Quantum Cloud Services: AWS Braket, Azure Quantum & IBM Quantum
Quantum cloud computing news: QCaaS platforms, AWS Braket, Azure Quantum, IBM Quantum Experience. Cloud quantum access & hybrid computing.
Quantum computing cloud services democratize access to quantum hardware, enabling researchers, enterprises, and developers to experiment with quantum processors without multi-million-dollar infrastructure investments.
Major global platforms include IBM Quantum with 20+ systems (5-1,000+ qubits); Amazon Braket providing hardware-agnostic access to IonQ, Rigetti, OQC, and D-Wave systems; and Microsoft Azure Quantum offering diverse hardware including IonQ, Quantinuum, and Rigetti.
India's Quantum Cloud Infrastructure
India's National Quantum Mission plans indigenous quantum cloud infrastructure development. The Foundation for QC Innovation at IISc Bengaluru will provide access to quantum computing resources as hardware matures. Until indigenous platforms are operational, the Department of Science and Technology facilitates cloud access to international quantum computers for Indian researchers.
The Andhra Pradesh Quantum Valley Tech Park, developed in partnership with IBM and TCS, will provide cloud access to an IBM Quantum System Two with 156-qubit Heron processor—the largest quantum computer in India. TCS will support development of algorithms and applications for Indian industry and academia through this facility.
The NQM targets making quantum computing resources accessible to startups, MSMEs, and researchers, with the quantum fabrication facilities at IISc Bengaluru and IIT Bombay providing prototyping and testing access.
quantum-computingQuantum Computing Stocks To Watch Now - February 10th - MarketBeat
Quantum Computing Stocks To Watch Now - February 10th Written by MarketBeatFebruary 10, 2026 ShareLink copied to clipboard. Image from MarketBeat Media, LLC. Key Points IonQ (IONQ), D‑Wave Quantum (QBTS), and Quantum Computing Inc. (QUBT) were among the highest dollar‑volume "quantum computing" stocks flagged by MarketBeat's screener as stocks to watch on Feb 10. Different technology approaches: IonQ provides general‑purpose quantum access via cloud platforms (AWS Braket, Azure Quantum, Google Cloud), D‑Wave offers its Advantage annealing systems plus Ocean software and Leap cloud service, and Quantum Computing Inc. focuses on integrated photonics with portable room‑temperature EQC machines, quantum RNG and authentication solutions. Investor caution: MarketBeat warns these stocks are a speculative, long‑horizon theme with high volatility and binary outcomes driven by scientific milestones, commercialization progress, partnerships and broader market sentiment. MarketBeat previews the top five stocks to own by March 1st. 3 Key Ways D-Wave Is Developing an Advantage in Quantum ComputingIonQ, D-Wave Quantum, and Quantum Computing are the seven Quantum Computing stocks to watch today, according to MarketBeat's stock screener tool. "Quantum computing stocks" are shares of companies whose businesses are significantly tied to developing, manufacturing, supplying, or commercializing quantum computers, related hardware (qubits, cryogenics), software, algorithms, or services — including pure-play quantum firms, component suppliers, and larger tech companies with quantum divisions. For investors, these stocks represent a speculative, long‑horizon investment theme with high volatility and binary outcomes driven by scientific milestones, commercialization progress, partnerships, and broader market sentiment. These companies had the highest dollar trading volume of any Quantum Computing stocks within the last several days. Get IonQ alerts:Sign UpIonQ (IONQ)IonQ, Inc. engages in th
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quantum-computingQuantum AI Shortcut Could Speed up Language Models with Reduced Complexity
Scientists are developing novel methods to improve sequence prediction, a crucial task in areas such as natural language processing and dynamical systems modelling. Alessio Pecilli and Matteo Rosati, both from the Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche at the Universit`a degli Studi Roma Tre, alongside et al., present a variational implementation of self-attention, termed Quantum Attention by Overlap Interference (QSA), which leverages quantum principles to predict future sequence elements. This research is significant because QSA achieves nonlinearity through state overlap interference and directly calculates a loss function as an observable expectation value, circumventing conventional decoding processes. Moreover, the team demonstrates that QSA exhibits potentially advantageous computational scaling compared to classical methods and successfully learns sequence prediction from both classical data and complex many-body quantum systems, establishing a trainable attention mechanism for dynamical modelling. Quantum self-attention via direct Renyi-1/2 entropy measurement Scientists have developed a novel quantum self-attention mechanism, termed QSA, that directly addresses computational bottlenecks within transformer architectures and large language models. This breakthrough focuses on the core self-attention operation, crucial for predicting sequential data by weighting combinations of past information. Unlike previous quantum approaches, the research realizes necessary non-linearity through interference of quantum state overlaps, directly translating a Renyi-1/2 cross-entropy loss into an expectation value measurable as an observable. This innovative design bypasses the need for complex decoding processes typically required to convert quantum predictions into classical outputs, streamlining the training procedure. Furthermore, the QSA naturally integrates a trainable data-embedding, establishing a direct link between quantum s
