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-computingOptimal Quantum Differential Privacy via Fisher Information Spectral Analysis
--> Quantum Physics arXiv:2605.24166 (quant-ph) [Submitted on 22 May 2026] Title:Optimal Quantum Differential Privacy via Fisher Information Spectral Analysis Authors:Justice Owusu Agyemang, Jerry John Kponyo, Elliot Amponsah, Godfred Manu Addo Boakye View a PDF of the paper titled Optimal Quantum Differential Privacy via Fisher Information Spectral Analysis, by Justice Owusu Agyemang and 3 other authors View PDF HTML (experimental) Abstract:The Quantum Fisher Information (QFI) metric governs a fundamental duality: it quantifies both how precisely a parameter can be estimated (metrology) and how distinguishable two quantum states are (privacy). We exploit this duality to establish a geometry-aware framework for quantum differential privacy (DP) that replaces isotropic depolarizing noise with direction-dependent noise aligned to the QFI eigenstructure of the quantum embedding. We prove six principal theorems: (1) the minimax-optimal mechanism concentrates the noise budget in the dominant QFI eigenmode, achieving $\varepsilon = (\Delta^2/2)\lambda_{\max}(1-c\gamma)$ with $O(d/\lambda_{\max})$ advantage; (2) mixed-state QFI decomposition reveals that dephasing in the adversary's basis $\textit{increases}$ accessible information, while misaligned-basis dephasing provides constructive privacy amplification from hardware noise; (3) a tight privacy $-$ utility uncertainty relation $\varepsilon \cdot (1 - F) \ge \frac{\Delta^2}{2}\frac{\operatorname{Tr}(F)}{d}$; (4) adaptive QFI estimation converging at $O(1/\sqrt{n})$ yields $1.92\times$ tighter bounds; (5) QFI-aligned composition saturates at $O(1)$ versus $O(k)$ for standard composition; and (6) hardware noise can be harnessed for privacy amplification. Adversarial vulnerabilities, Wasserstein guarantees, subspace projection, and a zero-knowledge audit protocol follow as corollaries. Results are validated on Qiskit Aer GPU simulations, IBM Quantum hardware (ibm_fez, 156 qubits), and against classical DP baselines, achiev
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quantum-computingScientists trained an AI model using an IBM quantum computer — and it answered questions correctly that the base model couldn't - Live Science
Scientists trained an AI model using an IBM quantum computer — and it answered questions correctly that the base model couldn't Live Science
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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-computingDid Amazon Just Deliver a Sweeping Blow to IonQ?
Amazon's (AMZN +1.29%) quiet engagement with IonQ (IONQ +10.21%) reflects the careful ways large companies may choose to explore new business opportunities. Rather than making a long-term commitment, the tech giant made a small investment in the quantum computing specialist, representing a tactical move within a broader strategy related to its cloud-based quantum computing services. Image source: The Motley Fool. Taking a look at Amazon's investment in IonQ According to 13F filings, Amazon took an initial stake in IonQ during the second quarter of 2025, acquiring 854,207 shares. Technology giants like Amazon don't typically buy shares in specialized companies without clear business alignment. I think the investment in IonQ allowed the company to signal potential interest for a collaboration with Amazon Web Services (AWS) while also gaining indirect exposure to advancements in trapped-ion quantum systems. Investing in IonQ could have been a way to strengthen ties in AWS' emerging quantum ecosystem without requiring Amazon to raise its already aggressive capital expenditure roadmap. At the end of the day, the scale of its IonQ investment remained modest relative to Amazon's liquidity resources, suggesting it served more as a bridge and a hedge for its internal developments rather than a core pillar of the company's quantum AI roadmap. ExpandNYSE: IONQIonQToday's Change(10.21%) $5.36Current Price$57.83Key Data PointsMarket Cap$20BDay's Range$53.97 - $61.1252wk Range$25.89 - $84.64Volume2.6MAvg Vol29MGross Margin-2879.52% Why Amazon may have been interested in IonQ Several factors probably motivated Amazon's investment in IonQ. First, the integration of IonQ's quantum hardware with AWS Braket, Amazon's managed service for quantum computing experiments, was likely core to the thesis. Developers can access IonQ's systems directly through AWS, creating a seamless experience for running hybrid quantum-classical workloads. In theory, this enhances Braket's appeal and could d
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quantum-computingWISER and Fraunhofer ITWM Advance Quantum AI for Industrial Applications
