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IQM Accelerates Commercialization of Japan's First Enterprise-Grade Quantum Computer
Finnish quantum computing company IQM has secured Japan's first enterprise-grade quantum computer order, marking a new stage in the commercialization of quantum computing in Japan. The Finnish company will deploy a 20-qubit quantum computing system for Toyo Corporation by the end of 2026.
This collaboration highlights a trend: quantum computing is moving from the laboratory to enterprise infrastructure, particularly accelerating its deployment in high-performance computing and advanced manufacturing in Asia.
Enterprise-Grade Quantum Computing Moves Beyond Research
This quantum system, named Radiance, will be deployed at Toyo Corporation, supporting both local deployment and cloud access. This is Japan's first enterprise-built and self-used quantum computer, and it also strengthens IQM's presence in the Asia-Pacific region—the company already operates quantum computing systems in South Korea and Taiwan.
Toyo Corporation plans to use it for industrial R&D, integrating quantum capabilities with high-performance computing (HPC) infrastructure; simultaneously, it aims to cultivate local talent and support the implementation of Japan's national quantum strategy.
Japan has set an ambitious goal: to achieve tens of millions of domestic users of quantum technology and a quantum industry economic scale exceeding 50 trillion yen by 2030. To achieve this goal, it's not enough to remain at the level of theoretical research; the commercialization of physical quantum hardware must be widespread.
The industry is accelerating the practical application of quantum computing.
Jan Goetz, CEO and co-founder of IQM, stated that leading companies are truly building core quantum capabilities by constructing and operating their own quantum infrastructure. Toyo Corporation's move, relying on IQM's leading technology, is an important step in the implementation of Japan's national quantum strategy.
Toyo Corporation President Toshiya Kono added that quantum technology is a core strategic area for future economic growth and a key support for the new era of Japanese manufacturing. Industry competition has shifted from theoretical research to the practical application track of supercomputing integration, scenario development, and talent cultivation. The company will collaborate with IQM to promote Japan's global leadership in the application of quantum technology on a large scale.
This collaboration reflects the industry trend: quantum computing has entered a new stage, with enterprise commercialization, quantum-supercomputing hybrid architectures, and real-world applications becoming the watershed for core competitiveness in the industry.
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According to Reuters, citing an internal ruling by the Competition Commission of India (CCI) on April 8, the final hearing in the case is now set for May 21 due to Apple's continued refusal to submit the global financial and operational data necessary for regulatory accounting and penalties. This signifies a significant acceleration of the antitrust process against Apple in India, with no longer tolerating companies using legal disputes as a pretext to delay the investigation.
This cross-border regulatory battle began as early as 2021. At that time, a coalition of Indian developers and social media app companies collectively complained about Apple, alleging that the App Store's closed operating model contained monopolistic practices, such as forcing apps to use in-app payment channels, charging high platform commissions, squeezing out local manufacturers' survival space through the closed iOS ecosystem, and undermining fair competition in the Indian digital app market. After several years of comprehensive investigation, the CCI officially determined in 2024 that Apple had abused its market dominance, but did not impose a specific penalty.
In 2024, India revised its competition laws, breaking with the previous practice of calculating fines solely based on a company's local revenue. A new clause was added to levy antitrust penalties based on the global revenue of multinational corporations, with a maximum penalty of 10% of the average global revenue over three years. According to this rule, Apple could face a $38 billion fine in this case, breaking the record for antitrust penalties in the global technology industry.
Indian authorities pointed out that since the new regulations took effect, Apple has refused to disclose core financial details of its global services business, citing an ongoing constitutionality lawsuit in the Delhi High Court. Apple has been slow to cooperate with regulators in calculating the fine, attempting to prolong the investigation and evade a huge penalty through legal proceedings. Faced with Apple's continued lack of cooperation, Indian regulators repeatedly rejected applications for a stay of investigation, no longer allowing pending litigation to interfere, and forcefully pushing forward the enforcement process while providing a short-term window for supplementary reporting, forcing Apple to acknowledge its compliance obligations.
