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Germany-based full-stack quantum computing leader QUDORA Technologies launched Qudora Japan K.K. This May 28, 2026 expansion marks a turning point in the company's global expansion plan, focusing on Asia-Pacific's growth.

An Important Chiyoda-ku Location

New subsidiary headquarters are in Tokyo's Chiyoda-ku business and administrative district. From its central base, Qudora Japan K.K. fosters regional cooperation, client growth, and commercial progress throughout Japan and the Asia-Pacific.

QUDORA has recruited a top leadership team with strong local business contacts to lead this challenging project. As President of Qudora Japan K.K., Ned Cahoon will be joined by Country Manager Mitsuo Harahata and Executive General Manager Yuichi Watanabe. Local Japanese market experience and QUDORA's German engineering heritage are emphasized in this leadership structure.

Using Scientific Magnificence Tradition

QUDORA's technology is based on Germany's cold-atom physics supremacy since the first trapped-ion systems and atomic clocks. Found in 2021 and based in Braunschweig, the company has quickly become a “full-stack” provider, handling everything from quantum processing software to hardware.

Their hardware relies on patented microwave technology NFQC (Near Field Quantum Control). One of the company's founders designed NFQC to address the instability of the quantum state, a longstanding quantum computing concern.

The Coherence Search for “Quantum Utility”

In quantum mechanics, coherence time—the duration of a qubit's quantum state—is significant. Limited algorithm complexity is often caused by short coherence times in present quantum solutions. QUDORA's NFQC technology addresses this with “exceptionally long” coherence times and high-precision qubit management.

By lengthening quantum state life, QUDORA's technology increases algorithmic depth and complexity. Quantum error correction's high overhead is reduced by this technical benefit, enabling more realistic applications. Manufacturing and scalability are prioritized to give high-performance quantum computing to business and industry.

Why Japan? Combining Vision and Industry

QUDORA expanded into Japan because of its “strategic vision and support for quantum computing,” which it sees as a crucial ecosystem for innovation. The corporation considers Japan a “valued ecosystem partner” rather than a market.

QUDORA CEO Dr. Amado Bautista-Salvador praised Japan's quantum roadmap as one of the most comprehensive and collaborative, covering the full value chain, key supply chains, advanced quantum hardware development, and clear pathways to quantum utility.

Japan's industrial strengths meet QUDORA's trapped-ion technologies. The company targets industries with low coherence periods that have limited quantum solutions, such as:

Hi-performance computing

Automobile engineering

Materials engineering

Pharmaceutical research

Finance

Ecosystem Integration and Engagement

QUDORA entered the Japanese market after several strategy changes. The company is already active in the Q-STAR Alliance, Japan's leading quantum industry association. QUDORA also attended the SCA/HPC Asia 2026 conference in Osaka and joined AHK Japan, the official German business representative, to engage with the German-Japanese business community.

For prospective customers, QUDORA offers multiple deployment techniques. Their solutions interface effortlessly with HPC and business infrastructure. This includes on-premises deployments for research institutes and high-performance computing centers that need local hardware and cloud-based access for rapid quantum resources.

Future outlook

Ned Cahoon, Qudora Japan's new president, was optimistic about the local climate. He claimed Japan's technology creates a “unique environment for innovation and collaboration”. Qudora Japan K.K. builds strong, long-lasting quantum community links to bring the industry closer to real quantum use.

ORCA Computing's official participation in London's Digital Realty Innovation Lab (DRIL) advances quantum technology's economic feasibility. This cooperation allows real-world testing of next-generation computation capabilities, increasing quantum system integration into enterprise data ecosystems.

Innovation in Infrastructure Testing

Digital Realty, the world's leading cloud- and carrier-neutral data center provider, launched the Digital Realty Innovation Lab for next-generation testing. The DRIL's major mission is to help clients evaluate quantum and AI technologies in real-world settings before they are commercialized. By enabling real-world tests, Digital Realty intends to lower the risks of integrating new technologies and expedite innovation for EMEA organizations.

