Quantum Computing Finance & Banking: Portfolio Optimization & Risk Analysis
Quantum finance news: JPMorgan, Goldman Sachs quantum banking. Portfolio optimization, risk modeling, Monte Carlo & algorithmic trading.
Financial services represent the largest commercial opportunity for near-term quantum computing, with institutions developing quantum algorithms for portfolio optimization, risk analysis, derivative pricing, and fraud detection. The sector's mathematical foundations in optimization and stochastic modeling align naturally with quantum computational advantages.
High-value use cases include portfolio optimization using quantum algorithms to solve mean-variance optimization across thousands of assets; risk analysis and Monte Carlo simulations where quantum amplitude estimation offers quadratic speedup; and derivative pricing for path-dependent options requiring high-dimensional integration.
India's Banking and Financial Services Quantum Landscape
India's banking and financial services sector, with over $2.5 trillion in assets, represents a significant potential market. The National Quantum Mission includes financial applications within its quantum computing applications scope. The Reserve Bank of India (RBI) and Securities and Exchange Board of India (SEBI) monitor quantum computing implications for market infrastructure and security.
Tata Consultancy Services (TCS) partners with IBM and the Andhra Pradesh government to deploy India's largest quantum computer at the Quantum Valley Tech Park in Amaravati, with applications including financial optimization. TCS develops quantum algorithms for portfolio optimization, risk modeling, and fraud detection. Infosys explores quantum computing through its Quantum Living Labs (QLL), offering advisory and proof-of-concept services with demonstrated capabilities in logistics, finance, cybersecurity, and healthcare.
The NQM targets developing quantum machine learning and optimization algorithms applicable to financial services, with commercial deployment expected as hardware matures toward the 50-1000 qubit range.
quantum-computingQUDORA Partners with QAI to Bring Ion-Trap Quantum Computing to South Korea
Insider Brief Press release – QUDORA Technologies GmbH (“QUDORA“), a European full-stack ion-trap quantum computing company, today announced the signing of a Memorandum of Understanding (MOU) with Korean quantum AI specialist QAI Co., Ltd. (“QAI”) to pursue the deployment of QUDORA’s ion-trap quantum computing technology in Korea and demonstrate hybrid AI-Quantum services for a wide […]
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quantum-computingWhite House Quantum Summit Details Over $2.2 Billion in Support for 2028 Fault-Tolerant Computing
The White House hosted the Summit on American Quantum Innovation on July 7, 2026, to begin implementing President Trump’s June 22 Executive Orders on quantum technologies. Federal agencies announced significant funding, including over $2 billion in Commerce Department incentives and up to $200 million from the Defense Innovation Unit for quantum sensing. The Department of Energy is tasked with delivering a fault-tolerant, scientifically relevant quantum computer by 2028, supported by initiatives from the NSF, NSA, and NIST focused on research, supply chains, and manufacturing. These efforts aim to build a resilient US quantum industrial base through public-private partnerships and workforce development. The post White House Quantum Summit Details Over $2.2 Billion in Support for 2028 Fault-Tolerant Computing appeared first on The Qubit Report.
