23 papers · ranked by Valyu relevance
C. -C. Joseph Wang, F. Perkkola, I. Salmenperä, A. Meijer-van de Griend + 1 more
Hybrid variational quantum algorithms are promising for solving practical problems, such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy quantum computers. However, variational quantum algorithms (derived from randomized hardware-efficient…
Davide Lonigro, Dariusz Chruściński
We investigate the validity of quantum regression for a family of quantum Hamiltonians on a multipartite system leading to phase-damping reduced dynamics. After finding necessary and sufficient conditions for the CP-divisibility of the corresponding channel, we evaluate a hierarchy of equations equivalent to the…
Prasanna Date, Thomas Potok
A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to…
Dan-Bo Zhang, Shi-Liang Zhu, Z. D. Wang
Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classical data into quantum states. Our implementation is based on a hybrid…
Authors not listed
Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical machine learning models for BDE prediction using a chemically curated feature set encompassing atomic…
Prasanna Date, Thomas E. Potok
—A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to…
Guo, Xuyang, Dai, Jun + 2 more
Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest. For an unbiased benchmarking of quantum kernels, it is necessary to construct the most…
Bo Qi, Zhibo Hou, Li Li, Daoyi Dong + 2 more
A simple yet efficient state reconstruction algorithm of linear regression estimation (LRE) is presented for quantum state tomography. In this method, quantum state reconstruction is converted into a parameter estimation problem of a linear regression model and the least-squares method is employed to estimate the…
Matthew Otten, Imene Goumiri, Benjamin W. Priest, George Chapline + 1 more
'M. Schneider'] Quantum computers have the opportunity to be transformative for a variety of computational tasks. Recently, there have been proposals to use the unsimulatably of large quantum devices to perform regression, classification, and other machine learning tasks with quantum advantage by using kernel methods.…
Rania Derouich, Nour El Houda Mathlouthi
We present the first systematic, hardware-executed benchmark of twelve distinct quantum data-encoding strategies for drug-response prediction on a real superconducting quantum processing unit (QPU). All experiments were conducted on the IQM Garnet 20-qubit QPU via the IQM Resonance cloud platform, using the Qrisp…
Jun Dai, Roman V. Krems
With gates of a quantum computer designed to encode multi-dimensional vectors, projections of quantum computer states onto specific qubit states can produce kernels of reproducing kernel Hilbert spaces. We show that quantum kernels obtained with a fixed ansatz implementable on current quantum computers can be used for…
Prasanna Date, Davis Arthur, Lauren Pusey-Nazzaro
Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train…
Don Roosan, Samira Samrose, Rubayat Khan, Saif Nirzhor + 1 more
Predicting protein–ligand binding affinity is a fundamental challenge in computational biology and drug discovery, complicated by diverse factors including protein sequence variability, ligand chemical diversity, and structural resolution. Here, we present an integrative study that combines classical machine learning…
Ming Li, Wenjun Wang, Xiaoyu Zhang, Jing Wang + 3 more
'Shuqian Shen' 'Vladimir I. Manko'] Concurrence is a crucial entanglement measure in quantum theory used to describe the degree of entanglement between two or more qubits. Local unitary (LU) invariants can be employed to describe the relevant properties of quantum states. Compared to quantum state tomography, observing…
Emilio Corcione, Fabian Jakob, Lukas Wagner, Raphael Joos + 8 more
A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main…
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Adrian Jinich, Benjamin Sanchez-Lengeling, Haniu Ren, Rebecca Harman + 1 more
A quantitative understanding of the thermodynamics of biochemical reactions is essential for accurately modeling metabolism. The group contribution method (GCM) is one of the most widely used approaches to estimating standard Gibbs energies and redox potentials of reactions for which no experimental measurements are…
Julian M. Kleber
Computer-Aided Drug Design is advancing to a new era. Recent developments in statistical modelling, including Deep Learning, Machine Learning and high throughput simulations, enable workflows and deductions not achievable 20 years ago. The key interaction for many small molecules in the context of medicinal chemistry…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Authors not listed
The capabilities of regression models was investigated to predict the hydrogen bond energy based on partial charges, bond orders, bond distances and element types. Support vector regression in combination with gradient boosting resulted in a mean absolute percentage error of 3 % which is a significant improvement…
Abhishek Mishra, Ansh Rai
Biosynthetic gene clusters (BGCs) encode enzymatic pathways for natural products with pharmaceutical potential, yet prioritizing candidates from fragmented environmental DNA (eDNA) assemblies remains computationally challenging. We present BGC-QDR (Biosynthetic Gene Cluster Quantum Discovery and Ranking), an…
Authors not listed
Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
Authors not listed
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome…