Quantum Machine Learning: QML Algorithms & Quantum AI Applications
Quantum machine learning news: QML algorithms, quantum AI, quantum neural networks. Hybrid quantum-classical ML & quantum advantage research.
Quantum machine learning (QML) explores intersections between quantum computing and artificial intelligence, investigating whether quantum algorithms can accelerate data analysis, pattern recognition, and model training beyond classical capabilities.
Theoretical foundations include quantum advantages for linear algebra subroutines central to machine learning—matrix inversion, principal component analysis, and vector inner products. The HHL algorithm promises exponential speedup for specific sparse, well-conditioned systems.
India's Quantum Machine Learning Landscape
India's National Quantum Mission supports quantum machine learning research through its Quantum Computing Thematic Hub at IISc Bengaluru. The Indian Institute of Science offers a Certificate Programme in Quantum Computing and Artificial Intelligence through its Centre for Continuing Education, providing comprehensive training in quantum AI applications with hands-on coding using Qiskit and PennyLane.
Tata Consultancy Services (TCS) develops quantum machine learning algorithms for enterprise applications. Infosys explores quantum AI through its Quantum Living Labs. IIT Delhi offers certification programs in quantum computing and machine learning in collaboration with industry partners.
The NQM targets developing quantum algorithms for optimization, simulation, and machine learning, with human resource development including training programs for quantum professionals.
Current NISQ-era QML relies on hybrid quantum-classical approaches including variational quantum algorithms, quantum neural networks, and quantum kernel methods. Challenges include "barren plateaus" in optimization landscapes limiting trainability, and limited qubit counts restricting model complexity.
quantum-computingDigital twins for compact hybrid quantum classical learning in FMCW radar detection
--> Quantum Physics arXiv:2605.24187 (quant-ph) [Submitted on 22 May 2026] Title:Digital twins for compact hybrid quantum classical learning in FMCW radar detection Authors:Sebastian Ratto Valderrama, Ahmed N. Sayed, Arien Sligar, Jose R. Rosas-Bustos, Omar M. Ramahi, George Shaker View a PDF of the paper titled Digital twins for compact hybrid quantum classical learning in FMCW radar detection, by Sebastian Ratto Valderrama and 5 other authors View PDF HTML (experimental) Abstract:Frequency-modulated continuous-wave radar sensing often relies on labeled measurements that are costly, restricted, or difficult to collect at scale. This work evaluates physics-informed digital twins as controlled testbeds for early-stage quantum-classical radar learning. Two synthetic radar benchmarks are considered: unmanned aerial vehicle classification from range-Doppler maps and human fall detection from Doppler-time spectrograms. For both tasks, inputs are standardized, reduced using principal component analysis, and classified using either a radial basis function support vector classifier or a quantum support vector classifier. All quantum-kernel results are obtained using noiseless classical simulation; no quantum hardware is used, and no quantum-advantage claim is made. Across five random seeds, the quantum support vector classifier improves the UAV benchmark from four principal components onward, reaching an accuracy of 0.941 +/- 0.012 at eight components, compared with 0.880 +/- 0.029 for the classical baseline. On the fall-detection benchmark, both classifiers perform similarly, with a small quantum-kernel improvement at higher feature dimensions. A Gaussian-noise robustness study shows limited performance degradation across the tested noise levels, while preserving the UAV quantum-kernel gain. These results support digital twins as useful, controlled environments for radar-QML benchmarking prior to measured-data validation and hardware execution. Comments: Subjects: Quantum
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quantum-computingA Matched Spectral Benchmark of Quantum Inspired Feature Maps