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quantum-computingSEALSQ and GlobalFoundries Form Alliance to Develop Post-Quantum Semiconductor Blocks and Cryogenic CMOS Infrastructure
SEALSQ and GlobalFoundries Form Alliance to Develop Post-Quantum Semiconductor Blocks and Cryogenic CMOS Infrastructure Post-quantum hardware engineer SEALSQ Corp (Nasdaq: LAES) and foundry group GlobalFoundries (Nasdaq: GFS) have signed a strategic Memorandum of Understanding (MoU) to co-develop secure semiconductor platforms, post-quantum cryptography (PQC) IP, and cryogenic silicon control layers. The development track links GlobalFoundries’ commercial Complementary Metal-Oxide-Semiconductor (CMOS) fabrication processes and bulk manufacturing volume with SEALSQ’s hardware-based certified security cores and PQC-ready root-of-trust modules. The joint initiative focuses on moving quantum computing hardware out of boutique lab setups by manufacturing essential system control units within established, high-volume semiconductor cleanrooms. [ SEALSQ - GlobalFoundries Alliance Matrix ] Manufacturing Hub ──► GlobalFoundries high-volume U.S. and European fabrication facilities. Hardware IP Module ──► Hard macro certified PQC blocks engineered with MIPS architecture. Cryogenic Engine ──► CryoCMOS ASICs for sub-Kelvin quantum processing unit (QPU) control. Sovereign Mandate ──► Secure, traceable supply chain alignment supporting U.S. and European policies. The corporate partnership targets three primary technological segments: Certified PQC Security IP Integration: In collaboration with MIPS (a GlobalFoundries subsidiary), the engineering groups will design pre-certified PQC security IP hard macro blocks and Chiplet Hardware Security Module (CHSM) components. These functional blocks act as hardware-based roots of trust for Secure Enclaves, enabling semiconductor developers to embed hardware-level quantum-resistant protection directly during the initial silicon layout phase rather than implementing it as a post-fabrication software layer. Cryogenic CMOS (CryoCMOS) Architectures: Building on SEALSQ’s quantum ASIC design track and GlobalFoundries’ dedicated Quantum Technology S
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quantum-computingUniversity of Augsburg Team Designs Valence Bond Embeddings for Deep Chemistry Simulations
Scientists at the University of Augsburg have developed a new methodology addressing a fundamental challenge in quantum chemistry: the accurate and efficient simulation of large molecular systems. Francisco Javier del Arco Santos and Jakob S. Kottmann have combined hybrid Fermionic-Bosonic encodings with Quantum Valence Bond Theory to construct quantum circuits capable of representing more complex molecules than previously achievable, offering a potential pathway towards resolving bottlenecks in quantum computation and expanding the scope of variational quantum eigensolvers. Hybrid encoding and Quantum Valence Bond Theory expand accessible molecular simulation scales A six-fold increase in the size of molecular systems simulated using variational quantum eigensolvers has been demonstrated, significantly exceeding the limitations inherent in traditional active space methods. Published on June 26, this advancement facilitates the simulation of chemically relevant systems that were previously intractable due to computational constraints and the inherent limitations of current quantum hardware. Conventional quantum chemistry calculations often struggle with molecules containing more than a few dozen electrons, owing to the exponential scaling of computational resources with system size. The University of Augsburg researchers overcame this hurdle by strategically combining hybrid Fermionic-Bosonic encodings with Quantum Valence Bond Theory to systematically construct quantum circuits, establishing a clear and direct relationship between the chosen encoding scheme and the resulting electronic structure representation. This allows for a more nuanced and controlled approach to quantum simulation. Quantum circuits now provide novel avenues for simulating molecular properties, circumventing the limitations of existing techniques and opening possibilities for more intricate chemical investigations. The core innovation lies in achieving a more compact and flexible representatio
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quantum-computingOhio University Team Proposes Bivariate Bicycle Codes for Low-Overhead Error Correction
Current quantum computers are susceptible to errors which impede network performance, and existing correction techniques demand many qubits to function effectively. Alejandro Rosales and Animesh Yadav at Ohio University have developed a method to improve the reliability of QCNNs, a type of computer program that combines the strengths of quantum processing with image recognition techniques, similar to how facial recognition software works. Quantum computers are prone to errors, hindering the performance of these networks; existing error correction methods require a substantial number of qubits, creating a key obstacle to progress. This new technique employs a distance-4 code, offering a constant encoding rate and linear code distance, and represents a step towards practical quantum machine learning. Bivariate bicycle error correction enables substantial gains in quantum convolutional neural Previously, such networks failed to converge at all without error correction. This advancement addresses the vital issue of noise affecting near-term quantum devices, severely limiting the performance of QCNNs, and also reduces the substantial qubit overhead associated with established methods like surface codes. Integrating a constant-overhead QEC protocol with QCNNs provides a viable path towards practical quantum machine learning applications. The error threshold of 0.3% allows for sustained performance even with additional qubit requirements for error correction. Simulations utilising realistic noise sources demonstrated the BB code’s ability to maintain a constant encoding rate and linear code distance, essential for scaling to larger QCNNs; the team also benchmarked their approach against a feed-forward neural network used for error correction. Bivariate bicycle codes enhance stability in near-term quantum convolutional neural networks Quantum convolutional neural networks promise potential speedups for complex tasks like image recognition, but their inherent instability of
