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QpiAI Inaugurates 8-Inch Quantum Chip Foundry in Bengaluru Targeting 10,000-Qubit QPUsquantum-computing

QpiAI Inaugurates 8-Inch Quantum Chip Foundry in Bengaluru Targeting 10,000-Qubit QPUs

QpiAI Inaugurates 8-Inch Quantum Chip Foundry in Bengaluru Targeting 10,000-Qubit QPUs Full-stack quantum and AI startup QpiAI has inaugurated an 8-inch quantum processing unit (QPU) manufacturing facility in Jakkur, Bengaluru. Formally designated as Phase 2 of its 70,000-square-foot R&D center, the quantum foundry can fabricate flip-chip superconducting quantum processors with up to 128 physical qubits. The company plans to complete Phase 3 by 2027, expanding the cleanroom infrastructure to fabricate single QPUs containing up to 10,000 physical qubits. [ QpiAI Vertically Integrated Quantum Foundry Stack ] │ ┌────────────────────────────────────────┼────────────────────────────────────────┐ ▼ ▼ ▼ Cleanroom Lithography & Packaging Transmon & Fluxonium QPU Lineup Quantum Supremacy Centres (QSCs) • Class 100 & Class 1,000 Cleanrooms. • QVidya (8-qubit Transmon). • 10-Acre Campus in India. • 8-Inch Wafer Flip-Chip Assembly. • Indus (25-qubit Transmon). • 4 International QSC Sites Planned. • End-to-End Etching & 3D Stacking. • Kaveri (64-qubit) / Yukti (9-qubit). • Hybrid QPU-AI Data Infrastructure. Foundry Capabilities and Processor Roadmap The facility handles the full device manufacturing lifecycle on-site—including electron-beam lithography, wet/dry etching, patterning, Josephson-junction fabrication, 3D flip-chip assembly, and cryogenic packaging. Operating with Class 100 and Class 1,000 cleanroom specifications, the foundry manufactures superconducting QPUs along with peripheral control chips and sensors, while supporting R&D into photonic and semiconductor spin qubits. QpiAI has already fabricated four primary quantum processors at the site: QVidya: An 8-qubit superconducting transmon processor. Indus: A 25-qubit transmon QPU integrated into hybrid classical HPC data centers. Kaveri: A 64-qubit superconducting transmon chip using proprietary low-loss flip-chip interconnects. Yukti: A 9-qubit processor based on a fluxonium qubit variant, des

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Researchers Cut Quantum Resource Demand for Power Grid Islandingquantum-computing

Researchers Cut Quantum Resource Demand for Power Grid Islanding

A new method limits the spread of disturbances in electrical grids through controlled islanding, partitioning a compromised grid into connected, electrically sustainable islands. Classical methods face sharply growing computational costs as network size and island count increase. Quantum optimisation offers an alternative for exploring this combinatorial partition space. However, monolithic quantum formulations encode all assignment decisions in one circuit, causing qubit demand and circuit complexity to scale with network size. In this study, Yuqi Jiang of Tsinghua University and colleagues propose a qubit-bounded sequential distributed quantum approximate optimisation algorithm (QAOA) framework to tackle coherent controlled islanding under limited quantum resources. It formulates the optimal islanding strategy through a series of sequential QAOA optimisations. Distributed quantum algorithm tackles large-scale power grid partitioning with fewer qubits A five-fold reduction in qubits needed for controlled islanding has been achieved, resolving problems with 300 buses, a scale previously inaccessible to monolithic quantum approximate optimisation algorithm (QAOA) approaches. Modern power systems are undergoing a significant transformation with the increasing integration of distributed energy resources (DERs) such as solar photovoltaic arrays, wind turbines, and energy storage systems. While these DERs offer numerous benefits, including increased resilience and reduced carbon emissions, they also introduce substantial variability and uncertainty into the power grid. This variability stems from the intermittent nature of renewable energy sources and the decentralised control of these resources. During disturbances, such as faults or sudden load changes, these effects can intensify generation-load imbalances and potentially lead to cascading failures, resulting in widespread blackouts. Controlled islanding, a proactive grid management technique, aims to mitigate these r

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Researchers Cut Signal-Learning Measurements by Seven Million-Foldquantum-computing