Businesses creating future compute environments will have better access to advanced infrastructure testing with the DRIL's London debut. This is more than a trial for ORCA Computing; it's a key commercial milestone that shows how quantum acceleration can be seamlessly integrated with AI and HPC infrastructure.

Data Center Quantum Computing

ORCA's PT Series photonic quantum systems are crucial to this deployment. ORCA's devices can function in a live data center without additional cooling or infrastructure, unlike many quantum architectures that demand cryogenic temperatures or housing. The “data center-native” strategy of ORCA claims that quantum computing should be integrated directly into organizations' hardware frameworks for AI and computationally intensive tasks.

ORCA Computing co-founder and CEO Richard Murray, PhD, stressed the importance of this integration. He said ORCA was founded on the concept that quantum systems must work with commercial infrastructure. Murray was pleased that the London-based company was involved in the DRIL launch, noting that their solutions worked effectively in commercial data centers.

ORCA gives clients direct access to leading quantum innovators, says Seamus Dunne, Digital Realty UK and Ireland Managing Director. Dunne showed how ORCA's connection with the DRIL allows users to research hybrid quantum-classical computing methods and apply these technologies to AI and business.

Strategic Leadership and Science

ORCA Computing has a strong quantum research-trained leadership team. Prof. Ian Walmsley, ORCA Computing Board Chairman, is an expert in waveguide circuits, quantum optics, and quantum memories. His notable achievements include being an Honorary Fellow of St. Hugh's College, Oxford, and Provost of Imperial College London.

The SPIDER method for studying ultra-fast laser pulses is widely recognized as Prof. Walmsley's scientific contribution. He is President of the Optical Society of America, Director of the Networked Quantum Information Technologies (NQIT) hub, and Fellow of the American Physical Society, Institute of Physics, and Royal Society. These scientific achievements demonstrate ORCA's photonic quantum systems' technological precision, including the PT-1 and PT-2.

Real World Applications: Energy and Others

ORCA's Industrial Challenges initiative includes the Digital Realty Innovation Lab extension. Quantum intervention in biofuel production and renewable energy optimization seems promise in the energy market.

Traditional computational approaches struggle to study molecular structures, which are needed to manufacture biofuels, new materials, and medications. Due to the enormous variety of chemical configurations and computational expense, classical systems struggle to locate low-energy conformations for some molecules. The industry must accelerate this research since molecule structure affects physical and chemical qualities.

ORCA is working with BP on a hybrid quantum-classical approach to use its technologies. Generative adversarial network (GAN) methods yield low-energy conformations of small and medium-sized hydrocarbon molecules in this study. This alliance aims to overcome computational barriers to molecular research, reducing carbon emissions and improving biofuel efficiency.

Growing Quantum Environment

ORCA's DRIL participation shows its growing strategic partnerships. The company is working with NVIDIA, Toyota Tsusho, SiC technologies, and JIJ to advance commercial photonic quantum technologies.

ORCA's latest London installation solidifies its position as one of the few full-stack quantum computing enterprises capable of rapid commercial deployment. By showing that its systems can work in real time with emerging AI and infrastructure technologies, ORCA is bridging the gap between theoretical quantum research and enterprise-grade applications.

GlobalPlatform launched Pavona, the first open-source silicon distribution with production-grade post-quantum cryptography (PQC), which is intended to transform the semiconductor industry. This project offers a community-governed architecture for secure-by-default silicon that is scalable from large data centers to tiny IoT devices, breaking from proprietary, single-vendor security implementations.

Addressing Global Security Crisis

According to experts, the digital world is experiencing a “rising tide of cyber insecurity,” propelled by unsecure technology from the start. As supply chains become increasingly decentralized and AI grows, chip security must be stable and autonomous.