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quantum-computingGoogle Cuts Surface Code Error Rate to 7.72 × 10−4 With RL
Google Quantum AI researchers report achieving a logical error rate of 7.72 × 10−4 using the surface code, a crucial step toward stable quantum computation. The team unified calibration with computation by repurposing quantum error detection events, typically used for correction, as a learning signal for a reinforcement learning agent. This allows the quantum computer to continuously adjust its control parameters during computation, improving the logical stability of the surface code 3.5-fold against injected drift with complementary decoder steering. Numerical simulations suggest this framework’s optimization speed remains consistent even as quantum codes scale to include over a thousand control parameters, potentially enabling larger, more powerful machines. The researchers state that this work “enables a new paradigm: a quantum computer that learns from its errors and never stops computing.” A logical error rate of 7.72 × 10−4 achieved with the surface code demonstrates a significant advance toward stable quantum computation. Researchers at Google Quantum AI and Google DeepMind unified the processes of calibration and computation to reach this milestone. The core innovation lies in giving the quantum error correction process a dual role: not only correcting the quantum state, but also teaching a reinforcement learning agent to stabilize the system. This framework was experimentally demonstrated on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold with complementary decoder steering. Beyond surface codes, the team also achieved an average logical error rate of 8.19 × 10−3 with the color code. The reinforcement learning agent manages over a thousand control parameters, which specify how an abstract quantum error correction circuit translates into analog waveforms controlling the quantum system. Numerical simulations reveal the optimization speed of this reinforcement learning framework is independent of system size, su
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quantum-computingPalesi and Colleagues Propose Dependency-Aware Scheduling for Multi-Core Quantum Systems
Scheduling quantum circuits on multicore processors now occurs by assigning each gate as soon as its dependencies and resources are available, enabling greater parallelism across cores. Rajeswari Suance P S of the Indian Institute of Technology Guwahati, Chandigarh University, and the University of Catania and colleagues have devised a method for organising quantum calculations, key as quantum computers increase in size and complexity. Current systems limit the number of qubits, and this approach utilises multiple processing cores to overcome these restrictions. By scheduling each step of a calculation as soon as its requirements are met, the team achieved a 40 per cent reduction in processing time compared to traditional methods, improving how efficiently cores use resources. Rajeswari Suance P S and colleagues tackle the challenge of increasing the processing power of quantum computers by distributing calculations across multiple cores, a strategy mirroring the move to multicore processors in classical computing. As quantum computers grow, simply adding more qubits to a single chip becomes increasingly difficult, and this new approach instead connects smaller processing units, each containing a limited number of qubits, to work in parallel. This is akin to adding more lanes to a motorway to handle increased traffic, improving overall system capacity. The researchers of Technology Guwahati, Chandigarh University, and the University of Catania have developed a scheduling method that assigns each step of a quantum calculation as soon as its requirements are met, unlike traditional ‘layered scheduling’ which organises tasks like an assembly line. Greedy scheduling delivers substantial gains in multicore quantum circuit completion times A 40 per cent reduction in makespan, the total time to complete a quantum circuit, occurred using a new greedy scheduling strategy developed by researchers from University of Catania, Chandigarh University, and Indian Institute of Techn
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quantum-computingBenhemou and Colleagues Designs Automated Framework for Inter-Code Logical CNOT Synthesis
Scientists at Quantinuum have developed a new automated framework that establishes connections between diverse quantum error-correcting codes, addressing a fundamental challenge in the construction of practical, large-scale quantum computers. Asmae Benhemou and Noah Berthusen, from Quantum AI, present a system utilising chain maps to generate logical CNOT circuits between arbitrary CSS codes, resolving limitations encountered when integrating different code families. The approach not only rediscovers established connections between codes but also identifies new, low-depth solutions, potentially improving the efficiency of operations such as code switching and Pauli product measurements in heterogeneous quantum architectures. Automated framework enables low-depth connections between arbitrary quantum error correction Quantinuum researchers achieved a five-fold reduction in the complexity of connecting disparate quantum error correction codes, moving from circuits requiring a depth of ten to those with a depth of two in certain instances. Their automated framework, utilising ‘chain maps’, now enables logical CNOT circuits between arbitrary CSS codes, a key step towards building more flexible quantum computers. CSS codes, named after Calderbank-Shor-Steane, are a prominent class of quantum error-correcting codes defined by their structure relating to classical error-correcting codes. The ability to perform logical operations, such as the CNOT gate, between different CSS codes is crucial for modular quantum computation and fault-tolerant quantum information processing. Previously, such connections were largely limited to structurally related code families, hindering the development of heterogeneous quantum systems. The new method not only replicates established connections but also uncovers novel, low-depth solutions, including those preserving or partially preserving error detection capabilities, and can extend these to full code distance with additional measurements.