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quantum-computingQuantum Compilation Speeds up 100x, Bringing Practical Quantum Computers Closer
Researchers are tackling the challenge of efficiently translating complex quantum algorithms into instructions for near-term quantum hardware. Aaron Hoyt from University of Washington and Pacific Northwest National Laboratory, alongside Meng Wang and Fei Hua from Pacific Northwest National Laboratory, et al., present QASMTrans, a novel end-to-end quantum compilation framework designed for just-in-time deployment. This work is significant because QASMTrans achieves over 100x faster compilation speeds than existing tools like Qiskit on certain circuits, while maintaining comparable fidelity and uniquely offering direct integration with hardware control systems via pulse generation. By bridging the gap between logical circuits and physical implementation, and incorporating noise-aware optimisation and circuit space sharing, QASMTrans facilitates the development and execution of real-time adaptive quantum algorithms on current quantum processing units. Rapid Quantum Circuit Transpilation via Pulse-Level Gate Set Optimisation Scientists have unveiled QASMTrans, a high-performance quantum compiler designed to rapidly translate abstract quantum algorithms into device-specific control instructions. This C++-based framework achieves over 100x faster compilation than existing tools like Qiskit for certain circuits, enabling the transpilation of large, complex circuits in a matter of seconds. QASMTrans distinguishes itself by offering complete, end-to-end device-pulse compilation and direct integration with quantum control systems such as QICK, effectively bridging the gap between logical circuits and the underlying hardware. The research focuses on accelerating the process of transpilation, which converts high-level quantum circuits into a format compatible with the limitations of near-term quantum devices. By employing latency-aware Application-tailored Gate Sets at the pulse level, QASMTrans identifies critical sequences within a circuit and generates optimized pulse schedu
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quantum-computingQuantum Entanglement’s ‘no Signalling’ Rule Bends, but Doesn’t Break
Scientists are increasingly scrutinising the no-signalling principle, a cornerstone of Bell inequality and steering experiments, as experimental flaws can mimic violations beyond statistical fluctuations. Lucas Maquedano (Federal University of Paraná), Sophie Egelhaaf (University of Geneva), and Amro Abou-Hachem (Lund University, with et al. including Jef Pauwels and Armin Tavakoli) present extensions to local hidden variable and local hidden state theories, accommodating quantifiable signalling. Their research develops non-classicality tests applicable to these extended models, utilising both complete statistical analysis and corrections to established Bell and steering inequalities. This work is significant because it addresses apparent signalling in realistic scenarios, specifically demonstrating its applicability to data arising from processor imperfections and inefficient detectors. These violations, previously attributed to statistical fluctuations, can arise from subtle systematic effects present in realistic experimental setups. The work introduces extensions to local hidden variable and local hidden state theories, allowing for bounded and quantifiable amounts of signalling between entangled particles. This approach moves beyond simply enforcing no-signalling through data post-processing, instead explicitly relaxing classical models to incorporate a measurable signalling parameter. The study establishes methods for developing non-classicality tests applicable to these extended models, utilising both exact calculations based on complete statistical data and corrections to standard Bell and steering inequalities. These techniques were demonstrated using two scenarios known to exhibit apparent signalling: data obtained from an IBM quantum processor and post-selected data originating from inefficient detectors. By quantifying the permissible signalling, the research provides a means to distinguish genuine quantum non-classicality from artefacts introduced by ex
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quantum-computingQuantum Simulations Take a Leap Forward with Superconducting Circuits