Dominic Rizzo, CEO of ZeroRISC and Pavona's governing board chair, said silicon is now "critical infrastructure." “Pavona is the home for composable open-source secure silicon: a standards-aligned ecosystem bringing together the world's leading experts across industry, academia, and open-source,” Rizzo said. He said the initiative is the result of years of open-source work for cooperation and commercial use.

Technical Excellence: 3nm Readiness and PQC

Pavona is built on high-performance security. The project began with two successfully taped-out reference designs: a solo chip root of trust and an integrated root of trust for chiplet architectures produced at TSMC's advanced 3nm (N3) node.

Pavona's Post-Quantum Cryptography stack is a key technology. According to research presented at Real World Crypto 2026 in Taipei, the release features hardware-software co-designs that boost the newly standardized ML-KEM and ML-DSA algorithms by 6–9x. For the first time, embedded silicon may use production-grade PQC with 36–75% higher maximum operating frequency at “near-zero” area cost.

Pavona distributes modularly, unlike monolithic open-source systems. Engineers can use its composition engine and meticulously kept IP library to select and assemble subsystems for AI accelerators or automotive controllers.

Titans of Industry Coalition

Twelve founding members from various technical sectors support the project. This alliance includes:

Winbond Electronics, Qualcomm Technologies, and Analog Devices lead semiconductors.

Meta and Tenstorrent rule AI and cloud.

The SIMPLE Crypto Association, Oxford University, and Max Planck Institute for Security and Privacy are research and standards bodies.

Agile Analog, Baochip, CrossBar, and ZeroRISC provide specific IP.

David Ramku, Director of Ecosystems and Technical Operations at Meta, noted that an auditable root of trust with PQC allows designers to embed security across the spectrum, from client devices to servers, making this work important for the AI era. Since chiplet architectures require each “tile” in a package to be trusted, Pavona's certified, silicon-proven root of trust is essential for the open compute ecosystem, according to Tenstorrent CEO Jim Keller.

Openness and Certification

Pavona's commercial standards set it apart. The framework meets SESIP, FIPS 140-3, and Common Criteria certification standards. This ensures open-source solutions are ready for wide market deployment in regulated industries, not merely academic exercises.

GlobalPlatform CTO Gil Bernabeu compared Pavona's potential impact to Linux in the software industry. He claims Pavona provides the “standards infrastructure required for commercial adoption,” which accelerates silicon-based root-of-trust device development.

Openness is important, according to Baochip founder Andrew “bunnie” Huang, who believes end users should know what protects their secrets. Huang remarked, “Pavona delivers the coalition that all of us need to have confidence your best interests are served.

Availability, Governance

Pavona is hosted by GlobalPlatform and features dual governance. An autonomous technological Steering Committee manages the technological roadmap, while a contributing Governing Board manages operations and finance. Yocto and Zephyr are used to build this framework, which combines community-driven agility with GlobalPlatform's certification experience.

Membership costs range from Platinum to Silver, and academic institutions and non-profits can join the project as Associates.

At www.pavona.org, the IP library, high-level designs, and extensive documentation are readily available. Hardware-native continuous integration (CI) dashboards on each contribution allow engineers to reproduce a reference root-of-trust architecture in under an hour.

Empirically Optimised Dynamical Decoupling Improves Quantum Computation.

Despite its potential, quantum computing is hindered by the fragility of quantum states, which are susceptible to ambient noise mistakes. Numerous error suppression and mitigation solutions are being studied to eliminate these defects and increase quantum calculation reliability. Dynamical Decoupling reduces quantum computation defects most efficiently and cheaply.

Suppressing Errors on Noisy Hardware

Despite advances in DD design, pulse sequences that successfully decouple computational qubits on noisy quantum devices remain a challenge. Quantum processor noise is complicated and dynamic, therefore theoretically-derived DD sequences often fail in practice. This requires a more flexible and hardware-sensitive DD implementation.