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quantum-computingRigetti's Quantum Reality: Delays, Low Revenue, And An Unjustified Premium
Melissa Tucker1.5K FollowersFollow5ShareSavePlay(6min)Comments(2)SummaryRigetti Computing faces persistent delays in scaling its superconducting qubit technology, with milestone slippages and underwhelming fidelity improvements.RGTI maintains a robust balance sheet ($570M cash, no debt), but continues to burn ~$20M per quarter with limited revenue visibility and no new meaningful contracts.Despite a $100M Department of Commerce LOI, funding is not the constraint; commercial traction remains weak, with recent contracts appearing as one-offs.RGTI’s premium valuation (248–310x PS) appears unjustified without revenue growth or technical breakthroughs, risking multiple compression toward peer levels. Just_Super/iStock via Getty Images I have covered Rigetti Computing (RGTI) before, where I outlined the company’s background in detail, explained why I didn’t understand all the excitement about the company, and why I considered it a sell. Since the This article was written byMelissa Tucker1.5K FollowersFollowWith a professional background spanning multiple industries, from ecnomocis to logistics and construction to retail, I bring a diverse perspective to investing. My international education and career experiences have provided me with a global outlook and the ability to analyze market dynamics from different cultural and economic perspectives. I have been actively investing for over a decade, honing a strategy that focuses on cyclical industries while maintaining a diversified portfolio that includes bonds, commodities, and forex. My interest in cyclical sectors stems from their potential for significant returns during periods of economic recovery and growth. However, I also recognize the importance of balancing risk, which is why I incorporate fixed-income investments (long or short).Analyst’s Disclosure: I/we have a beneficial long position in the shares of IONQ, INFQ either through stock ownership, options, or other derivatives. I wrote this article myself, and it expr
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quantum-computingLUMI AI Factory Selects IQM to Deploy Superconducting Quantum Computer for Hybrid HPC-AI Acceleration
LUMI AI Factory Selects IQM to Deploy Superconducting Quantum Computer for Hybrid HPC-AI Acceleration The LUMI AI Factory, coordinated by CSC – IT Center for Science, has selected hardware manufacturer IQM Quantum Computers (Nasdaq: IQMX) to deliver and integrate its IQM Halocene H4 superconducting quantum computer. Designated as LUMI-IQ, the system will be deployed at CSC’s data infrastructure center in Kajaani, Finland, with installation scheduled for 2027. The integration contract is jointly financed by the EuroHPC Joint Undertaking alongside the sovereign governments of Finland, Czechia, Norway, and Poland. Financially, the total contract value matches IQM’s full corporate revenue for the fiscal year ended December 31, 2025, as disclosed in the company’s July 1, 2026 public prospectus. [ LUMI-IQ System Integration Matrix ] Hardware Platform ──► IQM Halocene H4 on-premises superconducting processing unit. Initial Capacity ──► 150 qubits combining active error mitigation with NISQ operations. Facility Location ──► CSC IT Center for Science data complex (Kajaani, Finland). Financing Syndicate ──► EuroHPC Joint Undertaking, Finland, Czechia, Norway, and Poland. The procurement marks a structural shift toward full-stack on-premises quantum deployment within high-performance computing (HPC) ecosystems. Rather than operating as an isolated, cloud-accessible sandbox, the initial 150-qubit Halocene processing engine will sit directly adjacent to the pan-European LUMI supercomputer. The physical architecture integrates specialized Quantum Processing Units (QPUs) with automated, low-latency classical control infrastructure. This hardware arrangement enables European research and development teams to execute co-processing workflows, where computationally intensive machine learning loops and molecular calculations bounce seamlessly between high-density classical graphics nodes (GPUs) and quantum processors without routing delays. The engineering roadmap managed by IQM CEO Ja
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quantum-computingSEALSQ and GlobalFoundries Align on Trusted Supply Chains