Apple, on the other hand, has consistently denied any antitrust violations and strongly opposes India's global revenue penalty rule. The company argues that its limited market share in India, coupled with the issue of operating in a single region, makes the penalty based on global revenue unfair, violating international regulatory logic and constituting an unreasonable rule. The CCI's position is clear: penalizing based solely on Indian revenue is insufficient to effectively constrain global giants; refusal to submit data will directly limit Apple's room for appeal; and regulators can determine the final penalty amount based on existing evidence.
Currently, India is a core growth market for Apple globally. In just two years, its iPhone market share there has climbed from 4% to 9%. Apple continues to expand its supply chain and shift production capacity in India, deepening its market dependence. Following the hearing on May 21, India is highly likely to issue a final penalty quickly, and Apple will inevitably continue to appeal, potentially leading to a protracted legal battle.
It is widely believed that this dispute is not merely a compliance crisis for Apple in a single market, but also reflects the overall trend of tightening regulations on global tech giants in emerging markets. India's adoption of global revenue penalties may trigger other countries to follow suit, continuously changing the global anti-monopoly compliance landscape for multinational internet and technology companies. Closed-loop app stores and platform commission models will face stricter regulatory constraints globally.
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Samsung Electronics' 2nm Yield Improves to 55%, Still Significantly Lagging Behind TSMC
Industry data shows that Samsung Electronics has made progress in its 2nm process technology, with its yield rate improving from less than 20% in the second half of last year to around 55% currently. However, this achievement still lags behind its competitor TSMC by about 10%, with the latter's yield rate remaining stable in the 60%-70% range.
Industry analysts point out that if performance grading and losses in the back-end packaging and testing processes are considered, Samsung's actual effective yield rate for its 2nm process may be as low as 40%. This level not only fails to meet the foundry needs of major fabless customers like Qualcomm (who typically require around 70%), but also highlights Samsung's shortcomings in the stability of advanced process mass production.
According to supply chain sources, the yield gap has prompted Qualcomm to consider shifting its next-generation application processor orders from Samsung to TSMC. This potential shift reflects the core competitive logic of the chip industry: at advanced process nodes, yield directly determines a company's profitability and market competitiveness.
Currently, TSMC, with its more mature 2nm process, has secured orders from leading customers such as Apple, Nvidia, and AMD. If Samsung successfully secures another order from Qualcomm, it will further solidify its leading position in the 2nm field. For Samsung, despite continuous improvement in yield rates, catching up with TSMC still faces serious challenges, especially in meeting the high standards of major customers for stable mass production.
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The global semiconductor industry is witnessing a new round of technological competition, and TSMC's 2nm process technology has become a scarce resource fiercely contested by major tech giants. Reports indicate that TSMC's 2nm capacity is fully booked, with scheduling extending to 2028, highlighting its dominant position in advanced process technology.
Why is the 2nm process so sought after?
TSMC's 2nm process utilizes a new generation of GAA (Gate All-Around) architecture. Compared to the previous FinFET technology, GAA significantly improves current control capabilities through a nanosheet stacking structure, while effectively suppressing leakage current. According to industry data, the 2nm process can achieve a 10% to 15% performance improvement at the same power consumption, or reduce power consumption by 25% to 30% without changing performance. This technological leap makes it an ideal choice for high-performance computing, artificial intelligence chips, and mobile devices.
The battle for customers intensifies.
Tech giants including Nvidia, AMD, Qualcomm, Apple, and MediaTek have all joined the 2nm order queue. Although mass production is expected by the end of 2026, TSMC's plan to increase monthly capacity to 100,000 wafers will still be insufficient to meet the explosive demand. Analysts point out that the extended capacity schedule to 2028 reflects both the urgent need for advanced processes and the strained semiconductor supply chain.
TSMC's Process Strategy
Unlike Samsung Electronics' rapid advancement in 2nm technology, TSMC focuses more on process maturity and yield control. While Samsung has already started mass production of 2nm GAA, its performance has fallen short of expectations compared to 3nm, while TSMC has won customer trust with its stable technological approach. Furthermore, TSMC's capital expenditure in 2026 is projected to climb to a record high of $48 billion to $50 billion, further solidifying its technological leadership.