Quantum computing promises to revolutionise several scientific and technological domains through fundamentally new ways of processing information. Laurin E. Fischer, affiliated with the Laboratoire de théorie et simulation des matériaux, Faculté des sciences et techniques de l’ingénieur, University of unspecified location and IBM Quantum, alongside colleagues, demonstrate significant progress in enabling large-scale digital quantum simulations using superconducting qubits. This research is particularly significant because it addresses a critical limitation in current quantum devices, imperfections that hinder practical advantage for complex problems in fields such as condensed matter physics and materials science. By exploring methods across the computational stack, including hardware innovations, noise modelling, error mitigation and algorithmic improvements, this work represents a crucial step towards extracting meaningful results from noisy quantum data and realising the full potential of quantum simulation. The thesis was presented on 28 October at the Faculty of Science and Engineering, Laboratory of Theory and Simulation of Materials, Doctoral Programme in Materials Science and Engineering for the degree of Doctor of Science by Laurin Elias Fischer. It was accepted on the proposal of the jury, with Professor Harald Brune as president, Professors Nicola Marzari and Ivano Tavernelli as thesis directors, Professor Zoë Holmes as rapporteur, Professor Zoltán Zimborás as rapporteur, and Professor Frank Wilhelm-Mauch as rapporteur. The work is documented as arXiv:2602.04719v1 [quant-ph] from February 2026. Advancing quantum simulation through hardware innovation, noise mitigation and algorithmic refinement promises to unlock previously intractable scientific challenges Scientists across condensed matter physics and materials science widely recognise the transformative potential of quantum computing. However, the realization of practical quantum advantage for problems
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quantum-computingQuantum Computing Speed-Up Achieved with New State Preparation Technique
Quantum state preparation represents a fundamental subroutine within numerous quantum algorithms, and minimising its circuit complexity remains a crucial endeavour. Giacomo Belli and Michele Amoretti, both from the Quantum Software Laboratory at the University of Parma, alongside Giacomo Belli, present a novel approach to optimise this process. Their research introduces a simplified algebraic decomposition that segregates the preparation of the real and complex components of a desired quantum state, demonstrably reducing circuit depth, total gate count, and complexity when ancillary qubits are employed. This improvement stems from utilising a single operator per uniformly controlled gate, contrasting with the three required by the original method, and establishes a significant advancement in the field by offering a more efficient pathway to prepare both dense and sparse quantum states, as validated through simulations using the PennyLane library. This work introduces a novel algorithm for quantum state preparation that demonstrably improves upon existing methods, specifically optimising the algorithm developed by Sun et al., which previously defined the asymptotically optimal bounds for this process. The breakthrough lies in a simplified algebraic decomposition, effectively separating the preparation of the real and complex components of the desired quantum state. This separation allows for the implementation of uniformly controlled gates using a single operator, a substantial reduction from the three operators required in the original decomposition. By leveraging the PennyLane library, the team implemented and rigorously tested this new algorithm in a simulated environment, assessing its performance across both dense and sparse quantum states, including those with physical relevance. The resulting circuits exhibit reduced circuit depth, a lower total gate count, and fewer controlled-NOT gates when utilising ancillary qubits. Performance comparisons against the wide
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quantum-computingQuantum Algorithm Finds Perfect Solutions to Complex Problems Beyond Classical Reach
Combinatorial optimisation presents a significant challenge for modern computation, with many real-world problems quickly exceeding the capabilities of classical algorithms. Kate V. Marshall, Daniel J. Egger, and Michael Garn, alongside their colleagues at IBM Quantum and The Hartree Centre, demonstrate a novel approach utilising quantum-enhanced Markov chain Monte Carlo (QeMCMC) to tackle these complex problems. Their research combines on-device sampling with established techniques like warm-starting and parallel tempering, offering a pathway towards solving intractable instances of combinatorial optimisation with near-term quantum hardware. The team empirically validates their algorithm by successfully recovering global optima for Maximum Independent Set (MIS) problems with up to 117 decision variables on a 117-qubit quantum processor, and provides initial evidence suggesting a potential scaling advantage over classical methods for this practically relevant problem, which impacts fields ranging from financial modelling