Nasdaq: LAES and Nasdaq: GFS announced a publicly traded investment in future security as SEALSQ Corp and GlobalFoundries announced a strategic partnership to co-develop technologies spanning post-quantum cryptography and quantum computing. The collaboration will focus on pre-certified Post-Quantum Cryptography security IP, developed alongside GlobalFoundries company MIPS, with hard macro blocks and Chiplet Hardware Security Modules targeting applications like Hardware Security Modules and Secure Enclaves. Building on GlobalFoundries’ recent investments in quantum technology, the companies will also advance a CryoCMOS ecosystem to support scalable quantum computing systems. “A shared long-term vision between GF and SEALSQ is that semiconductors, cybersecurity, post-quantum cryptography, and quantum computing are converging into a single technology ecosystem,” said Carlos Moreira, CEO of SEALSQ, emphasizing the alignment of their respective expertise and ambitions. GF & SEALSQ Co-Develop Post-Quantum Cryptography Security IP The strategic Memorandum of Understanding, announced recently, will see the two firms co-develop secure semiconductor platforms and solutions designed to withstand the threat of future quantum computers. This partnership addresses the immediate need to secure data against potential attacks, where adversaries collect encrypted information with the intention of decrypting it once quantum computers become powerful enough. A key component of this effort will involve MIPS, a GlobalFoundries company, working alongside SEALSQ to create pre-certified Post-Quantum Cryptography (PQC) security IP blocks. These hard macro components and Chiplet Hardware Security Modules (CHSM) are specifically targeted for integration into applications demanding high security, such as Hardware Security Modules (HSMs) and Secure Enclaves. Pre-certification is crucial, streamlining the adoption process for clients needing to meet stringent security standards. Beyond bolste
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quantum-computingHartmut Neven
Quantum PeopleHartmut NevenHe turned a NASA side project into the lab that crossed quantum computing’s hardest thresholds, and he keeps telling the world the machines are arriving faster than anyone expects.Founder, Google Quantum AIWillow chip 2024Sycamore supremacy 2019Quantum Echoes 2025Neven’s lawIn this articleFrom computer vision to the quantum frontierHow machine learning led him to qubitsFounding the Google Quantum AI labThe Sycamore supremacy momentWillow and the error correction thresholdNeven’s law and the case for fast progressQuantum Echoes and useful advantageA public voice for quantum computingRecognition and lasting influenceWhy Hartmut Neven mattersFrequently asked questionsHartmut Neven at a glanceBorn1964, Aachen, GermanyNationalityGerman AmericanPhDPhysics, Ruhr University Bochum, 1996Founded Google Quantum AI2012, at NASA AmesCurrent roleVP of Engineering, GoogleLandmark chipWillow, 105 qubits, 2024Earlier milestoneSycamore supremacy, 2019Latest resultQuantum Echoes advantage, 2025Known forNeven’s law of doubly exponential progressKey takeawaysHartmut Neven founded Google Quantum AI in 2012 and still leads it as a Vice President of Engineering, one of the longest continuous tenures at the top of any quantum program.He reached quantum computing from computer vision, having built face recognition startups that Google acquired in 2006 before he turned to qubits.Under his direction the lab produced the 2019 Sycamore supremacy result and the 2024 Willow chip that crossed the error correction threshold.In 2025 the team reported a verifiable quantum advantage with its Quantum Echoes algorithm, a result another quantum machine can check.Neven’s law captures his central public message, that quantum progress is arriving at a doubly exponential pace that most observers underestimate.From computer vision to the quantum frontierHartmut Neven built one of the most consequential research programs in modern computing, yet his path into quantum hardware ran thro
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quantum-computingQpiAI Open-Sources Quantum SDK for 8- and 25-Qubit Cloud Access
QpiAI has released its Quantum SDK as open-source software, immediately providing developers with a pathway to run algorithms on the company’s 8-qubit and 25-qubit quantum computers via QpiAI-QCloud. The Python-based toolkit includes both local state-vector and density matrix simulators, allowing for algorithm prototyping and validation before utilizing actual quantum hardware. This move is designed to expand access to quantum software development for a global audience, fostering innovation across industries like finance, logistics, and artificial intelligence. “Quantum computing will scale only when developers can experiment, learn, and deploy without friction,” said Lakshya Priyadarshi, VP, Quantum Platforms & Solutions, QpiAI, emphasizing the SDK’s role as a bridge between theory and real-world application. QpiAI Quantum SDK Enables Algorithm Development and Hardware Access QpiAI has empowered developers with direct access to quantum hardware through the open-sourcing of its Quantum SDK, a move that bypasses the typical limitations of simulation-only environments and facilitates real-world algorithm testing. The Python-based toolkit is now freely available at https://github.com/qpiai/quantum-sdk and allows users to deploy algorithms on QpiAI’s 8-qubit and 25-qubit quantum computers via the QpiAI-QCloud platform at https://qcloud.qpiai.tech, representing a significant step toward democratizing access to quantum resources. This release isn’t merely about providing software; it’s about establishing a tangible connection between theoretical development and practical execution, crucial for accelerating progress in the field. QpiAI intends this release to broaden participation in quantum software development, targeting developers, researchers, universities, startups, and enterprise innovation teams globally. This dual approach is designed to optimize the development lifecycle, allowing for rapid iteration and refinement of quantum solutions. The toolkit is engineer
