23 papers · ranked by Valyu relevance
Azadeh Alavi, Fatemeh Kouchmeshki, Alavi, Abdolrahman
Hybrid quantum and classical learning aims to couple quantum feature maps with the robustness of classical neural networks, yet most architectures treat the quantum circuit as an isolated feature extractor and merge its measurements with classical representations by direct concatenation. This neglects that the quantum…
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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…
Luca Candelori, Swarnadeep Majumder, Antonio Mezzacapo, Javier Robledo Moreno + 5 more
Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a steep quantum cost for the loading of classical data and poor trainability of many quantum machine learning algorithms designed for near-term…
Riccardo Molteni, Casper Gyurik, Vedran Dunjko
Quantum computers are believed to bring computational advantages in simulating quantum many-body systems. However, recent works have shown that classical machine learning algorithms are able to predict numerous properties of quantum systems with classical data. Despite examples of learning tasks with provable quantum…
Leonardo Placidi, Ryuichiro Hataya, Toshio Mori, Koki Aoyama + 3 more
We introduce MNISQ, the first large-scale dataset for both quantum and classical machine learning during the NISQ era, containing 4.95 million circuits of 10 qubits constructed with up to 100 two-qubit gates. MNISQ serves as a foundational resource for developing natural language processing (NLP) models for quantum…
Priyanshu Sarma-Sarkar, Rajkumar Saini, Partha Pratim Roy
Approximately 50% of the population in India is estimated to experience sleep-related disorders. Sleep deprivation is a prevalent condition that adversely impacts cognitive performance, neural functioning, and overall health. Electroencephalography (EEG) offers an objective means of capturing neural alterations…
Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia, Dorsa Vazirzade + 5 more
Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine learning is a field that studies these vulnerabilities to build robust machine learning models. Quantum machine learning is an interdisciplinary…
Amir Hossein Salehi Shayegan, Laya Dejam
This study investigates RF magnetron sputtered ZnO thin films doped with Al, Cu, N and co-doped with Al/Cu, followed by post deposition annealing at 300-600 °C. Structural and compositional characterization was performed by X ray diffraction (XRD) and energy dispersive spectroscopy (EDS), while surface morphology and…
Kahn Rhrissorrakrai, Kathleen E Hamilton, Prerana Bangalore Parthasarathy, Aldo Guzmán-Sáenz + 4 more
Learning on sample-limited data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble models are when trained on a sample-limited data problem in healthcare and life sciences. We constructed multiple types of quantum ensembles for binary…
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…
Abhishek Sawaika, Samuel Yen-Chi Chen, Udaya Parampalli, Rajkumar Buyya
Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable strategy in the field of artificial intelligence. Most of the environments used for RL are often high-dimensional, and traditional RL…
Nenad Tomašev, Jarrod R. McClean, Johannes Bausch
Artificial intelligence and quantum computing have both seen tremendous progress in recent years, opening up new avenues for accelerating scientific discovery. Classical machine learning has already proven to be useful for addressing challenges in quantum computation; and hardware progress is well underway towards the…
Mohammad Aamir Sohail, Ranga R. Sudharshan, S. Sandeep Pradhan, Arvind Rao
We present a new Hamiltonian-learning framework based on time-resolved measurement data from a fixed local IC-POVM and its application to inferring gene regulatory networks. We introduce the quantum Hamiltonian-based gene-expression model (QHGM), in which gene interactions are encoded as a parameterized Hamiltonian…
Azamat Salamatov, Gowtham Atluri
How long a drug stays bound to its target-the residence time - is now recognized as a stronger in vivo efficacy driver than binding affinity alone. Yet current machine learning (ML) models for dissociation kinetics (koff) ignore two critical sources of structure information: (1) the change in protein–ligand geometry…
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…
Diljot Singh, Omana J., Smrithy G. S.
The rapid commercialization of generative artificial intelligence (AI), along with the maturation of quantum technologies has raised a question: can quantum-powered neural networks become the next major shift in large language model (LLM) technology? This naturally leads to another misconception that quantum systems…
S. Sony Priya, R. I. Minu
Face recognition systems struggle when faces appear at different orientations. Standard convolutional neural networks handle translation well but have no built-in way to deal with rotations or reflections. Quantum neural networks offer a different kind of expressiveness, but most existing designs ignore spatial…
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We present a unified theoretical framework that classifies and analyzes quantum enhancement strategies for classical algorithms, establishing design paradigms that systematically combine quantum subroutines with classical procedures. The theory identifies four fundamental enhancement mechanisms: quantum search…
Basel Mansour, Daniel Kei Takahashi, George Rafaelyan
Accurate prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties is a central challenge in early-stage drug discovery, where experimental determination remains costly and time-consuming. In this work, we propose a quantum-inspired preprocessing framework in which statistical…
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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…
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This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
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We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations…
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We present a comprehensive theoretical analysis of quantum subspace diagonalization methods for molecular electronic structure calculations, establishing rigorous complexity bounds and convergence guarantees. Building on recent developments in adaptive quantum algorithms for chemical systems, we formulate a general…