Industry Impact and Future Outlook
The shortage of 2nm capacity may prompt some customers to switch to other foundries, with Samsung potentially being a beneficiary. However, in the short term, TSMC will remain the dominant player for most companies. With the rapid development of technologies such as artificial intelligence and autonomous driving, the demand for advanced processes will continue to grow. How TSMC balances capacity expansion and technological iteration will be a key point to watch in the future of the semiconductor industry.
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San Jose, CA – March 17, 2026 – NXP Semiconductors N.V. (NASDAQ: NXPI) announced an innovative robotics solution that provides reliable, secure real-time data processing and transmission, as well as advanced networking capabilities, supporting sensor fusion, machine vision, and precision motor control. As the first result of NXP’s portfolio of foundational robotics solutions, this deployable solution, jointly developed by NXP and NVIDIA, combines the NVIDIA Holoscan Sensor Bridge with NXP’s highly integrated System-on-Chip (SoC). This solution reduces the number of discrete components, significantly reduces footprint, lowers power consumption and cost, while simplifying the software complexity of robot perception and execution, and is also applicable to humanoid robot forms.
Physical AI represents the next frontier of innovation, characterized by systems that can accurately, reliably, and safely perceive, understand, and interact with their environment. Humanoid robots represent one of the most advanced forms of physical AI, requiring secure, reliable, and low-latency data processing and transmission throughout the robot's body to achieve synchronized motion, high-density sensor fusion, and advanced actuation control.
NXP's newly launched integrated robot body solutions address this challenge by providing powerful edge intelligence and low-latency network connectivity for secure and reliable real-time communication. These solutions seamlessly integrate NVIDIA Holoscan Sensor Bridge into NXP's software enablement architecture, allowing developers to easily implement real-time processing and establish direct transmission paths between the robot body and pre-defined areas of its "brain," significantly reducing latency. This significantly simplifies the challenges of bringing AI into the physical world, with real-time decision-making being a key requirement.
Charles Dachs, Executive Vice President and General Manager of the Secure Connected Edge Business Unit at NXP Semiconductors, stated, “Physical AI is redefining the capabilities of machines in the real world, and humanoid robots represent the most complex manifestation of this transformation. By combining NXP’s deep expertise in edge processing, secure networking, functional safety, and real-time control with NVIDIA’s robotics platform, we are significantly simplifying the development process for physical AI, enabling seamless connectivity between the physical AI edge and the central brain. In the future, NXP will bring more groundbreaking innovations to accelerate the development of the physical AI ecosystem; the achievements showcased today are just the tip of the iceberg.”
Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA, stated, “The development of autonomous machines requires a high-performance computing architecture to synchronize complex motor control with real-time perception. By integrating the NVIDIA Holoscan Sensor Bridge into its edge product portfolio, NXP provides developers with a scalable foundational platform to accelerate the deployment of physical AI.”
NXP and NVIDIA are working closely together to help define a unified architecture suitable for full-body humanoid robots. NXP's edge processors, motor control MCUs, automotive-grade networking technology, high-throughput asymmetric data transfer capabilities acquired through the acquisition of Aviva Links, and functional safety expertise built on decades of experience in the automotive industry, combined with NVIDIA's AI infrastructure, create a flexible, energy-efficient system architecture for next-generation robots.
The first solutions in NXP's robotics portfolio to support the Holoscan Sensor Bridge include a machine vision solution based on the i.MX 95 application processor, which transmits high-bandwidth data to the robot's "brain"; and a motor control solution based on the i.MX RT1180 crossover MCU kinematics chain, aggregated by an NXP S32J TSN switch and directly connected to the "brain." This motor control solution integrates support for mainstream industrial protocols such as EtherCAT® and Time-Sensitive Networking (TSN). These flexible, software-driven solutions employ a highly integrated design that effectively reduces footprint, power consumption, and cost without sacrificing performance, functional safety, or information security, providing a complete and scalable foundational platform for the design of full-body humanoid robots.
