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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>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-08-15T03:57:10.830+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frqst.2026.1862834</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frqst.2026.1862834</link>
        <title><![CDATA[Bayesian quantum phase estimation beyond the eigenstate approximation: sample complexity, Cramér–Rao bounds, and adaptive measurement design for non-eigenstate and mixed-state inputs on NISQ devices]]></title>
        <pubdate>2026-08-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>B Akoramurthy</author><author>B Surendiran</author><author>Xiaochun Cheng</author><author>Mohanty SN</author>
        <description><![CDATA[BackgroundQuantum phase estimation (QPE) is a foundational subroutine in quantum algorithms that ranges from Shor’s factoring scheme to variational quantum eigensolvers and quantum metrology protocols. Classical post-processing replacements for the inverse quantum Fourier transform (iQFT) —specifically Kitaev-style iterative estimation and Bayesian update schemes—substantially reduce circuit depth, making them practical for noisy intermediate-scale quantum (NISQ) hardware. Despite this promise, the statistical performance of these methods under the conditions that actually arise in practice—non-eigenstate inputs characterised by eigenstate overlap p≡O=|ψ|ϕ|2<1 and mixed-state purity γ≡P=Tr(ρ2)<1—remains uncharacterised in the literature.MethodsWe develop a rigorous information-theoretic framework for Bayesian QPE (BQPE) beyond the eigenstate assumption. Starting from the binary measurement likelihood attenuated by the joint factor OP, we derive exact classical Fisher information expressions and apply the Cramér–Rao inequality to obtain tight variance lower bounds. We then construct an adaptive measurement protocol based on von Mises posterior updates, prove its asymptotic efficiency in the frequentist sense, and establish matching upper and lower bounds on sample complexity. All theoretical results are validated against 2,000-trial Monte Carlo simulations and cross-checked against published nitrogen-vacancy (NV) centre and photonic experimental data.ResultsWe prove that the fundamental variance floor for any unbiased frequentist estimator is (1−pγ)/(N pγ n̄2)1/(N (pγ)2 n̄2), where N is the number of measurement shots and n̄2 is the mean squared measurement exponent evaluated at the optimal basis ϕ*=nθ*−π/2. The Bayesian MAP estimator achieves this bound asymptotically. The adaptive protocol attains sample complexity Θlog(1/δ)/(ε2pγ)Θlog(1/δ)/(ε2(pγ)2) to reach precision ε with probability ≥1−δ, representing a 2–4× 2–6× improvement over semiclassical QPE in the practically relevant regime p<0.8 (at matched γ=1). We additionally characterise estimation regimes in the (p,γ) plane; the minimum relative sample overhead of the adaptive protocol compared to the corrected CRLB occurs in the limit pγ→1, while the absolute sample count is minimised at fixed ε by maximising (pγ)2. Abstract CRLB and sample-complexity formulas are corrected to use (pγ)2 throughout, consistent with Theorem 1.ConclusionWe derive a corrected frequentist Cramér–Rao lower bound Var(θ̂)≥1/(N(pγ)2n̄2) for BQPE under non-eigenstate and mixed-state inputs, prove that the MAP estimator is asymptotically efficient, establish matching sample complexity bounds Θ(log(1/δ)/(ε2(pγ)2)) with explicit constants C1=1/8 and C2=12, and validate the protocol via 2,000-trial Monte Carlo simulation. A numerical cross-check against digitised data from three published hardware experiments shows agreement within 7.7–13.4%.Speculative claims (moved to future research)hardware-specific quantum advantage claims, direct comparison of shot counts across baselines under non-matched definitions, and the claim that performance is optimal at pγ=0.5 are not supported by the current analysis and are reserved for future research with full experimental access and matched comparisons.Conclusion revisedConfirmed results separated from speculative claims.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frqst.2026.1862700</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frqst.2026.1862700</link>
        <title><![CDATA[Eliminating vendor lock-in in quantum machine learning via framework-agnostic neural networks]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Poornima Kumaresan</author><author>Shwetha Singaravelu</author><author>Lakshmi Rajendran</author><author>Santhosh Sivasubramani</author>
        <description><![CDATA[IntroductionQuantum machine learning (QML) sits at the intersection of quantum computing and artificial intelligence and offers the potential to solve problems that remain intractable for classical methods. However, the current landscape of QML software frameworks is severely fragmented: models developed in TensorFlow Quantum cannot execute on PennyLane backends, circuits authored in Qiskit Machine Learning cannot be deployed to Amazon Braket hardware, and researchers who invest in one ecosystem face prohibitive switching costs when migrating to another. This vendor lock‐in impedes reproducibility, limits hardware access, and slows scientific discovery.MethodsWe present a framework‐agnostic quantum neural network (QNN) architecture that abstracts away vendor-specific interfaces through a unified computational graph, a hardware abstraction layer (HAL), and a multi‐framework export pipeline. The core architecture supports simultaneous integration with TensorFlow, PyTorch, and JAX as classical co‐processors, while the HAL provides transparent access to IBM Quantum, Amazon Braket, Azure Quantum, IonQ, and Rigetti backends through a single application programming interface (API). We introduce three pluggable data encoding strategies (amplitude, angle, and instantaneous quantum polynomial encoding) that are compatible with all supported backends, and an export module that leverages Open Neural Network Exchange (ONNX) metadata to enable lossless circuit translation across Qiskit, Cirq, PennyLane, and Braket representations.ResultsWe benchmark the framework on the Iris, Wine, and MNIST‐4 classification tasks, demonstrating training‐time parity (1% to 7% overhead) relative to native framework implementations while achieving statistically indistinguishable classification accuracy. Hardware validation on IBM Heron r2 processors (up to 156 superconducting qubits) confirms that parameter‐shift gradients computed through the HAL agree with simulator predictions within noise margins.DiscussionBy removing the obligation to commit irrevocably to a single vendor ecosystem, the framework addresses the single largest non‐technical barrier to QML adoption and establishes a reference architecture for interoperable quantum software.]]></description>
      </item><item>
        <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>
      </item><item>
        <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>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frqst.2025.1661544</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frqst.2025.1661544</link>
        <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>
      </item><item>
        <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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