--> Quantum Physics arXiv:2605.24324 (quant-ph) [Submitted on 23 May 2026] Title:A Matched Spectral Benchmark of Quantum Inspired Feature Maps Authors:Toheeb Ogunade, Taofeek Kassim, Etinosa Osaro View a PDF of the paper titled A Matched Spectral Benchmark of Quantum Inspired Feature Maps, by Toheeb Ogunade and 2 other authors View PDF HTML (experimental) Abstract:Quantum machine learning is often motivated by the idea that quantum systems can expose useful high-dimensional structure that is difficult to access with classical models. We isolate one central component of this claim: the fixed data-encoding map. Amplitude, angle, and basis encoding are evaluated as deterministic feature maps for classical supervised learning under matched output dimensionality and strong classical controls. The benchmark compares these encodings against raw linear models, random Fourier features, polynomial features, PCA, RBF SVMs, and shallow neural networks across diverse classical datasets. Rather than treating performance as a single endpoint, we analyze the geometry of each representation through effective rank, condition number, centered kernel alignment, predictive performance, and practical overhead. The resulting picture is mechanistic: amplitude encoding can remove magnitude information through unit-sphere normalization, angle encoding can become geometrically redundant with raw linear features, and basis encoding can impose a binary Hamming geometry that is poorly aligned with smooth decision structure. These findings do not argue against quantum computation, however, they show that fixed quantum-inspired encoding geometry alone is not a reliable source of machine-learning advantage on classical data. Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2605.24324 [quant-ph] (or arXiv:2605.24324v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.24324 Focus to learn more arXiv-issued DOI via DataCite (pending registratio
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quantum-computingSample-efficient benchmarking of shallow all-to-all random quantum circuits
--> Quantum Physics arXiv:2605.22909 (quant-ph) [Submitted on 21 May 2026] Title:Sample-efficient benchmarking of shallow all-to-all random quantum circuits Authors:Gregory Bentsen, Bill Fefferman, Soumik Ghosh, Michael J. Gullans, Yinchen Liu View a PDF of the paper titled Sample-efficient benchmarking of shallow all-to-all random quantum circuits, by Gregory Bentsen and Bill Fefferman and Soumik Ghosh and Michael J. Gullans and Yinchen Liu View PDF HTML (experimental) Abstract:Random circuit sampling (RCS) remains one of the most competitive frameworks for demonstrating quantum advantage in near-term noisy intermediate-scale quantum (NISQ) hardware. Unfortunately, absent error-correction, existing benchmarks to characterize these experiments, like linear cross-entropy, have been classically spoofed due to noise. Because of this, there are interesting regimes, like shallow-depth random quantum circuits, where sampling is plausibly classically intractable, but no existing benchmark can distinguish between a noisy quantum computer and an adversarial classical spoofer. In this paper, we demonstrate that the nonlinear cross-entropy provides a sample-efficient benchmark for shallow-depth all-to-all random quantum circuits whose score cleanly separates noisy quantum computers from state-of-the-art classical spoofers, even in the presence of depolarizing noise. Further, we develop a binary classifier based on the notion of heavy output generation that features logarithmic sample complexity at short depth. Our evidence comes from exact analytic expressions for all-to-all Brownian circuit ensembles derived using replica tricks, and numerical simulations that corroborate these results for discrete Haar-random unitary circuits. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.22909 [quant-ph] (or arXiv:2605.22909v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.22909 Focus to learn more arXiv-issued DOI via DataCite Submission
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quantum-computingEstimating Green's functions with a robust quantum Arnoldi method
--> Quantum Physics arXiv:2605.22920 (quant-ph) [Submitted on 21 May 2026] Title:Estimating Green's functions with a robust quantum Arnoldi method Authors:Jacob S. Nelson, Andrew B. Baczewski View a PDF of the paper titled Estimating Green's functions with a robust quantum Arnoldi method, by Jacob S. Nelson and Andrew B. Baczewski View PDF HTML (experimental) Abstract:Many applications of Green's functions (GFs) require their evaluation over intervals or at multiple points, motivating quantum algorithms that return an efficiently computable functional representation rather than mere point estimates. We introduce a robust quantum Arnoldi method (ROQAM) that achieves this goal. Its robustness is derived from formulation in terms of orthogonal polynomials, which preserves the upper-Hessenberg structure of the projected matrices despite finite-precision estimation. We also show that as the iteration depth increases, the precision required for matrix-element estimation can be reduced. Resource estimates for the spectral function of a quantum impurity model indicate that ROQAM outperforms pointwise estimation via quantum singular value transformation by multiple orders of magnitude. Finally, we show that the ROQAM can be used to estimate GFs at nonzero temperatures using only a single Krylov subspace. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.22920 [quant-ph] (or arXiv:2605.22920v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.22920 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jacob Nelson [view email] [v1] Thu, 21 May 2026 18:00:41 UTC (1,108 KB) Full-text links: Access Paper: View a PDF of the paper titled Estimating Green's functions with a robust quantum Arnoldi method, by Jacob S. Nelson and Andrew B. BaczewskiView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2026-05 Referen