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IBM and Lam Research Join Forces to Usher in a New Era of Breakthrough in Sub-1nm Process Technology
A Powerful Alliance: Mutual Empowerment of Technological R&D and Equipment Innovation
This collaboration focuses on three core areas: novel semiconductor materials, advanced etching/deposition processes, and High NA EUV (High Numerical Aperture Extreme Ultraviolet) lithography technology. IBM, leveraging its technological accumulation in 2nm chip development (as of 2021), will provide support for logic process and chip architecture design; while Lam Research, relying on its leading advantages in etching and deposition equipment, will address the equipment compatibility challenges faced at the sub-1nm node. The two parties will form a joint team to build a complete nanosheet device process flow at IBM's Albany campus, achieving closed-loop verification from R&D to mass production.
Breaking Physical Limits: Three Key Technological Breakthroughs
New Material Exploration: Overcoming the limitations of quantum tunneling effects in traditional silicon-based materials to develop more stable and efficient semiconductor substrate materials;
Process Optimization: Improving etching precision and thin film deposition uniformity to address the process challenges brought about by the surge in circuit density at the sub-1nm node;
High NA EUV Lithography Deployment: Collaborating with ASML to commercialize 8nm resolution lithography technology with single-exposure capability. This technology offers a 40% improvement in imaging contrast compared to traditional EUV, making it crucial for sub-1nm mass production.
Targeting the AI Era: A Future Blueprint for High-Performance Chips
Both parties clearly stated that the core objective of this collaboration is to develop low-power, high-performance transistors for the AI era. With the explosive growth in computing power demand, the sub-1nm process is expected to elevate chip performance to new heights while reducing energy consumption. Lam Research executives emphasized, "This is a deep, collaborative revolution in equipment and processes, not just a process iteration."
Industry analysts point out that the collaboration between IBM and Lam Research may accelerate the global semiconductor industry's technology race, and the breakthroughs and commercialization of sub-1nm technology over the next five years are highly anticipated.
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Global AI computing giant Nvidia recently announced a strategic partnership with leading optics technology company Coherent, investing $2 billion (approximately RMB 13.74 billion) to support its R&D and capacity expansion. This multi-year, non-agreement also includes billions of dollars in procurement commitments aimed at providing core technological support for next-generation AI infrastructure.
According to the agreement, Nvidia will gain a stable supply chain and access to Coherent's advanced laser and optical networking products and production capacity. The collaboration will focus on optical interconnects and advanced packaging integration technologies, which are considered key foundations for building future AI data centers, enabling ultra-high bandwidth and high-efficiency data transmission solutions.
Notably, this $2 billion investment will directly support Coherent's manufacturing capabilities in the United States, including major infrastructure projects such as new wafer fabs. This partnership not only continues the two companies' 20-year partnership but also signifies industry recognition of the strategic value of optical technology in the AI era.
Industry analysts point out that this investment by Nvidia is a crucial part of its efforts to build a complete AI ecosystem. Following its previous investments in companies such as CoreWeave and OpenAI, NVIDIA is leveraging both capital and technology to secure its core position in the artificial intelligence industry chain. Coherent CEO Jim Anderson stated that this collaboration will accelerate innovative breakthroughs in optical interconnect technology for AI data centers, providing stronger infrastructure support for future AI systems.
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Gaurav Gupta, Research Vice President at Gartner, points out that countries pursuing digital sovereignty are accelerating the development of their local AI technology stacks, attempting to break free from dependence on the closed US model. Trust, cultural compatibility, and compliance have replaced dataset size as the core considerations for AI platform selection. The material revolves around the definition and driving factors of AI sovereignty, clarifying that its core is the independent control a country or organization has over the development, deployment, and application of AI within its own territory. It also reveals the unique advantages of localized models, the investment requirements for building AI sovereignty, and its impact on global cooperation and the industrial landscape.