Empirical Optimisation: A New DD Method

Recent discoveries have introduced a powerful paradigm: empirical optimisation of DD sequences. Learning algorithms or combinatorial optimisation are used to empirically find device-tailored DD sequences. Instead than using theoretical models, this method discovers optimal approaches using quantum hardware experiments.

Empirical learning is used to optimise DD (GADD), inspired by genetic algorithms. In particular, this strategy has optimised DD procedures for IBM's superconducting qubit-based quantum processors. GADD simulates natural selection by enhancing Dynamical Decoupling (DD) sequences based on how well they suppress quantum device errors.

Outstanding Performance and Generalisability

Empirical optimisation yielded excellent results. We found that empirically taught DD approaches suppress errors better than canonical sequences in all experimental settings. These experimentally optimised sequences reduce superconducting qubit noise better than theoretically derived DD sequences and traditional decoupling sequences like CPMG, XY4, and UR6.

The harder the computer problem, the better these experimentally learnt solutions work. Error suppression improves with problem size and circuit complexity. This discovery is significant as quantum algorithms get more complex and require more qubits and deeper circuits.

Advantages of this empirical learning approach include stability and generalisability.

The approaches identified provide long-term performance without retraining. Maintaining peak performance reduces operational expenditures.

The optimisation approach is scalable and successful for increasingly complex quantum systems since these empirically taught methods can generalise to bigger circuits when trained on modest sub-circuit architectures. The methodology also finds time-constant ways as circuit width and depth increase.

Practical Uses and Benchmarking

Empirically optimised Dynamical Decoupling (DD) performs well on many quantum techniques and scales:

Mirror randomised benchmarking on 100 qubits was examined.

This prepared 50 qubits for GHZ states.

It improved the 27-qubit Bernstein-Vazirani algorithm.

This method can increase near-term quantum gadget performance, as shown by these investigations. IBM has led this study and may have combined these results with Qiskit. IBM prioritises quantum computing and software.

Bigger context and future implications

Revolutionizing Quantum Chemistry: New QMCkl Library Breaks Computational Barriers for High-Accuracy Simulations

In a major leap for computational materials science, researchers have unveiled a high-performance software library designed to standardize and accelerate one of the most accurate, yet computationally demanding, methods in quantum chemistry. The Quantum Monte Carlo kernel library (QMCkl) represents a collaborative breakthrough led by scientists from the University of Twente and Université Paris-Saclay, aiming to solve the “electronic structure problem” that has long limited the simulation of complex systems. By providing a modular, portable collection of high-performance kernels, the library allows researchers to model materials with a level of precision and speed previously thought impossible.

You can also read Southeastern Quantum Collaborative(SQC) Launched By UAH

The Challenge of the Electronic Structure Problem At the center of modern chemistry and physics lies the quest to solve the Schrödinger equation for systems involving many interacting electrons. This is fundamental to understanding why drug molecules bind to proteins or how catalysts drive chemical reactions. However, electrons are “correlated,” meaning the movement of one instantly affects all others, making calculations exponentially more difficult as the system size increases.

While standard methods, such as Density Functional Theory (DFT), use approximations to simplify these interactions, these shortcuts often fail for complex materials. Quantum Monte Carlo (QMC) methods circumvent these approximations by employing statistical sampling to obtain the most accurate solutions; however, the substantial computational cost has historically limited their application to small, simple systems. QMCkl was specifically engineered to remove these traditional bottlenecks by streamlining the complex mathematics of electron interaction.

A Modular Solution Built on “Separation of Concerns” The development of QMCkl, led by Emiel Slootman, Vijay Gopal Chilkuri, and Aurélien Delval, introduces a fundamental shift in the architecture of quantum chemistry software. Rather than building a single, monolithic program, the team created a modular library of “kernels,” the essential building blocks for QMC calculations.