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quantum-computingClassical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
--> Quantum Physics arXiv:2605.23138 (quant-ph) [Submitted on 22 May 2026] Title:Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning Authors:Gino Kwun, Dhanvi Bharadwaj, Gokul Subramanian Ravi View a PDF of the paper titled Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning, by Gino Kwun and 2 other authors View PDF HTML (experimental) Abstract:Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces. To overcome this bottleneck, we propose CRiSP (a Clifford Reinforcement Learning agent for State Preparation), a framework that formulates discrete prefix selection as a sequential decision-making problem. CRiSP utilizes Neural-Guided Monte Carlo Tree Search, driven by a Transformer-based policy trained via self-play, to insert learned Clifford gates before fixed parameterized rotations. This enables the construction of high-quality initial states entirely through polynomial-time classical stabilizer simulation without altering the underlying circuit architecture. By integrating a curriculum learning strategy that progressively expands the search horizon, the agent efficiently scales to deep circuits. Evaluated on QAOA benchmarks of up to $22$ qubits and $1{,}370$ parameters, CRiSP outperforms state-of-the-art Clifford initialization methods by a mean of $3.17\times$ (max $45.02\times$) in average energy accuracy and $2.44\times$ (max $16.01\times$) in best-achieved energy accuracy. Assessments on VQE tasks further demonstrate the framework's robustness and generalizability. Comments: Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technol
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quantum-computingA General Quantum Speed Limit for Non-Hermitian Systems
--> Quantum Physics arXiv:2605.23250 (quant-ph) [Submitted on 22 May 2026] Title:A General Quantum Speed Limit for Non-Hermitian Systems Authors:Zhanxi Wang, Xiaozhe Hao, X. X. Yi View a PDF of the paper titled A General Quantum Speed Limit for Non-Hermitian Systems, by Zhanxi Wang and 2 other authors View PDF HTML (experimental) Abstract:The quantum speed limit (QSL) refers to the maximum speed of a quantum system to evolve from an initial state to its orthogonal states. The bound on the QSL for Hermitian systems, for example the Mandelstam-Tamm (MT) and Margolus-Levitin (ML) as well as Sun-Zheng(SZ) bound, was studied respectively from the perspectives of average value and variance of the system Hamiltonian as well as the geometry of the system. While the compactness of the MT-type, ML-type and SZ-type bounds has been examined well for Hermitian systems, a compact QSL for non-Hermitian systems has not been well studied. In this work, based on the biorthogonal basis theory we derive two distinct and tighter bounds on the QSL for non-Hermitian systems, which correspond to the MT and ML bounds for Hermitian systems. We show that the shortest evolution time corresponding to the two bounds of the non-Hermitian system can be attained by certain initial states, showing the compactness and tightness of our bounds. These initial states dubbed fastest initial states(FIS) are different from that in Hermitian systems. A bound close to QSL for non-FIS is presented and comparison of our bound with others in literature is performed. To illustrate our results, we present a minimal non-Hermitian system to show QSL, and the condition for the shortest evolution time is derived analytically using the present theory. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.23250 [quant-ph] (or arXiv:2605.23250v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.23250 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submiss
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quantum-computingQuantum Computing Stocks Short Interest Jumps Amid Valuation Concerns - Benzinga
Quantum Computing Stocks Short Interest Jumps Amid Valuation Concerns Benzinga
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quantum-computingIs anyone working on QRAM?