Insider Brief WISER and Fraunhofer ITWM studied the use of quantum machine learning for anomaly detection in industrial manufacturing systems. The collaboration evaluated Quantum Neural Networks for tasks such as pneumatic leak detection and rotating machinery fault analysis using industrial sensor data. The research explored how near-term quantum AI methods could support predictive maintenance and process optimization in industrial environments. PRESS RELEASE — At its core, the collaboration explored how emerging quantum computing methods can support anomaly detection in manufacturing, a critical task for identifying faults in complex production systems. By analyzing sensor data from industrial equipment, such approaches aim to detect irregularities at an early stage, helping to reduce downtime, improve quality control, and increase overall efficiency. The study focused on practical scenarios such as identifying pneumatic leaks and detecting faults in rotating machinery, illustrating how quantum-enhanced models could complement existing data-driven solutions in industry. Building on this application perspective, the team conducted a systematic evaluation of Quantum Neural Networks (QNNs), a class of machine learning models designed for near-term quantum hardware. The results show that QNNs can achieve competitive performance, including 87.77 percent accuracy in pneumatic leak detection and strong ROC-AUC performance on NASA bearing fault datasets. The study further analyzes key design choices such as data encoding strategies, highlighting binary and exponential encodings as effective trade-offs between model expressivity and trainability. The full technical details are available in the corresponding arXiv publication. »Quantum Neural Networks (QNNs), holds promise for integrating quantum principles into machine learning. However, a critical gap exists in understanding the practical limitat
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quantum-computingKipu Quantum Launches Hybrid Framework to Enable Offline Inference for Quantum Machine Learning
Kipu Quantum Launches Hybrid Framework to Enable Offline Inference for Quantum Machine Learning Kipu Quantum has released an off-line Digitized Quantum Feature Extraction (DQFE) pipeline that allows quantum-enhanced machine learning models to execute inference operations entirely on classical hardware. The architecture separates the quantum and classical processing loops, restricting quantum processor utilization to an initial, specialized training stage. By eliminating real-time Quantum Processing Unit (QPU) dependencies during active inference loops, the framework removes operational bottlenecks such as multi-user cloud queue latency and continuous hardware access costs. The system has been validated on IBM Quantum hardware, including the 156-qubit IBM Quantum Heron r2 processor, across multiple high-volume enterprise analytics use cases. Technical Architecture & Specifications / Operational Implementation The DQFE pipeline functions by processing a representative, stratified subsample—typically 20%—of the primary classical training dataset on physical superconducting quantum processors. The hardware encodes these input vectors into a spin-glass Hamiltonian using a digitized counterdiabatic driving protocol to map non-linear data multi-correlations into high-dimensional Hilbert spaces. Once these highly expressive quantum feature representations are extracted, they are transferred into a lightweight classical surrogate model trained via regularized Ridge regression. This surrogate model acts as a mathematical proxy, learning to reproduce the quantum feature mapping directly from raw classical inputs using a single matrix multiplication. The resulting production model executes with microsecond inference latency, integrates with standard MLOps pipelines, and bypasses live quantum execution overhead. Strategic Positioning & Ecosystem Integration The off-line framework is integrated into Kipu Quantum’s commercial Rimay product suite, accessible via the company
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quantum-computingSuperpositions Launches Cloud-Based Quantum Software Ecosystem and Automated Use-Case Library
Superpositions Launches Cloud-Based Quantum Software Ecosystem and Automated Use-Case Library Quantum computing startup Superpositions has launched its integrated product ecosystem and open-access Quantum Solutions Library to streamline the operationalization of industrial quantum-hybrid software. The platform addresses standard bottlenecks in corporate adoption, such as restricted multi-vendor hardware access, inadequate benchmark auditing, and the engineering overhead associated with mapping enterprise data to noisy intermediate-scale quantum (NISQ) devices. The company’s business model transitions from custom proof-of-concept (PoC) consulting toward a credit-based, non-lock-in software-as-a-service (SaaS) architecture. The cloud infrastructure connects classical programmers, financial analysts, and industrial engineers to managed backend processors through a tiered software stack. Technical Architecture & Specifications / Operational Implementation The software ecosystem consists of three interconnected layers designed to unify development pipelines. The foundational layer, Kit, is an open-source Python library optimized for hybrid quantum machine learning (QML) and combinatorial optimization with native multi-backend execution capabilities. The intermediate layer, Studio, is a browser-based integrated development environment (IDE) featuring an automated multi-agent AI copilot that translates plain-language business constraints into mathematical models like Quadratic Unconstrained Binary Optimization (QUBO) or Ising formulations. The system auto-generates executable code and schedules workloads across accelerated simulators or real Quantum Processing Units (QPUs) from IBM Quantum, IonQ, Rigetti, and IQM without requiring manual circuit re-configuration. The final layer, Enterprise, handles data ingestion pipelines for managed cloud services, enforcing rigid benchmarking against verified classical baselines. Strategic Positioning & Ecosystem Integration Th