One of the most innovative features of QMCkl is its design philosophy, known as the “separation of concerns,” which was inspired by legendary numerical libraries like BLAS and LAPACK. This approach separates the scientific development of algorithms from the low-level hardware tuning required for speed. To achieve this, the library includes two distinct versions of every kernel:

A Pedagogical Version (Fortran): Written for clarity and correctness, allowing physicists to verify the underlying science and ensure readability. A High-Performance Version (C): Written by computing experts to maximize speed on modern hardware, including CPUs and specialized accelerators. Because both versions produce identical numerical results and are accessed through the same Application Programming Interface (API), scientists can trust the accuracy of the results while benefiting from a professionally optimized engine.

You can also read Quantum Research Initiative Workshop Held By the UArizona

Core Building Blocks and Technical Capabilities QMCkl implements the most intensive tasks required for high-accuracy simulations, ensuring consistent and reproducible results across various computer architectures. The library covers several essential components, including:

Atomic and Molecular Orbitals: Calculating the spatial distribution of electrons, including necessary gradients and Laplacians. Jastrow Correlation Factors: Managing the complex interactions between electron pairs and electron-nucleus pairs. Cusp Corrections: Ensuring mathematical models remain accurate when electrons are extremely close to atomic nuclei. Derivatives for Optimization: Providing the necessary calculations for variational and structural optimization. The library is hardware-agnostic and portable, supporting a C-compatible API that facilitates interoperability with languages like Python, Fortran, and C++.

Transformative Speedups and Real-Time Visualization The impact of QMCkl on research workflows is immediate and significant. The library delivers substantial speed improvements in evaluating energy and its derivatives, which are vital for finding the stable structures of new molecules.

In specific tasks like orbital analysis and data visualization, researchers recorded speedups of over 100 times compared to traditional tools. This leap in efficiency makes it possible to visualize electronic structures in real-time and perform large-scale screening of new materials, tasks that were previously too slow to be practical for daily research.

You can also read van Cittert Zernike Theorem: From Classical to Quantum Light

Interoperability and the Road to Exascale Computing Beyond QMC, the kernels in QMCkl can accelerate deterministic quantum chemistry workflows and visualization tools. The library supports the TREXIO data standard, which promotes seamless data exchange between different software packages such as CHAMP, QMC=Chem, and Quantum Package. This interoperability allows researchers to move data between tools without losing accuracy, using the best software for each specific stage of a simulation.

As the scientific community enters the era of “Exascale” computing, where supercomputers perform a quintillion calculations per second, QMCkl is designed to adapt. Its modular structure allows it to be updated for new computer architectures, including emerging AI-driven hardware, without requiring scientists to rewrite their entire research code.

Conclusion: A New Era of Precision

The release of QMCkl marks a turning point for the quantum chemistry community by lowering the barrier to high-accuracy simulations. For industries ranging from battery technology to pharmaceutical development, the ability to quickly and accurately predict electron behavior is a game-changer. By prioritizing modularity and high-performance, revolutionizing Quantum Chemistry: New QMCkl Library Breaks Computational Barriers for High-Accuracy Simulations

MediaTek Ultra 910

Maximising Chromebook Performance with Agentic AI

The MediaTek Kompanio Ultra redefines Chromebook Plus laptops with all-day battery life and the greatest Chromebooks ever. By automating procedures, optimising workflows, and allowing efficient, secure, and customised computing, agentic AI redefines on-device intelligence.

MediaTek Kompanio Ultra delivers unrivalled performance whether you're multitasking, generating content, playing raytraced games and streaming, or enjoying immersive entertainment.

Features of MediaTek Kompanio Ultra

An industry-leading all-big core architecture delivers flagship Chromebooks unmatched performance.

Arm Cortex-X925 with 3.62 GHz max.

Eight-core Cortex-X925, X4, and A720 processors

Single-threaded Arm Chromebooks with the best performance

Highest Power Efficiency

Large on-chip caches boost performance and power efficiency by storing more data near the CPU.

The fastest Chromebook memory: The powerful CPU, GPU, and NPU get more data rapidly with LPDDR5X-8533 memory support.