QRAM sure would solve a lot problems for quantum algorithms. Yet I don’t know of anyone working on it. Is anyone working on it? submitted by /u/SurinamPam [link] [comments]
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quantum-computingQuantum Computing Company IonQ Is A Buy (Technical Analysis)
Walter Zelezniak Jr5.2K FollowersFollow5ShareSavePlay(11min)Comments(2)SummaryIonQ, Inc. demonstrates bullish technicals with strong price action, momentum, volume, and relative strength since summer 2024.IONQ reported Q1 2026 revenues of $64.67M, an 8x YoY increase, and raised full-year guidance to $270M, despite negative EPS and poor profitability.Technical indicators—30-week EMA, PPO momentum, and institutional volume—signal continued accumulation and outperformance versus the S&P 500.I am buying IONQ, using a stop-loss strategy below the 30-week EMA to manage downside risk amid ongoing unprofitability. spawns/iStock via Getty Images In this article will outline my bullish thesis for the quantum computing company IonQ, Inc. (IONQ). I will briefly discuss quantum computing, IONQ’s recent earnings report, and its valuation grade. Then I will outline my bullishThis article was written byWalter Zelezniak Jr5.2K FollowersFollowAs an individual investor nearing retirement I am trying to build my financial assets in order to have a fulfilling retirement. I am interested in trading both long and short; or at least using inverse ETFs, to take advantage of market declines. Having long term and short term trading strategies, proper execution of my trading plan, and absolute investing results are my goals. I see my articles as a way to keep me focused on developing winning trades. I also expect to learn much from the feedback that is provided in the comments section.Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, but may initiate a beneficial Long position through a purchase of the stock, or the purchase of call options or similar derivatives in IONQ over the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Seeking A
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quantum-computingOnline Short Course on “Free-Space Quantum Communication”
Online Short Course on “Free-Space Quantum Communication” Dates: Monday, July 27, 2026 to Friday, July 31, 2026Web page: https://www.prl.res.in/prl-eng/uncssteapRegistration deadline: Saturday, May 23, 2026Submission deadline: Saturday, May 23, 2026Applications are invited for a short course on “Free-Space Quantum Communication” (July 27-31, 2026) to be conducted Online by Physical Research Laboratory (PRL), Ahmedabad under the auspices of the Center for Space Science and Technology Education in Asia and the Pacific (CSSTEAP), affiliated to the United Nations. Application deadline June 30, 2026 Log in or register to post comments
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quantum-computingEPB and University of Tennessee at Chattanooga Launch $6.8 Million Quantum Workforce Initiative
EPB and University of Tennessee at Chattanooga Launch $6.8 Million Quantum Workforce Initiative The Board of Directors for EPB has approved a formal resolution establishing a $6.8 million USD joint funding partnership with the University of Tennessee at Chattanooga (UTC). The matching investment allocates $850,000 annually from each institution over a four-year operational term. The programmatic mandate expands regional academic infrastructure, funds applied research tracks, and builds commercialization pathways for emerging quantum hardware and software protocols. The initiative leverages Chattanooga’s existing municipal infrastructure, centering its operational workflows around the EPB Quantum Center. Technical Architecture & Specifications / Operational Implementation The technical framework builds directly upon the regional fiber-optic distribution grid, expanding academic access to the EPB Quantum Network. Launched commercially in 2023, the software-managed network provides programmable channels for quantum key distribution (QKD) and quantum networking experimentation, with UTC operating an active, on-campus network node. The newly expanded funding expands this physical testbed to integrate upcoming EPB Quantum Computing cloud-service resources slated for rollout later in 2026. The capital injection funds active research programs across four core technical disciplines: quantum algorithm design, quantum machine learning (QML) data models, multi-node quantum networking protocols, and nitrogen-vacancy or atom-based quantum sensing systems. Strategic Positioning & Ecosystem Integration The strategic investment aims to capture localized economic value from the commercialization of frontier technologies, aligning with long-term regional macro-projections. According to data from the McKinsey Quantum Technology Monitor 2026, the commercial scaling of quantum computing use cases is projected to generate up to $2.7 trillion in global economic value by 2035. On a
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quantum-computingWhy Rigetti Computing Stock Keeps Going Up