to molecular biology. This work demonstrates a novel approach to combinatorial optimization by integrating quantum-enhanced Markov chain Monte Carlo (QeMCMC) with techniques like warm-starting and parallel tempering. The research offers a pathway to tackling complex problems that currently challenge even the most advanced classical solvers. The core innovation lies in the synergistic combination of QeMCMC, which leverages quantum mechanics to improve sampling efficiency, with warm-starting and parallel tempering, classical techniques used to guide the optimization process. This hybrid approach allows the algorithm to explore the solution space more effectively, converging on optimal solutions faster than purely classical methods. For the largest instance tested, with 117 decision variables, the quantum hardware experiments converged in fewer iterations compared to classical simulations, suggesting that limitations in classical simulation techniques
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quantum-computingQuantum-enhanced Markov Chain Monte Carlo for Combinatorial Optimization
--> Quantum Physics arXiv:2602.06171 (quant-ph) [Submitted on 5 Feb 2026] Title:Quantum-enhanced Markov Chain Monte Carlo for Combinatorial Optimization Authors:Kate V. Marshall, Daniel J. Egger, Michael Garn, Francesca Schiavello, Sebastian Brandhofer, Christa Zoufal, Stefan Woerner View a PDF of the paper titled Quantum-enhanced Markov Chain Monte Carlo for Combinatorial Optimization, by Kate V. Marshall and 6 other authors View PDF HTML (experimental) Abstract:Quantum computing offers an alternative paradigm for addressing combinatorial optimization problems compared to classical computing. Despite recent hardware improvements, the execution of empirical quantum optimization experiments at scales known to be hard for state-of-the-art classical solvers is not yet in reach. In this work, we offer a different way to approach combinatorial optimization with near-term quantum computing. Motivated by the promising results observed in using quantum-enhanced Markov chain Monte Carlo (QeMCMC) for approximating complicated probability distributions, we combine ideas of sampling from the device with QeMCMC together with warm-starting and parallel tempering, in the context of combinatorial optimization. We demonstrate empirically that our algorithm recovers the global optima for instances of the Maximum Independent Set problem (MIS) up to 117 decision variables using 117 qubits on IBM quantum hardware. We show early evidence of a scaling advantage of our algorithm compared to similar classical methods for the chosen instances of MIS. MIS is practically relevant across domains like financial services and molecular biology, and, in some cases, already difficult to solve to optimality classically with only a few hundred decision variables. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2602.06171 [quant-ph] (or arXiv:2602.06171v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.06171 Focus to learn more arXiv-issued DOI via DataCite (pending r
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quantum-computingWorkshop on Quantum Computing and Quantum Information at UCNC 2026
Workshop on Quantum Computing and Quantum Information at UCNC 2026 Acronym: QCIC@UCNC 2026Dates: Sunday, February 8, 2026Web page: Workshop on Quantum Computing and Quantum Information at UCNC 2026Submission deadline: Sunday, April 26, 2026Call for Papers: Workshop on Quantum Computing and Quantum Information Trieste, Italy, June 2026 Satellite event to UCNC 2026, Trieste, Italy, June 22-26, 2026 (https://ucnc2026.units.it/ ) The workshop will focus on discussions within its thematic scope of quantum computing and quantum information theory. Topics of interest include but are not limited to: quantum sensing, quantum materials science, quantum hardware and architectures, quantum error correction, quantum cryptography and quantum communication, quantum algorithms, quantum optimization, quantum AI, quantum bioinformatics and other relevant experimental and theoretical topics. Submissions including non-finished ideas to raise discussion are possible and indeed welcomed. In addition, well-crafted tutorials on specific topics can be accepted as contributions. Authors are cordially welcome to send their contributions to Zornitza Prodanoff (email: zprodano@unf.edu) in pdf format. All contributions will be peer-reviewed. An author of an accepted contribution is assumed to present the contribution orally at the workshop. Schedule Submission deadline: April 26, 2026 Author notification: May 7, 2026 Workshop organizers: Mika Hirvensalo (University of Turku, Finland) Zornitza Prodanoff (University of North Florida) Log in or register to post comments
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quantum-computingPrecise Control of Quantum Coherence via Classical Mixing Parameters (R²>0.99) [OC]
