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        <title>Frontiers in Quantum Science and Technology | Quantum Computing and Simulation section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/quantum-science-and-technology/sections/quantum-computing-and-simulation</link>
        <description>RSS Feed for Quantum Computing and Simulation section in the Frontiers in Quantum Science and Technology journal | New and Recent Articles</description>
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        <pubDate>2026-05-03T04:12:16.602+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frqst.2025.1636042</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frqst.2025.1636042</link>
        <title><![CDATA[Encodings of the weighted MAX k-CUT problem on qubit systems]]></title>
        <pubdate>2025-12-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Franz G. Fuchs</author><author>Ruben Pariente Bassa</author><author>Frida Lien</author>
        <description><![CDATA[The weighted MAX k-CUT problem involves partitioning a weighted undirected graph into k subsets, or colors, to maximize the sum of the weights of edges between vertices in different subsets. This problem has significant applications across multiple domains. This study explores encoding methods for MAX k-CUT on qubit systems by utilizing quantum approximate optimization algorithms (QAOA) and addressing the challenge of encoding integer values on quantum devices with binary variables. We examine various encoding schemes and evaluate the efficiency of these approaches. The study presents a systematic and resource-efficient method to implement the phase separation operator for the cost function of the MAX k-CUT problem. When encoding the problem into the full Hilbert space, we show the importance of encoding the colors in a balanced way. We also explore the option of encoding the problem into a suitable subspace by designing suitable state preparations and constrained mixers (LX- and Grover-mixer). Numerical simulations on weighted and unweighted graph instances demonstrate the effectiveness of these encoding schemes, particularly in optimizing circuit depth, approximation ratios, and computational efficiency.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frqst.2025.1653104</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frqst.2025.1653104</link>
        <title><![CDATA[Quantum machine learning early opportunities for the energy industry: a scoping review]]></title>
        <pubdate>2025-10-08T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Francesco Strata</author><author>Luca Migliori</author><author>Nour Gebran</author><author>Nicolina Guarino</author><author>Giacomo Carlo Colombo</author><author>Sara Pezzuolo</author><author>Emiliano Luzietti</author>
        <description><![CDATA[Quantum computing innovations have garnered significant attention for their potential to revolutionize industries, with the energy sector being one of the most promising areas for application. As global energy demand increases and sustainability becomes more critical, computational technologies offer groundbreaking solutions for energy production, storage, and distribution. In this landscape, quantum computing plays a crucial role in unlocking the full potential of artificial intelligence and machine learning as research and development in the quantum machine learning field grows constantly. We here present a scoping review of early quantum machine learning applications within the energy industry value chain. Starting from 34 sources, we analyze and discuss 22 use cases in the energy sector, thoroughly examining each to understand its potential applications and impact. We then evaluate these early-stage quantum applications to determine their feasibility and benefits, offering insights into their relevance and effectiveness in the context of the industry’s evolving landscape. This is done by introducing a novel framework: the Assessment Model for Innovation Management (AMIM). Our research highlights the opportunities that quantum innovations present for the energy sector and offers actionable insights into which applications are the best investments and why. Overall, the feasibility and technological maturity of quantum machine learning use cases are still in the early stages, though their market compatibility and potential benefits are mostly relatively high. This indicates that while quantum machine learning holds immense potential, further development is necessary to fully realize its benefits in the energy sector.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frqst.2025.1661544</guid>
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        <title><![CDATA[Certified random number generation using quantum computers]]></title>
        <pubdate>2025-09-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Pingal Pratyush Nath</author><author>Aninda Sinha</author><author>Urbasi Sinha</author>
        <description><![CDATA[We investigate how current noisy quantum computers can be leveraged for generating secure random numbers certified by Quantum Mechanics. While random numbers can be generated and certified in a device-independent manner through the violation of Bell’s inequality, this method requires significant spatial separation to satisfy the no-signaling condition, making it impractical for implementation on a single quantum computer. Instead, we employ temporal correlations to generate randomness by violating the Leggett-Garg inequality, which relies on the No-Signaling in Time condition to certify randomness, thus overcoming spatial constraints. By applying this protocol to different IBMQ platforms, we demonstrate the feasibility of secure, semi-device-independent random number generation using low-depth circuits with single-qubit gates. We show how error mitigation techniques lead to LGI violation compatible with theoretical predictions on the existing IBMQ machines.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frqst.2024.1462004</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frqst.2024.1462004</link>
        <title><![CDATA[qCLUE: a quantum clustering algorithm for multi-dimensional datasets]]></title>
        <pubdate>2024-10-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dhruv Gopalakrishnan</author><author>Luca Dellantonio</author><author>Antonio Di Pilato</author><author>Wahid Redjeb</author><author>Felice Pantaleo</author><author>Michele Mosca</author>
        <description><![CDATA[Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qCLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qCLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qCLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qCLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks – especially in high-dimensional datasets with high densities of points.]]></description>
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