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quantum-computingPasqal Selected as Finalist in XPRIZE Quantum Applications Competition
Insider Brief Pasqal was selected as a finalist in the XPRIZE Quantum Applications competition focused on real-world quantum computing use cases. The competition evaluates quantum algorithms addressing challenges in areas such as energy, climate, and healthcare. Pasqal will advance to the next evaluation stage, where teams will be assessed on measurable progress toward quantum advantage and practical impact. PRESS RELEASE — Pasqal, a global leader in neutral-atom quantum computing, today announced that it has been selected as a finalist in the XPRIZE Quantum Applications competition for its work toward reaching quantum advantage. Pasqal recently announced plans to go public through a business combination with Bleichroeder Acquisition Corp. II. XPRIZE Quantum Applications is a three-year global competition with a grand prize of $5 million, designed to accelerate the development of quantum computing algorithms capable of addressing real-world challenges. Sponsored by Google Quantum AI and the Geneva Science and Diplomacy Anticipator (GESDA), the competition highlights growing global engagement in quantum innovation and reflects the field’s increasing technical maturity and feasibility. Pasqal was selected from a highly competitive pool of 62 Wildcard Registration submissions, and is one of five teams selected to move forward. “Being named a Finalist in the XPRIZE Quantum Applications competition is a strong validation of Pasqal’s mission to turn quantum innovation into real-world value,” said Loïc Henriet, Chief Technology Officer at Pasqal. “This recognition reflects the strength of our science, the rigor of the independent judging process, and our focus on delivering measurable quantum advantage for applications that matter—across energy, climate, and human health.” Teams across the competition are addressing 11 pressing societal challenges, including clean energy, climate adaptation, and public health, demonstrating how quantum innovation can help advance the UN Su
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quantum-computingStatistical Quantum Phase Estimation: Extensions and Practical Considerations
--> Quantum Physics arXiv:2605.18876 (quant-ph) [Submitted on 15 May 2026] Title:Statistical Quantum Phase Estimation: Extensions and Practical Considerations Authors:Amit Surana, Brandon Allen View a PDF of the paper titled Statistical Quantum Phase Estimation: Extensions and Practical Considerations, by Amit Surana and Brandon Allen View PDF HTML (experimental) Abstract:We present several refinements and extensions of the statistical quantum phase estimation (SQPE) framework to address some of its key practical limitations, improving its applicability to realistic cases. Recently, a family of statistical approaches for QPE have been proposed where each run uses only a few ancillae and shorter circuits than standard QPE and thus is better suited for early fault-tolerant quantum computers that are qubit-and depth-limited. SQPE method within that family estimates the cumulative distribution function (CDF) associated with spectral density of the Hamiltonian for a given trial state by using its Fourier approximation and then identifies the first jump discontinuity of the CDF to determine the ground state energy (GSE) of the Hamiltonian. It relies on random compilation procedure based on linear combination of unitaries (LCU) decomposition of the Hamiltonian assuming positive Pauli weights and requires a good estimate of lower bound on the overlap between the trial and true ground state, both of which may be difficult to achieve in practice. We address these limitations by generalizing the random compilation procedure for negative Pauli weights and employing a changepoint detection method for determining GSE which does not rely on an estimate of this overlap. We also show that by exploiting symmetry of the Fourier series one can reduce number of circuit runs/samples by a factor of 2x while keeping the GSE estimation accuracy the same. We illustrate these new developments numerically via a quantum simulator in Qiskit. Comments: Subjects: Quantum Physics (quant-ph) Cite as
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quantum-computingSignatures of quantum noise in the operation of Deutsch's algorithm