ChromeOS UX: We optimised speed to respond fast to switching applications during a virtual conference, following social media feeds, and making milliseconds count in in-game battle. Nowhere is better for you.

Because of its strong collaboration with Arm, MediaTek can provide the latest architectural developments to foreign markets first, and the MediaTek Kompanio Ultra processor delivers the latest Armv9.2 CPU advantage.

MediaTek's latest Armv9.2 architecture provides power efficiency, security, and faster computing.

Best in Class Power Efficiency: The Kompanio Ultra combines the 2nd generation TSMC 3nm technology with large on-chip caches and MediaTek's industry-leading power management to deliver better performance per milliwatt. The spectacular experiences of top Chromebooks are enhanced.

Best Lightweight and Thin Designs: MediaTek's brand partners can easily construct lightweight, thin, fanless, silent, and cool designs.

Leading NPU Performance: MediaTek's 8th-generation NPU gives the Kompanio Ultra an edge in industry-standard AI and generative AI benchmarks.

Prepared for AI agents

Superior on-device photo and video production

Maximum 50 TOPS AI results

ETHZ v6 leadership, Gen-AI models

CPU/GPU tasks are offloaded via NPU, speeding processing and saving energy.

Next-gen Generative AI technologies: MediaTek's investments in AI technologies and ecosystems ensure that Chromebooks running the MediaTek Kompanio Ultra provide the latest apps, services, and experiences.

Extended content support

Better LLM speculative speed help

Complete SLM+LLM AI model support

Assistance in several modes

11-core graphics processing unit: Arm's 5th-generation G925 GPU, used by the powerful 11-core graphics engine, improves traditional and raytraced graphics performance while using less power, producing better visual effects, and maintaining peak gameplay speeds longer.

The G925 GPU matches desktop PC-grade raytracing with increased opacity micromaps (OMM) to increase scene depths with subtle layering effects.

OMM-supported games' benefits:

Reduced geometry rendering

Visual enhancements without increasing model complexity

Natural-looking feathers, hair, and plants

4K Displays & Dedicated Audio: Multiple displays focus attention and streamline procedures, increasing efficiency. Task-specific displays simplify multitasking and reduce clutter. With support for up to three 4K monitors (internal and external), professionals have huge screen space for difficult tasks, while gamers and content makers have extra windows for chat, streaming, and real-time interactions.

DP MST supports two 4K external screens.

Custom processing optimises power use and improves audio quality. Low-power standby detects wake-up keywords, improving voice assistant response. This performance-energy efficiency balance improves smart device battery life, audio quality, and functionality.

Hi-Fi Audio DSP for low-power standby and sound effects

Support for up to Wi-Fi 7 and Bluetooth 6.0 provides extreme wireless speeds and signal range for the most efficient anyplace computing.

Wi-Fi 7 can reach 7.3Gbps.

Google Cloud is well positioned to provide enterprises with a unified, intelligent, open, and secure data and AI cloud. Dataproc, Dataflow, BigQuery, BigLake, and Vertex AI are used by thousands of clients in many industries across the globe for data-to-AI operations. From data intake and preparation to analysis, exploration, and visualization to ML training and inference, it presents BigQuery Studio, a unified, collaborative workspace for Google Cloud’s data analytics suite that speeds up data to AI workflows. It enables data professionals to:

Utilize BigQuery’s built-in SQL, Python, Spark, or natural language capabilities to leverage code assets across Vertex AI and other products for specific workflows.

Improve cooperation by applying best practices for software development, like CI/CD, version history, and source control, to data assets.

Enforce security standards consistently and obtain governance insights within BigQuery by using data lineage, profiling, and quality.

The following features of BigQuery Studio assist you in finding, examining, and drawing conclusions from data in BigQuery:

Code completion, query validation, and byte processing estimation are all features of this powerful SQL editor.

Colab Enterprise-built embedded Python notebooks. Notebooks come with built-in support for BigQuery DataFrames and one-click Python development runtimes.