https://imgur.com/a/Cw5rSu0 Experimental results demonstrating precise, linear control over quantum coherence through classical mixing parameter (λ) sweeps from pure quantum states (λ=0) to fully classical behavior (λ=1). **Key Findings:** • **Normalized Amplitude:** Linear decay with R² = 0.9987, showing predictable coherence degradation • **XX Correlator:** Near-perfect linear relationship (R² = 1.00000) across the full λ range • **T2 Coherence Time:** Measured via XBASIS protocol, demonstrates expected exponential decay • **Residual Analysis:** Minimal deviation from theoretical predictions, confirming model accuracy **Technical Details:** Results obtained using IBM quantum hardware (Qiskit), sweeping λ across 10 points from 0.0 to 1.0. Classical mixing implemented as ρ_mixed = (1-λ)ρ_quantum + λρ_classical, allowing continuous tuning between pure quantum superposition and classical mixed states. The near-perfect linear relationships suggest quantum coherence behaves more predictably than often assumed, with implications for error mitigation strategies and hybrid quantum-classical algorithms. **Implications:** - Benchmarking quantum devices - Adaptive error mitigation techniques - Hybrid algorithms with dynamic quantum/classical adjustment - Understanding decoherence mechanisms (Also posted on r/Futurology for discussion of future applications) submitted by /u/KevinMonette [link] [comments]
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quantum-computingBreaking ground on India’s quantum future - ibm.com
Quantum ResearchBlogBreaking ground on India’s quantum futureConstruction begins on India’s Quantum Valley Tech Park as the nation grows its quantum education initiatives and prepares for its first IBM quantum computer.Date7 Feb 2026AuthorsAnupama RayRobert DavisTopicsCommunityNetworkShare this blogBlog summary: India has begun construction on the Quantum Valley Tech Park in Amaravati, the future home of the country’s first IBM quantum computer. The ground breaking arrives as a nationwide push to grow India’s quantum workforce is accelerating. For example, one free online quantum computing course co-created by IBM has already surpassed 168,000 enrollments for 2026. While construction is under way, tech park members will have access to IBM quantum computers over the cloud thanks to a collaboration between IBM and India’s Tata Consultancy Services (TCS). India takes a bold step toward scaling its quantum workforce this week as the Government of Andhra Pradesh, a State in southern India, begins construction on Quantum Valley Tech Park in the capital city of Amaravati. Quantum Valley Tech Park will soon host India’s first IBM quantum computer, and tech park members already enjoy access to IBM’s cloud-based quantum computers thanks to a partnership between IBM and India’s Tata Consultancy Services (TCS), first announced last spring. These initiatives are bringing renewed national focus to India’s ongoing efforts in quantum education and workforce development. According to a report published by the Government of India’s apex policy think tank NITI Aayog (National Institution for Transforming India) in December, India will need to train approximately 100,000 quantum developers to secure its place as a quantum computing leader in the 2030s, a decade that will be shaped by the emergence of large-scale, fault-tolerant quantum computing. The message is clear: India’s long-term competitiveness in quantum computing will hinge on the strength of its talent pipeline. “With Quantum
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quantum-computingThe quantum era is coming. Are we ready to secure it? - blog.google
The quantum era is coming. Are we ready to secure it? Feb 06, 2026 · Share x.com Facebook LinkedIn Mail Copy link We’re issuing a call to action to secure the quantum computing era and outlining our own commitments on post-quantum cryptography Kent Walker President of Global Affairs, Google & Alphabet Hartmut Neven Founder and Lead, Google Quantum AI Share x.com Facebook LinkedIn Mail Copy link Your browser does not support the audio element. Listen to article This content is generated by Google AI. Generative AI is experimental [[duration]] minutes Voice Speed Voice Speed 0.75X 1X 1.5X 2X The world is on the threshold of solving impossible problems in drug discovery, materials science, energy, and beyond.That’s because of quantum computers — computers capable of solving problems that even the most powerful classical supercomputers can’t. They’re able to identify and consider different options at the same time. Concerningly, their unique ability to unravel scientific mysteries will also allow them to bypass our current digital locks, like the public-key cryptosystems that protect things like bank transfers, private chats, trade secrets and even classified information.To put that plainly: The encryption currently used to keep your information confidential and secure could easily be broken by a large-scale quantum computer in coming years.And while we’re not there yet, malicious actors are not waiting until a Cryptographically Relevant Quantum Computer (CRQC) is ready. They are likely already carrying out “store now, decrypt later” attacks and collecting encrypted data, just waiting for the day when a quantum computer can unlock it. So what do we do about that? In short: Get ready. Over the last decade, quantum computing research has reduced by orders of magnitude the estimated resources required to solve problems like breaking 2048-bit RSA encryption (left) and simulating useful molecules (right). Today, we are sharing an update to our work to keep users safer in