Yesterday, as you've probably heard, The Wall Street Journal reported on a Trump Administration plan to award $2 billion to nine quantum computing companies -- Rigetti Computing (RGTI +20.87%) among them -- and to take government equity stakes in the companies in return. Rigetti shares started moving one day before the announcement was made, then rocketed higher yesterday -- and higher again today. Up 18% through 10:55 a.m. Friday morning, Rigetti shares have gained an astounding 63% in just three days of trading, and investors are wondering: Is any price too high to pay for this quantum computing stock? Image source: Getty Images. And now it's official Shortly after WSJ broke the story, the U.S. Department of Commerce confirmed that not only does it plan to award grants, but it has in fact already signed letters of intent to do so. Operating under the CHIPS and Science Act, Commerce will "support and accelerate critical research and manufacturing of technologies for the quantum ecosystem to ensure continued United States leadership and national security." Two quantum foundries, Globalfoundries (GFS +7.54%) and International Business Machines (IBM +1.56%), will receive $375 million and $1 billion, respectively. Rigetti and five others will receive $100 million apiece, and the ninth company will receive $38 million. Each of the seven non-foundry recipients will focus on specific technologies needed to build quantum computers. Rigetti in particular will focus on miniaturization and cryostat devices for maintaining extremely low temperatures. ExpandNASDAQ: RGTIRigetti ComputingToday's Change(20.87%) $4.60Current Price$26.64Key Data PointsMarket Cap$7.3BDay's Range$22.67 - $26.7252wk Range$10.30 - $58.15Volume3.6MAvg Vol30.6MGross Margin-5945.49% What does this mean for Rigetti stock? The question now is how much good even this money can do for Rigetti, which is burning more than $80 million a year. Even if Rigetti gets all of the "up to $100 million" it's allotted, thi
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quantum-computingThe Hidden Bottleneck in Quantum Machine Learning: Getting Data into a Quantum Computer - Towards Data Science
The Hidden Bottleneck in Quantum Machine Learning: Getting Data into a Quantum Computer Towards Data Science
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quantum-computingQuantum Doeblin Coefficients: Interpretations and Applications
AbstractIn classical information theory, the Doeblin coefficient of a classical channel provides an efficiently computable upper bound on the total-variation contraction coefficient of the channel, leading to what is known as a strong data-processing inequality. Here, we investigate quantum Doeblin coefficients as a generalization of the classical concept. In particular, we define various new quantum Doeblin coefficients, one of which has several desirable properties, including concatenation and multiplicativity, in addition to being efficiently computable. We also develop various interpretations of two of the quantum Doeblin coefficients, including representations as minimal singlet fractions, exclusion values, reverse max-mutual and oveloH informations, reverse robustnesses, and hypothesis testing reverse mutual and oveloH informations. Our interpretations of quantum Doeblin coefficients as either entanglement-assisted or unassisted exclusion values are particularly appealing, indicating that they are proportional to the best possible error probabilities one could achieve in state-exclusion tasks by making use of the channel. We also outline various applications of quantum Doeblin coefficients, ranging from limitations on quantum machine learning algorithms that use parameterized quantum circuits (noise-induced barren plateaus), on error mitigation protocols, on the sample complexity of noisy quantum hypothesis testing, on the fairness of noisy quantum models, and on mixing, indistinguishability, and decoupling times of time-varying channels. All of these applications make use of the fact that quantum Doeblin coefficients appear in upper bounds on various trace-distance contraction coefficients of a quantum channel. Furthermore, in all of these applications, our analysis using quantum Doeblin coefficients provides improvements of various kinds over contributions from prior literature, both in terms of generality and being efficiently computable.► BibTeX data@artic
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quantum-computingQROM Copying Mechanism Halves Quantum Data Loading Costs
Xanadu Quantum Technologies has achieved a reduction in the operational costs of quantum computing through a breakthrough in Quantum Read-Only Memory (QROM) technology. The company’s new implementation approximately halves the number of expensive Toffoli gates required within QROM modules, a critical advancement for problem sizes constrained by qubit availability. This addresses a longstanding bottleneck in loading classical data onto quantum computers; QROM performance had remained stagnant for seven years prior to this innovation. Xanadu achieves these optimizations by replacing traditional qubit “swapping” with a “copying” mechanism, and streamlining data unloading processes. “Our team focuses on making quantum computing practical for real-world use,” said Dr. Christian Weedbrook, Xanadu Founder and Chief Executive Officer. “By halving QROM costs, we are using quantum algorithm developments to reduce the cost of quantum computation for many applications.” QROM Optimization Halves Toffoli Gate Count Seven years of stagnant Quantum Read-Only Memory (QROM) performance have been overcome by Xanadu Quantum Technologies with a new algorithmic breakthrough that is expected to significantly reduce the operational cost of quantum applications. Efficiently loading classical data onto quantum computers has long presented a challenge, limiting the potential of near-term, utility-scale fault-tolerant systems. Xanadu’s implementation is expected to approximately halve the number of expensive quantum operations required for QROM, a reduction that promises to unlock more complex computations on existing hardware. The core of this optimization lies in a novel approach to reducing Toffoli gates, among the most computationally intensive operations a quantum computer performs, within QROM modules. The team also streamlined the process of unloading data from QROM, consolidating multiple redundant steps into a single, efficient operation. This combined approach allows quantum programs
