark states with the typical configurations (). The mass spectra of hidden-charm hex…
Investigating potential energy surfaces of noncollinear molecule using variational quantum circuit
Anh Pham, Daniel Beaulieu
We demonstrate the simulation of a noncollinear molecule, e.g. H2O molecule using Variational Quantum Eigensolver (VQE) with high chemical accuracy. The 2D and 3D potential energy…
Max-cut Clustering Utilizing Warm-Start QAOA and IBM Runtime
Daniel Beaulieu, Anh Pham
Quantum optimization algorithms can be used to recreate unsupervised learning clustering of data by mapping the problem to a graph optimization problem and finding the minimum ener…