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quantum-computingQuantum Kernel Methods Show Competitive Radar Classification with 133-Qubit IBM Processor
Researchers are increasingly exploring quantum machine learning for complex signal processing tasks, and this study investigates the practical application of quantum kernel methods to radar micro-Doppler classification. Vikas Agnihotri, Jasleen Kaur from the National Institute of Technology, Rourkela, and Sarvagya Kaushik from the Indian Institute of Technology, Dhanbad, et al., demonstrate a Quantum Support Vector Machine (QSVM) capable of classifying aerial targets from radar signatures, even with the limitations of current noisy intermediate-scale quantum (NISQ) hardware. By combining classical feature extraction with quantum kernel encoding and evaluating performance on both simulators and IBM quantum processors, this work offers a crucial assessment of the feasibility and challenges of deploying quantum algorithms for real-world radar applications, potentially paving the way for more efficient and accurate target recognition systems. The research team extracted classical features and reduced their dimensionality using Principal Component Analysis (PCA) to facilitate efficient quantum encoding. Reduced feature vectors were then embedded into a quantum kernel-induced feature space via a fully entangled ZZFeatureMap before classification using a kernel-based QSVM. This reduction in dimensionality is crucial for efficient quantum processing and encoding of complex radar signals. The study systematically investigated the impact of noise, decoherence, and measurement shot count on quantum kernel estimation, identifying improved stability and fidelity on the newer Heron r2 architecture. By mapping micro-Doppler patterns into an expanded quantum state space, the classifier can more easily separate subtle differences in target dynamics. This work provides a comprehensive comparison between simulator-based and hardware-based QSVM implementations, highlighting both the feasibility and current limitations of deploying quantum kernel methods for practical radar signal class
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quantum-computingQuantum Circuit Optimization by Graph Coloring
AbstractThis work shows that minimizing the depth of a quantum circuit composed of commuting operations reduces to a vertex coloring problem on an appropriately constructed graph, where gates correspond to vertices and edges encode non-parallelizability. The reduction leads to algorithms for circuit optimization by adopting any vertex coloring solver as an optimization backend. The approach is validated by numerical experiments as well as applications to known quantum circuits, including finite field multiplication and QFT-based addition.Featured image: A quantum circuit of commuting gates (CZ and CCZ) is reduced to a graph-coloring instance. After solving the coloring problem, the solution is mapped back to an optimized circuit.Popular summaryOptimizing logic circuits is an active area of research in the quantum computing community. While the topic has roots in computational questions—such as how small a circuit can be—it is also practically relevant: a handful of these algorithms are already integrated into real-world software stacks such as Qiskit and PennyLane. Building on this line of work, it is shown that a certain class of quantum circuits, referred to here as commuting circuits, can be reduced to the vertex coloring problem on a graph. This reduction immediately suggests optimizing commuting circuits not as a circuit problem but as a graph problem, where a long-standing literature offers tools that may translate into practical improvements. Two well-known graph coloring solvers are evaluated, demonstrating the effectiveness of the approach. These findings are used to improve previously known quantum circuits for arithmetic operations.► BibTeX data@article{Lee2026quantumcircuit, doi = {10.22331/q-2026-02-06-1996}, url = {https://doi.org/10.22331/q-2026-02-06-1996}, title = {Quantum {C}ircuit {O}ptimization by {G}raph {C}oloring}, author = {Lee, Hochang and Jeong, Kyung Chul and Kim, Panjin}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur
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quantum-computingQASMTrans: An End-to-End QASM Compilation Framework with Pulse Generation for Near-Term Quantum Devices