You can create stored Python procedures for Apache Spark using this PySpark editor.

Dataform-based asset management and version history for code assets, including notebooks and stored queries.

Gemini generative AI (Preview)-based assistive code creation in notebooks and the SQL editor.

Dataplex includes for data profiling, data quality checks, and data discovery.

The option to view work history by project or by user.

The capability of exporting stored query results for use in other programs and analyzing them by linking to other tools like Looker and Google Sheets.

Follow the guidelines under Enable BigQuery Studio for Asset Management to get started with BigQuery Studio. The following APIs are made possible by this process:

To use Python functions in your project, you must have access to the Compute Engine API.

Code assets, such as notebook files, must be stored via the Dataform API.

In order to run Colab Enterprise Python notebooks in BigQuery, the Vertex AI API is necessary.

Single interface for all data teams

Analytics experts must use various connectors for data intake, switch between coding languages, and transfer data assets between systems due to disparate technologies, which results in inconsistent experiences. The time-to-value of an organization’s data and AI initiatives is greatly impacted by this.

By providing an end-to-end analytics experience on a single, specially designed platform, BigQuery Studio tackles these issues. Data engineers, data analysts, and data scientists can complete end-to-end tasks like data ingestion, pipeline creation, and predictive analytics using the coding language of their choice with its integrated workspace, which consists of a notebook interface and SQL (powered by Colab Enterprise, which is in preview right now).

For instance, data scientists and other analytics users can now analyze and explore data at the petabyte scale using Python within BigQuery in the well-known Colab notebook environment. The notebook environment of BigQuery Studio facilitates data querying and transformation, autocompletion of datasets and columns, and browsing of datasets and schema. Additionally, Vertex AI offers access to the same Colab Enterprise notebook for machine learning operations including MLOps, deployment, and model training and customisation.

Additionally, BigQuery Studio offers a single pane of glass for working with structured, semi-structured, and unstructured data of all types across cloud environments like Google Cloud, AWS, and Azure by utilizing BigLake, which has built-in support for Apache Parquet, Delta Lake, and Apache Iceberg.

One of the top platforms for commerce, Shopify, has been investigating how BigQuery Studio may enhance its current BigQuery environment.

Maximize productivity and collaboration

By extending software development best practices like CI/CD, version history, and source control to analytics assets like SQL scripts, Python scripts, notebooks, and SQL pipelines, BigQuery Studio enhances cooperation among data practitioners. To ensure that their code is always up to date, users will also have the ability to safely link to their preferred external code repositories.

BigQuery Studio not only facilitates human collaborations but also offers an AI-powered collaborator for coding help and contextual discussion. BigQuery’s Duet AI can automatically recommend functions and code blocks for Python and SQL based on the context of each user and their data. The new chat interface eliminates the need for trial and error and document searching by allowing data practitioners to receive specialized real-time help on specific tasks using natural language.

Unified security and governance

By assisting users in comprehending data, recognizing quality concerns, and diagnosing difficulties, BigQuery Studio enables enterprises to extract reliable insights from reliable data. To assist guarantee that data is accurate, dependable, and of high quality, data practitioners can profile data, manage data lineage, and implement data-quality constraints. BigQuery Studio will reveal tailored metadata insights later this year, such as dataset summaries or suggestions for further investigation.

Additionally, by eliminating the need to copy, move, or exchange data outside of BigQuery for sophisticated workflows, BigQuery Studio enables administrators to consistently enforce security standards for data assets. Policies are enforced for fine-grained security with unified credential management across BigQuery and Vertex AI, eliminating the need to handle extra external connections or service accounts. For instance, Vertex AI’s core models for image, video, text, and language translations may now be used by data analysts for tasks like sentiment analysis and entity discovery over BigQuery data using straightforward SQL in BigQuery, eliminating the need to share data with outside services.

Realme GT 7

The China-released Realme GT 7 improved smartphone performance on April 23, 2025. The GT 7's power, performance, and futuristic design reveal Realme's determination to challenge Samsung, Xiaomi, and OnePlus flagships.