Researchers Cut Signal-Learning Measurements by Seven Million-Fold

Researchers demonstrate that coupling a single controllable qubit to an otherwise conventional sensor can exponentially reduce the number of measurements required to learn classical signals. These rigorous quantum advantages apply to fundamental sensing tasks, including learning Fourier coefficients, extracting temporal correlations from time-varying signals, and estimating transformations. Quantum feature sensing accelerates signal learning and dark matter simulations A tenfold million-fold reduction in measurements needed for learning both Fourier amplitudes and time-varying signals has been achieved, a feat previously unattainable with conventional sensors. This substantial decrease, demonstrated using a superconducting cavity-qubit architecture, unlocks the potential for practical quantum advantages with minimal hardware requirements. The quantum feature sensing algorithms streamline signal learning and also deliver significant improvements in simulations important for detecting elusive dark matter and enhancing wireless communication systems. Wireless communication system simulations benefited from orders-of-magnitude improvements in performance. The theoretical underpinnings of this improvement are Quantum Phase-Space Inference, a framework establishing lower bounds and optimal algorithms for quantum-enhanced learning, alongside a certificate verifying quantum advantage. Utilising these algorithms, experiments observed a seven-fold increase in the speed of simulations used for detecting weakly interacting massive particles, known as dark matter. Although scaling to complex, real-world scenarios presents an engineering challenge, experiments showed 10^7-fold reductions in measurements for learning signals. Quantum Phase-Space Inference for enhanced sensing and learning Experiments demonstrated a superconducting cavity-qubit architecture achieving 10^7-fold reductions in the number of measurements required for Fourier-amplitude and time-varying signal learning.

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Shortcut for simulating logical magic states could accelerate the design of fault-tolerant quantum computersquantum-computing

Shortcut for simulating logical magic states could accelerate the design of fault-tolerant quantum computers

Step by step: A simplified schematic of the simulation framework, showing how circuit-level Pauli errors in noisy magic-state preparation protocols can be propagated and simplified using the underlying algebraic structure, reducing the problem to efficiently simulable logical Clifford errors acting on the output magic state. (Courtesy: Yousra Farhani) Building a useful quantum computer is not simply a matter of adding more qubits. The greater challenge is making these qubits reliable enough to perform long computations without errors overwhelming the result. Quantum error correction addresses this problem by encoding each logical qubit across many physical ones, but it comes at a cost: many of the operations required for a general-purpose or “universal” quantum computer become highly resource intensive once fault-tolerant error correction is introduced. To address this resource challenge, researchers at the University of California, Davis, US have developed a classical simulation method that efficiently models the preparation of some of the most demanding quantum states. The method, which they describe in PRX Quantum, works even for large, high-fidelity protocols that were previously beyond reach. Building a universal quantum computer Logical operations in fault-tolerant (that is, error-corrected) quantum computing architectures fall into two broad categories. The first category is a set of operations known as Clifford gates that are relatively straightforward to implement and, importantly, can be simulated efficiently on a classical computer. By themselves, however, Clifford gates are not computationally universal. For that, you also need non-Clifford operations, which lie outside the set of classically-simulable gates and provide the missing ingredient for universal quantum computation. To realize these non-Clifford operations in a fault-tolerant way, some qubits need to be in a special state known as a magic state. Preparing these magic states with sufficiently h

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Infleqtion: A Brilliant Science Project, But A Highly Risky Public Stockquantum-computing

Infleqtion: A Brilliant Science Project, But A Highly Risky Public Stock

Elina Selianska895 FollowersFollowSummaryInfleqtion, Inc. operates as an applied quantum engineering firm, not a traditional IT or semiconductor company.INFQ’s revenue is driven by state grants and defense contracts for experimental quantum devices, not scalable commercial demand.Classic valuation metrics are inapplicable; the company trades at a $2.9B cap with negligible, unpredictable sales and deep losses.I assign a Sell rating for standard portfolios due to extreme uncertainty, dilution risk, and the inability to model future profitability. Just_Super/iStock via Getty Images The company Infleqtion, Inc. (INFQ) trades on the open market with a capitalization approaching the mark of $2.9 billion. But the classic templates of valuation are absolutely inapplicable to this company. We cannot useThis article was written byElina Selianska895 FollowersFollowI am a private investor with 10 years of experience in the stock market. My approach to fundamental analysis probably differs from the classical method. I am firmly convinced: first, you must understand the business, and only after that look at the figures. For me, investing is an attempt to understand the place of a company or an asset in the future. Financial reports reflect only the past and the present. Therefore, my analysis always begins with an attempt to thoroughly understand the essence of the company itself. On what is this business really built? What value does it create today? And, most importantly, how will this business be integrated into the economy of tomorrow? I evaluate ideas through the prism of a long-term perspective. Before opening a trade, I must clearly see the place of this company in the world in 5–10 years. I am interested in what management is doing right now in order to capture the markets of the future. Multipliers, balances, and charts are secondary — they should only confirm the fundamental idea, not dictate it. I write on Seeking Alpha to share exactly this approach: helping readers s

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D-Wave Could Unlock Massive Enterprise Demand but the Valuation Risk Is Realquantum-computing