Gartner Research Vice President Gaurav Gupta stated, “Countries pursuing digital sovereignty are increasing their investment in local AI technology stacks in an effort to find alternatives to the closed US model, including computing power, data centers, infrastructure, and models that conform to local laws, cultures, and regional characteristics. Trust and cultural fit are becoming key factors. Policymakers are prioritizing AI platforms that align with local values, regulatory frameworks, and user expectations, rather than platforms with the largest training datasets.”
Localized models are more valuable in contextual understanding. In areas such as education, legal compliance, and public services, regional large language models (LLMs) outperform global models, especially in non-English language scenarios.
By 2029, countries will need to allocate 1% of their GDP to AI sovereignty building.
As clients in non-Western countries and regions adjust their collaboration strategies due to concerns about the effects of excessive “Westernization,” AI sovereignty will lead to reduced collaboration and exacerbate redundant resource allocation. Therefore, Gartner predicts that by 2029, countries committed to building sovereign AI technology stacks will need to allocate at least 1% of their GDP to AI infrastructure development.
AI sovereignty refers to the ability of a nation or organization to independently control the development, deployment, and application of AI within its geographical boundaries.
Driven by regulatory pressure, geopolitical factors, cloud localization, national AI initiatives, corporate risks, and national security concerns, governments and enterprises are increasing their investment in sovereign AI development. Simultaneously, concerns about falling behind in the AI technology race will also prompt nations and enterprises to accelerate innovation and increase investment, striving for self-sufficiency in all aspects of the AI technology stack.
Gupta stated, “Data centers and AI factory infrastructure constitute the core foundation of the AI technology stack supporting AI sovereignty. Therefore, the future will see explosive construction and investment in data center and AI factory infrastructure, driving a few companies that control the AI technology stack to achieve double-digit ‘trillion-dollar’ valuations.”
Based on this, Chief Information Officers (CIOs) must:
Design model-neutral workflows based on orchestration layers to enable cross-regional and cross-vendor LLM switching.
Ensure that AI governance, data residency, and model tuning practices comply with the specific legal, cultural, and linguistic requirements of each country.
Establish partnerships with national cloud providers, regional LLM vendors, and leaders in sovereign AI technology stacks in key markets, and build a vetted list of partners.
Closely monitor AI legislation, data sovereignty rules, and new standards that may impact the deployment of AI models and the location and manner of user data processing.
Summary
Gartner's predictions clearly define the intensity of investment in future AI sovereignty development—by 2029, relevant countries will need to invest at least 1% of their GDP in AI infrastructure. This foreshadows explosive growth in core infrastructure such as data centers and AI factories, but also warns of potential challenges such as redundant resource investment and weakened global cooperation. For CIOs, adapting to the AI sovereignty trend, building flexible and compliant AI workflows, and deepening local cooperation are key to navigating change. Overall, AI sovereignty has reshaped the global AI industry landscape, and in the future, technological autonomy, compliant control, and collaborative innovation will be the core directions for countries to achieve sustainable AI development.
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Intel (INTC) and AMD (AMD) have reportedly informed Chinese customers of supply shortages of their server central processing units (CPUs), with Intel warning of delivery times of up to six months.
Reuters points out that while prices vary depending on customer contract terms, the supply constraints have already led to a general price increase of over 10% for Intel server products in China.
Sources say these recent notices to Chinese customers (within weeks) indicate that the CPU shortage has worsened. This could pose even greater challenges for artificial intelligence companies and many other manufacturers.
The report adds that the Chinese market accounts for more than 20% of Intel's total revenue, and the supply of its fourth- and fifth-generation Xeon CPUs is particularly tight, with Intel implementing rationing.
Meanwhile, AMD has also reportedly informed customers of supply constraints, with delivery times for some AMD products extending to 8 to 10 weeks.
Intel mentioned CPU supply constraints during its January earnings call. In a statement to Reuters, the company said the rapid adoption of artificial intelligence has driven strong demand for "traditional computing."
The statement said, "We expect inventory levels to be at their lowest in the first quarter, but we are actively working to address this and anticipate improved supply conditions starting in the second quarter of 2026."