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quantum-computingTowards Quantum Network Performance Metrics: Challenges and Demonstration
--> Quantum Physics arXiv:2607.05642 (quant-ph) [Submitted on 6 Jul 2026] Title:Towards Quantum Network Performance Metrics: Challenges and Demonstration Authors:Mohamed Shaban, Mariam Kiran, Muhammad Ismail View a PDF of the paper titled Towards Quantum Network Performance Metrics: Challenges and Demonstration, by Mohamed Shaban and 2 other authors View PDF Abstract:As quantum networks move toward practical deployment, standardized performance monitoring becomes essential. This article proposes a structured monitoring framework for quantum networks with performance metrics, including quality (e.g., entanglement fidelity, QBER, loss, dark count rate), throughput and latency (e.g., entanglement rate, waiting time), timing (e.g., coincidence window, production and coincidence jitter), and exogenous factors (e.g., temperature, humidity, vibrations). These measurements enable real-time observability, benchmarking, and control, supporting use cases such as fault diagnosis, adaptive timing, and entanglement routing. Additionally, we implement a non-invasive prototype environmental monitoring system integrated with the quantum network infrastructure at Oak Ridge National Laboratory, demonstrating practical feasibility of live data collection and alert generation. Furthermore, we discuss the challenges of real-time monitoring and the trade-offs between observability and system performance. This work establishes a foundation for developing advanced quantum network monitoring systems and lays the groundwork for future autonomous control and quantum software-defined networking. Subjects: Quantum Physics (quant-ph); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2607.05642 [quant-ph] (or arXiv:2607.05642v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.05642 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mohamed Shaban [view email] [v1] Mon, 6 Jul 2026 21:10:08 UTC (13,657 KB) Full-t
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quantum-computingPasqal and MegazoneCloud Sign MoU for Neutral-Atom Hardware Deployment in South Korea
Pasqal and MegazoneCloud Sign MoU for Neutral-Atom Hardware Deployment in South Korea Neutral-atom quantum hardware developer Pasqal and South Korean cloud managed service provider MegazoneCloud have executed a Memorandum of Understanding (MoU) to integrate quantum workloads into commercial enterprise infrastructures across South Korea. The non-binding framework outlines the domestic distribution of Pasqal’s hardware layers via MegazoneCloud’s managed cloud service infrastructure, alongside collaborative application testing inside primary industrial verticals. Concurrently, Pasqal continues its public listing track on the Nasdaq Stock Market through a business combination with the special purpose acquisition company Bleichroeder Acquisition Corp. II (Nasdaq: BBCQ). [ Pasqal - MegazoneCloud Partnership Matrix ] Hardware Topology ──► Neutral-atom arrays driven by multi-client cloud software development kits. Integration Model ──► Integration into domestic managed cloud services and on-premises QPU setups. Targeted Verticals ──► Financial services, logistics optimization, biotechnology, and manufacturing. The enterprise alliance establishes a sequential deployment model consisting of three core technical directives. First, Pasqal’s full-stack hardware backends and specialized Quantum Software Development Kits (QSDKs) will be connected to MegazoneCloud’s regional cloud environments to give South Korean companies localized access to quantum nodes. Second, the entities will establish technical design workshops to develop functional use cases across logistics, financial portfolio tracking, biotechnology, and factory manufacturing workflows. Third, the roadmap includes plans for on-premises Quantum Processing Unit (QPU) installations, integrating neutral-atom processors directly into regional high-performance computing (HPC) environments to implement hybrid classical-quantum acceleration architectures. The commercial execution is supervised by Pasqal CEO Wasiq Bokhari and M
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quantum-computingWells Fargo Data Validates Quantum Sequence Prediction with QFWPs
