26 papers · ranked by Valyu relevance
Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum…
Authors not listed
* jacob.biamonte@qubit.org 1Quantum Software Initiative, Skolkovo Institute of Science and Technology, Skoltech Building 3, Moscow 143026, Russia 2 Institute for Quantum Computing, University of Waterloo, Waterloo, N2L 3G1 Ontario, Canada 3 ICFO-The Institute of Photonic Sciences, Castelldefels (Barcelona), 08860 Spain…
Amine Zeguendry, Zahi Jarir, Mohamed Quafafou, Andreas Wichert
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of…
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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…
Enrico Zardini, Enrico Blanzieri, Davide Pastorello, Chun-An Chou
In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered…
Mohadeseh Zarei Ghoabdi, Elaheh Afsaneh
Quantum machine learning algorithms using the power of quantum computing provide fast- developing approaches for solving complicated problems and speeding-up calculations for big data. As such, they could effectively operate better than the classical algorithms. Herein, we demonstrate for the first time the…
Zainab Abohashima, Mohamed Elhoseny, Essam H. Houssein, Waleed M. Mohamed
'Waleed M. Mohamed'] Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more. Fully towards to combine classical machine learning (ML)…
Lucas Lamata
Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum…
E. Ghasemian, M. K. Tavassoly
Although the environmental effects, i.e., dissipation and decoherence seem to be the strongest adversaries in the quantum information realm, here, we address how dissipation can be harnessed for quantum state preparation and universal quantum computation. In this line, we propose a realistic scheme for hybrid…
Purva Tijare, Naman Kumar Mehta, Gajendra P. S. Raghava
Over the past decade, quantum machine learning, particularly quantum support vector machines (QSVMs), has emerged as an optimistic alternative to classical machine learning (CML) techniques. This study rigorously benchmarks the performance of QSVM and CML-based models across four diverse datasets relevant to…
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Quantum machine learning (QML) sits at the intersection of quantum computing and classical machine learning, offering the prospect of new computational paradigms and advantages for processing complex data. This chapter introduces the fundamentals of QML for readers from both communities, establishing a shared…
Giuseppe Di Caro, Vahagn Kirakosyan, Alexander G. Abanov, Jerome R. Busemeyer + 10 more
The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the context of liquid biopsy diagnostics. However, it presents a significant challenge due to the high dimensionality and complexity of…
F. A. Cárdenas-López, L. Lamata, J. C. Retamal, E. Solano + 1 more
'Zoltan Zimboras'] We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its…
Samuel Yen-Chi Chen, Shinjae Yoo, Rosario Lo Franco
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the…
Farina Riaz, Shahab Abdulla, Hajime Suzuki, Srinjoy Ganguly + 5 more
'Ravinesh C. Deo' 'Susan Hopkins' 'Iván García-Magariño' 'Shah Nazir' 'Habib Ullah Khan'] Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first…
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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…
Lorenzo Buffoni, Filippo Caruso
Here we will give a perspective on new possible interplays between Machine Learning and Quantum Physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by…
Blerta Rahmani, Hiqmet Kamberaj
In this study, we employed a novel method for prediction of (macro)molecular properties using a swarm artificial neural network method as a machine learning approach. In this method, a (macro)molecular structure is represented by a so-called description vector, which then is the input in a so-called bootstrapping swarm…
Javier Mancilla, Christophe Pere, Andreas Wichert
Quantum Machine Learning (QML) has not yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniques have achieved small incremental advantages, and a few experimental cases in hybrid quantum…
Ben Geoffrey A S
This work seeks to combine the combined advantage of leveraging these emerging areas of Artificial Intelligence and quantum computing in applying it to solve the specific biological problem of protein structure prediction using Quantum Machine Learning algorithms. The CASP dataset from ProteinNet was downloaded which…
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…
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Accurately modeling the dynamics of open quantum systems is critical for advancing quantum technologies, yet traditional methods often struggle with balancing accuracy and efficiency. Machine learning (ML) offers a promising alternative, particularly through recursive models that predict system evolution based on the…
Rambach, Markus, Roy, Abhishek + 10 more
Machine learning is widely applied in modern society, but has yet to capitalise on the unique benefits offered by quantum resources. Boson sampling—a quantum-interference based sampling protocol—is a resource that is classically hard to simulate and can be implemented on current quantum hardware. Here, we present a…
Junjie Hu, Xiangyu Li, Dan-Dan Liu, Shiyi Wang + 3 more
The combination of parametric quantum circuits and density matrix coding can significantly reduce the number of parameters in artificial neural networks. The reduction in the number of model parameters helps to improve the commu- nication efficiency when training deep learning models under federated learning…
ARIF ULLAH, Pavlo O. Dral
Nonadiabatic quantum dynamics are important for understanding light-harvesting processes, but their propagation with traditional methods can be rather expensive. Here we present a one-shot trajectory learning approach that allows to directly make ultra-fast prediction of the entire trajectory of the reduced density…
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This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…