The Realme GT 7's design, hardware, camera, and global release are addressed.

Display & Design

Like its predecessors, the Realme GT 7 is stylish and useful. The smartphone has a polished steel frame and a Nebula Blue and Starry Night Black glass back.

A 6.78-inch AMOLED screen with 144 Hz refresh rate and 1.5K resolution fronts the GT 7. Bright colours, deep blacks, and smooth scrolling make this screen spectacular. The display's 2,160Hz PWM dimming enhances low-light gaming, binge-watching, and everyday use.

Performance

The GT 7's next-generation MediaTek Dimensity 9400+ chipset and 3nm CPU are major advancements. Higher CPU and GPU speeds, efficiency, and AI processing than prior generations.

Realme promises rapid multitasking, app opening, and data transfer with up to 16GB of LPDDR5X RAM and 1TB of UFS 4.0 storage. Game visuals and productivity are good on Realme GT 7.

Advanced vapour chamber cooling lowers phone temperature after long usage or intense gaming.

Batteries and charging

Battery life is another GT 7 highlight. Its 7,200mAh battery is one of the largest on a premium phone. One charge gives this massive capacity over two days of constant use.

Realme doesn't lose battery capacity for charging speed. GT 7 can be charged from 0% to 50% in 15 minutes with 100W SuperVOOC quick charging. For busy individuals, its durability and rapid charging is groundbreaking.

Cam System

Realme invested much on the GT 7's camera. The phone has three cameras:

50MP Sony IMX890 OIS primary sensor.

Ultra-Wide 8MP Lens.

2-megapixel macro.

The main sensor captures sharp, color-accurate photos in bright and dim light. HDR, night photography, and scene detection benefit from AI. Even though they're not innovative, macro and ultra-wide lenses provide casual photographers additional alternatives.

A punch-hole hides a 32MP selfie camera on the front. Real-time beautification, 4K video capturing, and AI-powered social media photo enhancements are available.

AI, Software Features

Realme GT 7 comes with Realme UI 6.0 on Android 15. The UI is simple, fast, and customisable.

The way this device integrates AI-enhanced functionality is unique. This includes:

AI-based scene recognition.

Instant AI translation.

Performance and battery management using AI resource allocation. Picture filters, AI Night Mode.

These features enhance smartphone use and productivity by making it more fluid and intelligent.

Additional Features & Connectivity

Features of Realme GT 7:

5G and global band support.

WiFi 7 for lightning-fast internet.

5.4 Bluetooth.

screen-mounted fingerprint scanner.

HD audio and stereo speakers.

Realme also has IP65 water and dust protection, making it more durable in difficult environments.

Differences between Realme GT 7 and GT 7 Pro

Despite many similarities, the GT 7 Pro is the ultra-premium option with enhancements like:

Generation 4 Snapdragon 8.

Periscope zoom.

Vegan leather or ceramic.

Fast 120-watt charging.

An LPTO AMOLED display.

The GT 7 is more balanced, prioritising performance above luxury.

Available Worldwide

Realme GT 7, released on April 23, 2025, is only available in China. The company has not declared a global or Indian release date.

Due to the GT series' popularity and the GT 7 Pro's global success, the Realme GT 7 is expected to launch in India, Europe, and Southeast Asia in May or June 2025.

The cost in China

CNY 2,799 (~$385) for 8GB+256GB.

CNY 3,199 ($440) for 12GB+512GB.

CNY 3,799 ($520) for 16GB + 1TB.

The GT 7 will undercut rivals while offering equivalent top specifications, although costs may vary internationally.

Concluding

Realme GT 7 is another flagship and upper mid-range attempt by Realme to rule the market. With its powerful Dimensity 9400+ processor, huge battery, AI-enhanced camera, and smooth AMOLED display, the GT 7 may be one of the most cheap smartphones of 2025.