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Atlas Demonstrates How Deep Technology Survives
In this year's global AI robot boom, the humanoid robot Atlas is undoubtedly the brightest star. However, the American robotics company behind it, Boston Dynamics, has never achieved sustained profitability since its founding in 1992, and has repeatedly faced capital impairment issues. Even after Hyundai Motor Group acquired it for approximately $1.1 billion in 2021, the company's accumulated losses over the past four years have still exceeded 1.2 trillion won.
If this company had been based in South Korea, it would likely have been labeled a "zombie company" and eliminated from the market within five years. It's hard to imagine that the capital market would be willing to wait 34 years for a technology to fully mature. Atlas's fluid joint movements on the stage at the Consumer Electronics Show 2026 last month were the result of decades of continuous investment and patient waiting. In the United States, how exactly do these seemingly "irrational" hardcore technology companies survive?
Boston Dynamics originated as a spin-off from a laboratory at MIT. In 1992, Mark Raibert, then a professor at MIT, spun off his "Leg Labs" into a company. In an era when robots were largely science fiction, the company's development was entirely reliant on government funding.
This support went far beyond a one-time startup grant. The Defense Advanced Research Projects Agency (DARPA) became the company's first client, assigning it a series of demanding research tasks. Behind these orders were highly forward-thinking ideas—such as developing transport robots to replace soldiers in dangerous terrain.
DARPA is widely recognized as the cradle of core American technology, investing huge sums of money in projects with a high probability of failure. The origins of today's mainstream technologies, such as the internet and voice assistants, can be traced back to this agency. In a 2016 white paper, DARPA officials wrote: "If none of our projects fail, it means we haven't taken enough risks." For a long time, the agency has maintained a tolerant attitude towards project failure rates as high as 90%.
It was with DARPA's support that Boston Dynamics was able to set aside the pressure of short-term profits and focus on 20 years of long-term technological accumulation. The quadruped robot "BigDog," launched in 2005, and Atlas, the first bipedal humanoid robot, launched in 2013, both originated from these long-term research projects. The US Congress has never criticized DARPA for the lack of immediate commercial results from these projects.
Entering the 2010s, robotics technology began to attract investment from major technology companies. Although the robotics market was still in its infancy, large corporations increasingly viewed it as a future growth engine. Google's $500 million acquisition of Boston Dynamics in 2013 vividly illustrates this trend and created a "capital greenhouse" for the company.
In 2017, Boston Dynamics was sold to SoftBank Vision Fund, and finally, in 2021, it came under the umbrella of Hyundai Motor Group, thus realizing the possibility of synergistic development with the manufacturing industry. This 34-year development process has been a convergence of multiple forces: universities, represented by MIT, sowed the seeds of technology; DARPA provided the nourishment for its growth; and global capital acted as a greenhouse, turning imagined technologies into reality. Now, Boston Dynamics' technology is finally seeing the dawn of mass production.
The situation in South Korea presents a stark contrast. In South Korea, it is extremely difficult for university laboratories to spawn businesses, and the government struggles to sustain funding for long-term research projects that have not yet yielded significant results. Acquisitions of startups by large corporations are extremely rare: only about 3% to 4% of startups in South Korea exit through mergers and acquisitions, with initial public offerings (IPOs) being almost the only viable way for companies to exit capital; in the United States, nearly 90% of startups are eventually acquired.
Regulatory barriers are a key reason why large-scale corporate mergers and acquisitions are difficult to pursue in South Korea. When large South Korean conglomerates acquire local startups, they face a series of stringent regulations related to the designation of subsidiaries; if post-acquisition performance fails to meet expectations, corporate executives may be charged with breach of fiduciary duty, and the resale market for these acquired companies is also very limited. Against this backdrop, the domestic startup ecosystem is already lagging behind, making the creation of a hardcore technology startup almost a pipe dream.
The Lee Jae-myung administration in South Korea recently launched a startup selection program and declared its intention to build South Korea into an entrepreneurial society. This move is undoubtedly a positive step towards fostering an entrepreneurial boom, but we should not overlook the fact that the growth of world-class hardcore technology has never relied on glamorous events, but rather on patient capital willing to tolerate failure, and the institutional support system that makes this patience possible.
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