--> Quantum Physics arXiv:2602.05154 (quant-ph) [Submitted on 5 Feb 2026] Title:QASMTrans: An End-to-End QASM Compilation Framework with Pulse Generation for Near-Term Quantum Devices Authors:Aaron Hoyt, Meng Wang, Fei Hua, Chunshu Wu, Chenxu Liu, Muqing Zheng, Samuel Stein, Drew Rebar, Yufei Ding, Travis S. Humble, Ang Li View a PDF of the paper titled QASMTrans: An End-to-End QASM Compilation Framework with Pulse Generation for Near-Term Quantum Devices, by Aaron Hoyt and 10 other authors View PDF Abstract:QASMTrans is a lightweight, high-performance, C++-based quantum compiler that bridges abstract quantum algorithms to device-level control and is designed for just-in-time (JIT) deployment on QPU testbeds with tightly integrated FPGAs or CPUs. We focus on achieving fast transpilation times on circuits of interest, we find more than 100x faster compilation than Qiskit in some circuits with similar circuit quality, enabling transpilation of large, high-depth circuits in seconds. Unlike existing tools, QASMTrans offers end-to-end device-pulse compilation and direct quantum control integration with QICK, closing the gap between logical circuits and hardware control enabling closed-loop optimization. QASMTrans supports latency-aware Application-tailored Gate Sets (AGS) at the pulse level, identifying high-impact gate sequences on the circuit critical path and synthesizing optimized pulse schedules using pre-defined robust circuit ansatz. Validated through integrated QuTiP pulse-level simulation, this is found to significantly reduce execution latency and can improve final-state fidelity by up to 12% in some tested circuits. QASMTrans further implements device-aware, noise-adaptive transpilation that uses device calibration data for circuit placement on high-quality qubits and can focus on the circuit critical path to reduce transpilation-pass time while maintaining comparable fidelity. Additionally, it introduces circuit space sharing via calibration-aware device part
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quantum-computingWarsaw Quantum Computing Group, Episode LXXIV
Warsaw Quantum Computing Group, Episode LXXIV Dates: Friday, February 6, 2026Organising group(s): Quantum AI FoundationWeb page: Warsaw Quantum Computing Group, Episode LXXIVRegistration deadline: Wednesday, February 18, 2026Submission deadline: Wednesday, February 18, 2026We've opened registration for the (online) Episode LXXIV of the Warsaw Quantum Computing Group meetups! On 19.02.2026, at 18:00 UTC+1, Dr Emil Żak will give a talk: "Simulating high-accuracy nuclear motion Hamiltonians in discrete variable representation using Walsh-Hadamard QROM with fault-tolerant quantum computers". Sign up by 18.02 (EOD UTC+1): https://docs.google.com/forms/d/e/1FAIpQLSd8ZuGyAd4WSjo8vGNgtnw4w1DLET4e... Details: https://www.qaif.org/events/warsaw-quantum-computing-group/next-meeting Log in or register to post comments
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quantum-computingWho’s News: Leadership Updates at IQM, IonQ, Q.ANT, Haiqu, and More
Who’s News: Leadership Updates at IQM, IonQ, Q.ANT, Haiqu, and More IQM Quantum Computers has transitioned to a single-CEO leadership model to support its next phase of global expansion. Dr. Jan Goetz, co-founder and former Co-CEO, has been appointed sole CEO effective January 1, 2026. Additionally, Dr. Søren Hein joins as Chief Operating Officer and Deputy CEO, bringing over 30 years of experience in tech leadership and deep-tech investment. Former Co-CEO Mikko Välimäki will continue to support the company as an advisor through March 2026. The full announcement is available here. IonQ has expanded its senior technical leadership to accelerate its quantum networking and hardware roadmaps: Domenico Di Mola has joined as Senior Vice President of Engineering, Quantum Networking, Sensing & Security (QNSS). Di Mola will lead the strategy for next-generation quantum-secure networking and distributed sensing architectures. His welcome announcement is available here. Dr. Tom Harty, following IonQ’s acquisition of Oxford Ionics, has moved into a key leadership role as Chief Technology Officer (CTO). Harty, an Oxford-trained physicist and world-record holder in quantum gate fidelity, will drive the integration of ion-trap-on-a-chip technology into IonQ’s commercial roadmap. His announcement is available here. Q.ANT has strengthened its management team with two executive hires focused on scaling its photonic computing platform: Utz Bacher has been named Vice President Software. Formerly with IBM Quantum, Bacher will lead the development of the software stack required to make photonic co-processing practical for AI and HPC. The news release is available here. Kim Fischer joins as Vice President of Marketing and Communications. Fischer brings over 20 years of experience in brand development and will focus on Q.ANT’s global market positioning and investor relations. Her announcement is available here. Haiqu has appointed Dr. Antonio Mei as Lead Product Manager. Mei, the forme
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quantum-computingInfleqtion Accelerates Quantum for Oncology as Q4Bio Challenge Enters Phase Three
Backed by Wellcome Leap program funding and selected to advance to final-stage demonstrations, Infleqtion moves forward with quantum-enabled biomarker discovery for precision oncology Infleqtion, a global leader in quantum sensing and quantum computing powered by neutral-atom technology, announced that its quantum software team, together with collaborators at the University of Chicago (UChicago) and Massachusetts Institute ... Read more The post Infleqtion Accelerates Quantum for Oncology as Q4Bio Challenge Enters Phase Three appeared first on Infleqtion.