Wells Fargo collaborated with researchers from National Taiwan University, Stevens Institute of Technology, and other institutions to refine a new approach to quantum sequence modeling, focusing on “Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates.” The team presents findings demonstrating improvements in performance by modulating the “old-state” within the quantum model, a technique that enhances the processing of sequential data without the limitations of traditional recurrent networks. Researchers found that bounding this modulation with a tanh gate corrected long-sequence divergence while preserving low-error behavior, a key challenge in quantum machine learning. This advancement was tested on standard quantum-dynamics forecasting tasks and on the prediction of Milan SMS telecommunication activity, aligning with existing explorations of real-world applications for quantum sequential modeling. Quantum Fast-Weight Programmers for Sequence Modeling Unlike models reliant on nonlinear recurrent hidden states, QFWPs offer a more parallelizable gradient path, making them attractive for time-series prediction and sequential control. The recent extension, Self-Modulating QFWP, introduces input-dependent modulation of both new fast-weight updates and accumulated fast-weight states, giving the model greater control over information retention and suppression. However, the original implementation suffered from divergence in long sequences due to an unbounded “old-state” multiplier. To address this, the researchers proposed a bounded old-state modulation rule, applying a tanh gate to the recurrent memory branch, leaving the additive update and new-update modulation untouched. “Our main architectural contribution is a bounded old-state modulation rule,” the team states, emphasizing the targeted stabilization strategy. Testing their models on two settings, CUDA-Q quantum-dynamics forecasting tasks and Milan SMS telecommunication activity predicti
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quantum-computingPasqal, MegazoneCloud to Integrate QPUs Into Managed Cloud Services
Pasqal and MegazoneCloud have formalized plans to bring industrial-scale quantum computing to South Korea, signing a Memorandum of Understanding on July 7, 2026, to accelerate enterprise adoption of the emerging technology. The collaboration will focus on integrating Pasqal’s full-stack neutral-atom Quantum Processing Unit (QPU) technology, including Quantum Software Development Kits, into MegazoneCloud’s managed cloud services, offering Korean businesses access to quantum workloads. Initial efforts will target four key sectors: finance, logistics, biotechnology, and manufacturing, through jointly developed use cases and demonstrations. “South Korea is one of the world’s most advanced technology economies, and its enterprises are heavy users of cloud computing and are ready for quantum,” said Pasqal CEO Wasiq Bokhari, framing the partnership as a foundation for driving adoption within existing enterprise workflows. Pasqal and MegazoneCloud Expand Quantum Computing Access in South Korea This collaboration centers on integrating Pasqal’s sophisticated quantum hardware directly into MegazoneCloud’s existing cloud infrastructure, a move designed to accelerate quantum adoption for Korean businesses. The initiative aims to bypass typical hurdles of quantum implementation, offering managed cloud access and targeted use case development, ultimately facilitating a transition from pilot programs to full-scale production deployments. This signifies a commitment to providing developers with the tools necessary to build and test quantum algorithms on actual hardware, rather than relying solely on simulations. The focus extends beyond technological integration, with plans to jointly develop quantum computing applications specifically tailored to four crucial sectors: finance, logistics, biotechnology, and manufacturing. These sectors were selected because they represent areas where quantum computing promises the most immediate and substantial gains in efficiency and innovation. P
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quantum-computingAlfred University and Classiq Launch Joint Academic Quantum Computing Initiative
Alfred University and Classiq Launch Joint Academic Quantum Computing Initiative Alfred University, the New York State College of Ceramics, and quantum development firm Classiq have announced a joint quantum computing initiative designed to integrate functional hardware-portable software modeling into engineering curricula and energy systems research. The academic collaboration deploys Classiq’s high-level synthesis platform to bypass manual, gate-level quantum circuit construction, allowing students and researchers to engineer functional algorithms without deep low-level compilation skills. The curriculum expansion is intended to support workforce development and applied energy optimization models across the State University of New York (SUNY) network. [ Alfred University - Classiq Framework ] Software Engine ──► Classiq high-level functional synthesis platform using agentic compilation workflows. Research Focus ──► Power system unit commitment optimization and ceramic/glass materials discovery. Academic Integration──► Inamori School of Engineering curricula, expanding across CUNY and SUNY networks. The instructional integration is led by Junpeng