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quantum-computingQuantum Algorithms Now Solve Complex Industrial Problems with Fewer Qubits
Scientists at West Virginia University and Cornell University have introduced a novel quantum reinforcement learning framework to address the significant computational challenges inherent in process synthesis, a crucial aspect of chemical engineering. Austin Braniff and colleagues, spanning the Department of Chemical and Biomedical Engineering at West Virginia University and the R.F. Smith School of Chemical and Biomolecular Engineering at Cornell University, have engineered a system that demonstrably improves scalability and overcomes previous limitations related to qubit requirements in the design of complex chemical processes. This framework not only provides a robust methodology for tackling these intricate problems but also establishes a valuable benchmark for rigorously comparing the performance of classical and quantum algorithms. This paves the way for future quantum applications within the broader field of process systems engineering Quantum algorithms enhance process synthesis optimisation efficiency and scalability Quantum reinforcement learning algorithms achieved a 1.2x improvement in efficiency on a per-parameter basis when compared to established classical reinforcement learning benchmarks for moderate-scale process synthesis problems. This enhancement stems from a critical decoupling of qubit requirements from the size of the problem being addressed. Traditionally, the computational burden of process synthesis escalates rapidly with increasing complexity, often rendering large-scale designs intractable. By reducing the dependence on qubit numbers, the fundamental units of quantum information, this new framework unlocks the potential to tackle more complex flowsheet designs than previously possible. The core innovation lies in the development of novel state encoding algorithms which efficiently represent the process design space within the quantum system, minimising the number of qubits needed for simulation. This circumvents the limitations imposed b
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quantum-computingRobots Learn Navigation Using Quantum Processing and Achieve Stable Trajectories
Mohamed Khair Altrabulsi and colleagues at NYUAD Research Institute, in collaboration with New York University, present Q-SpiRL, a quantum spiking reinforcement learning framework for obstacle-aware robot navigation in dynamic environments. Their research compares five agent families, focusing on a quantum-enhanced spiking neural network (QSNN) integrating spike-based temporal processing with quantum feature transformation. Experiments in grid-world environments, ranging from 20×20 to 40×40, show QSNN achieves a success rate of up to 99% while maintaining efficient and smooth trajectories, and key validation confirms the feasibility of deploying this hybrid policy on actual IBM quantum hardware. Quantum spiking neural networks achieve near-perfect robotic navigation in complex environments A 99% success rate in complex grid-world environments was attained by a new quantum-enhanced spiking neural network, exceeding previous capabilities in robot navigation. High reliability and efficient path planning in active environments previously proved difficult for robotic systems, with conventional methods struggling to scale effectively with increasing complexity. The Q-SpiRL framework, utilising a quantum spiking neural network (QSNN), combines the benefits of spike-based temporal processing, mimicking the brain’s efficient signalling, with variational quantum feature transformation to refine data interpretation. Traditional reinforcement learning algorithms often require extensive training and struggle with the ‘curse of dimensionality’ as the environment’s complexity increases, leading to slow learning times and suboptimal policies. Q-SpiRL addresses these limitations by leveraging the principles of both spiking neural networks and quantum computation. The Q-SpiRL framework outperformed tabular Q-learning, classical multilayer perceptrons, classical spiking neural networks, and quantum-enhanced multilayer perceptrons in terms of task completion, trajectory efficiency, and
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quantum-computingQuantum Entanglement’s Paradox Explained by Standard Theory Alone