D-Wave Could Unlock Massive Enterprise Demand but the Valuation Risk Is Real

D-Wave (QBTS +1.29%) is moving beyond experimental quantum research and into critical enterprise operations. Its hybrid computing framework and expansion into scientific simulation could open a major new growth phase, but uneven bookings, missed expectations, and an extreme valuation leave little room for error. Stock prices used were the market prices of Aug. 7, 2026. The video was published on Aug. 16, 2026. Read NextAug 13, 2026 •By Motley Fool TranscribingD-Wave Quantum (QBTS) Q2 2026 Earnings Call TranscriptAug 11, 2026 •By Daniel SparksD-Wave's Revenue Fell 44% in a Year. Its Market Value Rose 38%.Aug 11, 2026 •By Chris NeigerIs D-Wave Quantum a Buy? Here's What the Data Says.Aug 6, 2026 •By Johnny RiceWhy D-Wave Quantum Stock Just FellAug 5, 2026 •By Chris NeigerWhy D-Wave Stock Fell 25% in JulyAug 3, 2026 •By Rich SmithWhy D-Wave Quantum Stock Popped TodayAbout the AuthorRick is a Wall Street Journal best-selling author with over 20 years of experience trading stocks and options. The most authoritative publications, including Good Morning America, Washington Post, Yahoo Finance, MSN, Business Insider, NBC, FOX, CBS, and ABC News, cover his work. His passion is business, and he works tirelessly to deliver content in an easy-to-understand manner. In 2018, Rick wrote The Financially Independent Millennial to inspire his readers with his story about becoming financially independent at age 35 despite not learning about money when he was younger. His books are easy to read and often refer to key points that “He would tell his younger self.” When not thinking about business, Rick writes (mainly about cruise ship travel) for his travel blog and is an enthusiast of fast cars, technology, & cooking.CMFrickorfordStocks MentionedD-Wave QuantumNASDAQ: QBTS$21.17(+1.29%)+$0.27Motley Fool Stock Advisor’s Latest PickGet Access---% Avg Return*Average returns of all recommendations since inception. Cost basis and return based on previous market day close.

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Resource-efficient quantum eigenvalue transform with commutator scalingquantum-computing

Resource-efficient quantum eigenvalue transform with commutator scaling

--> Quantum Physics arXiv:2608.13862 (quant-ph) [Submitted on 14 Aug 2026] Title:Resource-efficient quantum eigenvalue transform with commutator scaling Authors:Arul Rhik Mazumder, James D. Watson, Samson Wang View a PDF of the paper titled Resource-efficient quantum eigenvalue transform with commutator scaling, by Arul Rhik Mazumder and 2 other authors View PDF Abstract:We develop quantum algorithms for estimating properties of general matrix functions of Hermitian matrices, with applications to phase estimation, Green's function evaluation, and estimating measurement distributions of time-evolved states. The resulting methods exhibit commutator scaling in matrix parameters similar to that usually found for product formulae, lower circuit depth in other parameters, and require only a single ancillary qubit. Our central primitive consists of classically postprocessing randomly chosen product formulae circuits, which mathematically corresponds to an approximation of a Richardson extrapolation. Within our framework, we introduce a protocol for approximating the measurement distributions of quantum states, extending beyond standard observable estimation. We also provide tightened gate complexity bounds for practically relevant systems, including those with k-local interactions, long-tailed matrix ensembles, and conserved quantities. Finally, numerical experiments confirm that our method can achieve significantly shallower circuit depths than standard product formulae in certain parameter regimes, and highlight the potential of their heuristic application. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.13862 [quant-ph]   (or arXiv:2608.13862v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.13862 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Samson Wang [view email] [v1] Fri, 14 Aug 2026 01:23:30 UTC (740 KB) Full-text links: Access Paper: View a PDF of the paper titled

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The Rise of Superconducting Erasure Qubits – an Industry Perspective
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The Rise of Superconducting Erasure Qubits – an Industry Perspective

TECHNICAL BLOG The Rise of Superconducting Erasure Qubits – an Industry Perspective Building a commercially useful quantum computer requires more than increasing qubit numbers. The real challenge is reducing errors to the point where quantum processors can reliably outperform classical systems on meaningful problems. Maria Violaris DEVELOPER ADVOCATE Maria has a hybrid role at OQC of quantum error correction research towards building a fault-tolerant quantum computer, and technical science communication. She has a PhD in theoretical quantum information from the University of Oxford, alongside which she interned with IBM Quantum making the “Quantum Paradoxes” YouTube series. She has spearheaded multiple new initiatives in the quantum community, including the “Quantum on the Clock” Schools Video Competition; Oxford Quantum Information Society; and quantum computing workshops. She has also written for Physics World magazine; published quantum education research; and hosts a Quantum Foundations Podcast on her YouTube channel, amongst other quantum content. Today’s superconducting qubits have made remarkable progress, but error rates remain too high for large-scale, fault-tolerant quantum computing. That’s why quantum error correction (QEC) is one of the defining engineering challenges for the industry. At OQC, we are taking a hardware-first approach to solving it. Our latest Perspective article, Developments in superconducting erasure-qubits for hardware-efficient quantum error correction, explores one of the most promising directions in the field: superconducting erasure qubits. It also explains how our newly developed OQC Dimon architecture fits within the rapidly evolving research landscape. Engineering qubits that reveal their own errors Traditional quantum error correction assumes errors occur silently, requiring significant overhead to detect and correct them. Erasure qubits however, change that assumption. An erasure error occurs when a qubit leaves its computati

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