Zhan, Assistant Professor of Renewable Energy Engineering at the Inamori School of Engineering, who has embedded the platform into active courses. Zhan’s core research focuses on power systems optimization, specifically the “unit commitment problem”—a multi-variable calculation where electric grid operators determine the most cost-effective generation schedules to meet fluctuating regional energy demands. The joint initiative builds upon Zhan’s previous National Science Foundation (NSF)-funded computational grants and a 2024 collaborative research program with the Rochester Institute of Technology and ISO-New England to explore quantum optimization paths for wholesale electrical grids. Concurrently, the initiative expands into solid-state physics and advanced materials modeling under S. K. Sundaram, Inamori Professor of Ma
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quantum-computingQuantum Inspire: Delft’s New 17-Qubit Processor Opens Quantum Access
Delft University of Technology has unveiled Tuna-17, a 17-qubit superconducting quantum computer and the third such processor released by the team in one year, following Tuna-5 and Tuna-9. Developed by the DiCarlo Lab at QuTech, Tuna-17 is notable for its open access; researchers, students, and educators worldwide can utilize the real quantum hardware via the Quantum Inspire cloud platform without usage caps. The team notes that building a superconducting quantum system of this complexity and performance is a major technical achievement, and that it is openly accessible and free to use. This full-stack integration, built entirely within a European value chain, represents a significant step toward enabling quantum error correction experiments and a wider range of advanced quantum algorithms. The DiCarlo Lab at QuTech has rapidly advanced superconducting quantum processor development, culminating in the release of Tuna-17. This 17-qubit system follows closely on the heels of Tuna-5 and Tuna-9, demonstrating an accelerating development cycle with three releases within a single year. This iterative progress underscores the lab’s focus on scalability, designing the Tuna architecture with future expansion in mind. This open-access approach is central to QuTech’s vision for European quantum technology, fostering collaboration and independence. The team explains that the system is built entirely within a 100% European value chain, with contributions from TNO, Orange Quantum Systems, Qblox, Delft Circuits, and QuantWare, solidifying a robust domestic supply chain. Tuna-17’s 17 qubits and 24 tunable couplers are specifically designed to facilitate quantum error correction experiments, alongside supporting advanced algorithms like low-depth factorisation and near-term (NISQ) applications; the team is already working on Tuna-28, signaling continued momentum. Researchers report optimizing the frequency positioning of tunable couplers to mitigate spectator effects on quantum oper
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quantum-computing3 Quantum Computing Stocks to Watch in the Second Half of 2026 - The Motley Fool
Over the past year, quantum computing stocks have emerged as a compelling complement to mainstream opportunities in the artificial intelligence (AI) ecosystem. While classical AI systems have demonstrated impressive capabilities in pattern recognition and generative tasks, many high-value problems remain computationally strained. Quantum machines leverage properties known as superposition and entanglement to better explore solutions in more sophisticated applications. This opens the door to hybrid quantum-classical environments that could usher in waves of better data, tighter constraints, and new algorithms for AI. According to an analysis by management consulting firm McKinsey & Company, quantum AI could generate between $1.3 trillion and $2.7 trillion in economic value by 2035. McKinsey sees quantum computing playing a critical role across energy and materials, pharmaceuticals, financial services, and travel and logistics, as well as advanced electronics and defense systems. In my view, three companies stand out in the quantum AI arena for distinct reasons: Nvidia (NVDA +0.38%), IonQ (IONQ 0.38%), and Quantinuum (QNT +10.29%). Let's dig into how each of these companies is involved with quantum computing and assess their respective investment profiles. Image source: Getty Images. Nvidia: The ecosystem enabler of tomorrow Nvidia dominates classical AI thanks to its one-two punch, featuring a deep roster of graphics processing unit (GPU) architectures and software system CUDA. The company's primary quantum efforts revolve around cuQuantum, a toolkit that accelerates the simulation of quantum circuits on Nvidia hardware. This design allows researchers to prototype next-generation algorithms without requiring capital-intensive physical quantum processors. ExpandNASDAQ: NVDANvidiaToday's Change(0.38%) $0.74Current Price$195.57Key Data PointsMarket Cap$4.7TMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dua
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