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quantum-computingQuantum AI Generates Images with a Novel Hybrid Computer Architecture
Researchers are tackling the limitations of quantum generative models in complex image creation, specifically addressing challenges in scalability and representing multi-modal distributions. Jeongbin Jo, Santanam Wishal from Asia Cyber University, and Shah Md Khalil Ullah from Khulna University of Engineering and Technology, alongside Shan Kowalski from Eleven Dimension LLC and Dikshant Dulai, present a Hybrid Quantum-Classical U-Net architecture incorporating Adaptive Non-Local Observables (ANO). This innovative approach compresses classical data into a quantum latent space and employs trainable observables to capture non-local features, thereby improving generative performance. Their work demonstrates the successful generation of structurally coherent images from the MNIST dataset, suggesting a viable route towards overcoming mode collapse and advancing quantum-enhanced image generation despite current hardware restrictions. By leveraging trainable observables, the model effectively complements classical computation and improves generative performance. Central to this breakthrough is the exploration of Skip Connections, which play a crucial role in preserving semantic information throughout the reverse diffusion process. Experimental validation using the full MNIST dataset, encompassing digits zero through nine, demonstrates the architecture’s capacity to generate structurally sound and readily identifiable images across all classes. While current quantum hardware imposes constraints on achievable resolution, the findings establish a viable pathway for mitigating mode collapse and boosting generative capabilities within the constraints of Noisy Intermediate-Scale Quantum (NISQ) technology. The proposed system employs a quantum diffusion model, building upon classical diffusion processes that progressively add noise to data before learning to reverse this process for image creation. The quantum component introduces a forward process defined by a depolarizing channe
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quantum-computing2 Quantum Computing Stocks to Buy Hand Over Fist in February - fool.com
By Leo Sun – Feb 4, 2026 at 1:05PM ESTKey PointsD-Wave uses its quantum systems to help companies optimize their workflows.IonQ could shrink quantum computers with its trapped-ion chips.Both companies could grow much larger as the quantum market expands.We’re bullish on these 10 stocks ›NYSE: QBTSD-Wave QuantumMarket Cap$8.0BToday's Changeangle-down(-10.70%) $2.29Current Price$19.11Price as of February 4, 2026 at 1:59 PM ETD-Wave Quantum and IonQ could be excellent plays on the nascent market.Quantum computers could represent the next leap forward for the tech sector. Unlike classical computers, which still store data as binary bits of zeros and ones, quantum computers can store those zeros and ones simultaneously in a quantum state as qubits. That difference enables quantum computers to crunch more data and perform specific tasks much faster than their classical counterparts, but they're also bigger, pricier, and less power-efficient. However, Fortune Business Insights expects the quantum computing market to expand at a 34.8% CAGR from 2025 to 2032 as companies roll out more sophisticated, cost-efficient systems. Let's examine two of those companies -- D-Wave Quantum (QBTS 10.70%) and IonQ (IONQ 10.89%) -- and see why they might be worth buying in February. Image source: Getty Images. What do D-Wave and IonQ do? D-Wave accelerates electrons in both clockwise and counterclockwise directions through superconducting loops to achieve a quantum state. These electron-powered systems are simpler and cheaper to manufacture than other types, but they can be expensive to operate and maintain because they require cryogenic refrigeration. D-Wave's systems are designed explicitly for quantum annealing, a process that helps organizations optimize their workflows by identifying the ones which consume the least power. It also designs its own QPUs and Advantage quantum systems, and provides quantum computing as a service through its cloud-based Leap platform. ExpandNYSE:
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