Gregory D. Scholes, at Princeton University, and colleagues have revealed how a single wavefunction encapsulates a range of potential measurement results. The approach explains both state vector collapse and the seemingly paradoxical nonlocal correlations observed between separated quantum subsystems. Quantum correlations, even those violating Bell’s inequality, arise naturally from classical measurements, offering a thorough explanation within the existing framework of quantum theory without requiring additional assumptions or nonlinearities. Mapping entangled states via Cartesian product decomposition reveals subsystem properties Dr. Eleanor Rieffel and colleagues at Quantum AI employed a technique focused on dissecting the overall description of an entangled state, a complete description of a quantum system, into the individual components representing its separated subsystems. Mathematically mapping the combined system’s state vector onto a Cartesian product of vector spaces effectively created separate ‘blueprints’ for each subsystem. This decomposition isn’t merely a mathematical convenience; it reflects the physical separability of the subsystems even while acknowledging their quantum connection. The Cartesian product allows for the representation of the combined system’s state as a tensor product of individual subsystem states, facilitating analysis of each component’s contribution to the overall entanglement. This process accounted for contextual phase factors, subtle elements within the initial state that encode how measurements on one subsystem influence the others, and these factors are inherent to the system’s description but not immediately obvious. These phase factors are crucial because they determine the interference patterns that give rise to quantum correlations and are directly linked to the system’s evolution over time. Ignoring them would lead to an incomplete and inaccurate representation of the entangled state. The technique avoids assumptions
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quantum-computingBalancing Quasi-Bragg Regime and Velocity Selectivity in Quantum-Enhanced Atom Interferometry
--> Quantum Physics arXiv:2605.21643 (quant-ph) [Submitted on 20 May 2026] Title:Balancing Quasi-Bragg Regime and Velocity Selectivity in Quantum-Enhanced Atom Interferometry Authors:Christian Miguel Karres (1 and 2), Daniel Derr (1), Enno Giese (1) ((1) Technical University of Darmstadt, (2) Johannes Gutenberg University Mainz) View a PDF of the paper titled Balancing Quasi-Bragg Regime and Velocity Selectivity in Quantum-Enhanced Atom Interferometry, by Christian Miguel Karres (1 and 2) and 2 other authors View PDF HTML (experimental) Abstract:Spin squeezing in atomic ensembles enables atom interferometry with sensitivities below the shot-noise limit, but the associated entanglement is highly susceptible to loss, making imperfections in atom optics a central limitation. Bragg diffraction is an established technique for driving transitions between atomic momentum states and enables large-momentum transfer through higher-order diffraction while preserving the internal state. However, it is intrinsically limited by two competing mechanisms: short light pulses induce parasitic diffraction into off-resonant orders beyond an effective two-level description, while long pulses face velocity selectivity. We derive analytical expressions in a second-quantized framework for the atom optics and phase uncertainty of a Mach-Zehnder interferometer including these effects. We demonstrate that sub-shot-noise scaling is achieved only in a regime of intermediate pulse duration. Furthermore, we show that deleterious effects of higher-order diffraction are partially mitigated by optimizing the input quantum state. Comments: Subjects: Quantum Physics (quant-ph); Atomic Physics (physics.atom-ph) Cite as: arXiv:2605.21643 [quant-ph] (or arXiv:2605.21643v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.21643 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Christian Karres [view email] [v1] Wed, 20 May 2026 1
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quantum-computingConcatenating Algebraic Codes over High-Rate Quantum LDPC Codes
--> Quantum Physics arXiv:2605.21898 (quant-ph) [Submitted on 21 May 2026] Title:Concatenating Algebraic Codes over High-Rate Quantum LDPC Codes Authors:Adam Wills, Michael E. Beverland, Lev S. Bishop, Jay M. Gambetta, Patrick Rall, Vikesh Siddhu, Andrew W. Cross View a PDF of the paper titled Concatenating Algebraic Codes over High-Rate Quantum LDPC Codes, by Adam Wills and 6 other authors View PDF Abstract:Different quantum error correction schemes trade off overhead, error suppression, and hardware connectivity. Code concatenation can relax these tradeoffs by using an outer code whose non-local connectivity is supplied by logical operations of an inner code rather than directly by hardware. Prior works showed that this can reduce memory overhead for local low-rate inner codes such as the surface code. Here, we study concatenation over non-local, high-rate inner codes. Such inner codes experience correlated errors among the many logical qubits in a single codeblock. We handle this by treating each block as a single logical Galois qudit, enabling concatenation with algebraic outer codes with excellent parameters and, crucially, list decoders. In particular, we consider a memory system formed by concatenating quantum Reed-Solomon outer codes over the gross code. For fault-tolerant syndrome extraction, we develop a Galois qudit Shor scheme using "time-like" Reed-Solomon protection against measurement errors. Interestingly, a lightweight fault tolerance scheme, that would fail for qubits, works well for large-alphabet qudits, suggesting a very different theory of fault tolerance for such qudits. The whole protocol is optimised via improved bicycle instruction logical error rates, novel compilation strategies, and recent decoder post-selection rules. At uniform $10^{-3}$ physical noise, the concatenated gross code reaches the teraquop regime, which it previously could not access, with a lower space overhead than the $288$-qubit two-gross code, while offering several ad
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