--> Quantum Physics arXiv:2605.19047 (quant-ph) [Submitted on 18 May 2026] Title:Signatures of quantum noise in the operation of Deutsch's algorithm Authors:Małgorzata Strzałka, Katarzyna Roszak View a PDF of the paper titled Signatures of quantum noise in the operation of Deutsch's algorithm, by Ma{\l}gorzata Strza{\l}ka and 1 other authors View PDF HTML (experimental) Abstract:We use Deutsch's algorithm as a stand in for more complex quantum algorithms in order to determine how quantum properties of an environment manifest themselves in results that can be obtained on quantum computers. We model pure dephasing in two different ways; one keeps the full density matrix of the qubits and environments (quantum) while the other uses Kraus operators (classical). We find that a single run of the algorithm yields the same effect in both cases, but running the algorithm twice leads to stark differences. Taking correlations and interplay between different decoherence processes into account leads to a slowing of decoherence effects for balanced functions. For constant functions, the effect is much more pronounced, and there is a qualitative change in the dependence of measurement outcomes on decoherence. We present results obtained on one of the IBM Quantum processors, which fully reproduce the predicted effect regardless of the assumptions made in the derivation. We further illustrate the findings on NV center spin qubits, which show more complex behavior due to a small size of the environment. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.19047 [quant-ph] (or arXiv:2605.19047v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.19047 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Małgorzata Strzałka [view email] [v1] Mon, 18 May 2026 19:13:03 UTC (597 KB) Full-text links: Access Paper: View a PDF of the paper titled Signatures of quantum noise in the operation of Deutsch's al
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quantum-computingPrediction: Rigetti Computing Stock Will Be Worth This Much in 1 Year
Quantum computers are an incredible innovation. They use a concept called superposition to simulate several different solutions to a given problem at once, so they're more efficient than traditional computers at processing specific workloads, particularly in areas like science and cryptography. Rigetti Computing (RGTI 3.79%) has built some of the industry's most capable quantum computers, but they still produce relatively high error rates, making them impractical for solving many real-world problems. As a result, the company is struggling to generate meaningful revenue. Rigetti stock has plummeted 61% from last year's record high; here's where I predict it could be in 12 months. Image source: Getty Images. Rigetti's most powerful computer is now widely available Rigetti is unique because it built an entire in-house supply chain for its quantum computing business. It operates a fabrication facility, it created its own programming language called Quil, and it even launched its own cloud platform where it leases quantum computing capacity to enterprises for a fee. Therefore, Rigetti can bring new computers to market and commercialize them much faster than its competitors. During the first quarter of 2026, the company made its flagship Cepheus-1-108Q system widely available through its own cloud platform, but also through third-party platforms including Amazon Braket and Microsoft Azure Quantum, giving it unprecedented reach. ExpandNASDAQ: RGTIRigetti ComputingToday's Change(-3.79%) $-0.63Current Price$15.99Key Data PointsMarket Cap$5.5BDay's Range$15.46 - $16.7652wk Range$10.30 - $58.15Volume578KAvg Vol29MGross Margin-5945.49% Cepheus-1-108Q is the industry's largest multichip quantum computer. It features 108 qubits, so it offers 3 times the scale of Rigetti's previous Cepheus-1-36Q system. It also boasts a single-qubit gate fidelity of 99.9%, which means it only makes one error in every 1,000 quantum operations. That error rate still makes Cepheus-1-108Q impractical
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quantum-computingAramco and Pasqal Launch Commercial Quantum Computing as a Service Platform in Saudi Arabia
Aramco and Pasqal Launch Commercial Quantum Computing as a Service Platform in Saudi Arabia Aramco has partnered with neutral-atom quantum computing developer Pasqal to inaugurate Saudi Arabia’s first operational quantum computer. Housed at the Aramco data center in Dhahran, the deployment introduces the Middle East’s first commercial Quantum Computing as a Service (QCaaS) platform. This cloud-based infrastructure provides remote access to high-performance quantum hardware for international clients, regional enterprises, and academic institutions. The strategic integration aims to build local computational expertise and accelerate the deployment of quantum-hybrid software solutions aligned with the Saudi Vision 2030 economic framework. Technical Architecture & Specifications / Operational Implementation The newly active quantum computer is driven by a Pasqal Quantum Processing Unit (QPU) that controls 200 programmable qubits. The underlying architecture utilizes neutral-atom technology, manipulating individual atoms to execute complex optimization, simulation, and artificial intelligence algorithms. Following initial validation hardware testing completed in November 2025, the system has entered active service to process production-ready operational workloads. The core technical roadmap focuses on executing quantum-hybrid computational workstreams, where classical data center infrastructure is paired with the QPU to resolve high-value industrial algorithms beyond the capabilities of standalone classical hardware. Strategic Positioning & Ecosystem Integration Aramco operates as the foundational client for the QCaaS platform, establishing specialized workstreams to address specific industrial bottlenecks. Technical application areas include port logistics optimization, well placement, rig scheduling, and CO₂ storage optimization across the energy, materials, and industrial sectors. The domestic venture capital branch of Aramco, Wa’ed Ventures, initially finance
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quantum-computingPasqal and Aramco Launch QCaaS Platform for Saudi Quantum Computer - HPCwire
Pasqal and Aramco Launch QCaaS Platform for Saudi Quantum Computer HPCwire
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quantum-computingAramco and Pasqal launch Saudi Arabia's first Quantum Computer and Middle East's first commercial Quantum Computing as a Service platform - Yahoo! Finance Canada
Aramco and Pasqal launch Saudi Arabia's first Quantum Computer and Middle East's first commercial Quantum Computing as a Service platform Yahoo! Finance Canada
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quantum-computingAramco and Pasqal launch Saudi Arabia's first Quantum Computer and Middle East's first commercial Quantum Computing as a Service platform - PR Newswire
Aramco and Pasqal launch Saudi Arabia's first Quantum Computer and Middle East's first commercial Quantum Computing as a Service platform PR Newswire
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quantum-computingAramco and Pasqal launch commercial Quantum Computing as a Service platform - Aramco
Aramco and Pasqal launch commercial Quantum Computing as a Service platform Aramco
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quantum-computingClemson University Advances Quantum Software Research Through $650,000 Initiative Focused on Real-World Hardware Performance - The Quantum Insider
Clemson University Advances Quantum Software Research Through $650,000 Initiative Focused on Real-World Hardware Performance The Quantum Insider
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quantum-computingClemson University Advances Quantum Software Research Through $650,000 Initiative Focused on Real-World Hardware Performance
Insider Brief Clemson University is expanding South Carolina’s quantum computing capabilities through a $650,000 initiative supporting the Scalable High-Performance and Quantum Computing Systems Lab, with a focus on improving how quantum software performs on real hardware. The funding includes $250,000 for student-led quantum and machine learning research, $150,000 for multi-year Quantathon events, and $250,000 to establish a long-term statewide student quantum club network. The initiative builds on South Carolina’s earlier $15 million investment in quantum information science and aims to strengthen both the state’s technical research capacity and future quantum workforce pipeline. PRESS RELEASE — Clemson University is advancing South Carolina’s quantum research capacity through a $650,000 initiative supporting the Scalable High-Performance and Quantum Computing Systems Lab (ScaLab), an effort focused on improving how quantum programs are optimized and executed on real hardware. Led by Dr. Rong Ge, ScaLab focuses on improving how quantum software runs on real machines. Quantum computers operate very differently from traditional systems, and writing programs that perform efficiently on physical devices remains a central challenge in the field. The lab develops tools that help adapt software to the unique constraints of quantum hardware, improving reliability and performance in real-world settings. The project supports core research, talent development, and statewide capacity building. Of the total investment: $250,000 supports graduate and undergraduate research within ScaLab, enabling hands-on work in quantum computing and machine learning tied directly to active research outputs. $150,000 funds multi-year Quantathon events over a three-year period, creating structured, applied learning environments where students engage real computational challenges aligned with emerging quantum and hybrid systems research. $250,000 establishes a Statewide Student Quantum Club with
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quantum-computingAramco and Pasqal launch Saudi Arabia’s first quantum computer and Middle East’s first commercial Quantum Computing as a Service platform - Yahoo Finance Singapore
Aramco and Pasqal launch Saudi Arabia’s first quantum computer and Middle East’s first commercial Quantum Computing as a Service platform Yahoo Finance Singapore
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