Researchers at the National University of Singapore have developed a quantum random number generator chip that can verify the integrity of its own hardware while producing random numbers, addressing a long-standing security challenge in digital encryption systems.

Random numbers are critical for encryption keys, secure transactions, digital signatures, and other cybersecurity applications. Existing random number generators, including quantum versions, rely on users trusting that their hardware components continue to operate as intended. If a component degrades or is tampered with, the output could become predictable without being detected.

The new chip is designed to eliminate that risk by continuously checking whether its measurement hardware is behaving as expected during operation.

The research team, led by Associate Professor Charles Lim from the National University of Singapore’s Department of Electrical and Computer Engineering, says the approach could strengthen security in sectors ranging from finance and healthcare to artificial intelligence and connected devices.

Hardware trust removed

Most quantum random number generators operate using what researchers call a trusted-device model. Under this approach, components such as lasers, modulators, and detectors are assumed to match their specifications throughout their lifetime.

The new chip instead uses a measurement-device-independent protocol. This means users only need to trust the quantum light signals entering the system rather than the detector responsible for measuring them.

During operation, the chip generates known quantum light states and compares detector responses against predictions from quantum theory. If the results match expectations, the system converts the data into certified random numbers. If not, the process stops automatically.

“The measurement unit in quantum random number generators has traditionally been very difficult to characterize, making its real-world reliability hard to guarantee. Our solution removes the need to trust that this unit is operating as specified during use,” said Associate Professor Charles Lim.

The device integrates both the signal encoder and optical detector on a single silicon chip manufactured using an eight-inch wafer process commonly used in semiconductor production. Unlike some quantum technologies, it operates at room temperature and does not require cryogenic cooling.

Researchers also addressed a challenge involving silicon-based light modulators. Adjusting the phase of light can unintentionally change its brightness, potentially affecting security. The team developed a control method that compensates for this effect and maintains stable optical signals.

Security over speed

The chip’s detector achieved a total efficiency of 69.1 percent, exceeding the protocol’s minimum requirement of 67 percent. Tests also showed that the system generated more certified random bits than it consumed as an input seed, confirming that it produces fresh randomness.

Researchers describe the device as the most secure quantum random number generator chip demonstrated so far. Its security analysis assumes a worst-case scenario in which an attacker may possess quantum correlations with the detector itself.

That level of security comes with a trade-off. The current experimental system produces 64 bits per second, significantly slower than conventional quantum random number generators that can exceed 100 gigabits per second.

The team believes performance can improve substantially through better detector technology. Laboratory photodiodes developed by the researchers have already reached 92.4 percent efficiency, and simulations suggest future versions of the chip could achieve data rates of 68 megabits per second.

“This chip paves the way towards integrating practical self-testing quantum random number generators into compact, secure systems,” added Assoc Prof Lim.

The study was published in the journal PRX Quantum.

Semiconductor maker Texas Instruments has introduced a new battery monitoring chip designed to give electric vehicles and energy storage systems earlier warning of battery failures while reducing the number of components required inside battery packs.

The company announced the BQ79826Z-Q1 on Tuesday, calling it the industry’s highest-cell-count battery monitor with an integrated electrochemical impedance spectroscopy (EIS) engine. The device is designed to provide real-time insight into battery health and detect conditions that could lead to failures such as thermal runaway.

The new monitor supports up to 26 battery cells per device, allowing engineers to track more cells with fewer monitoring chips. According to TI, the device can monitor up to 44 percent more channels than previous-generation solutions, helping reduce system complexity, board space requirements, and overall battery pack costs.

The launch comes as automakers and energy storage developers look for ways to improve battery safety, extend battery life, and lower system costs while scaling production of EVs and large-scale storage systems.

Early fault detection

A key feature of the BQ79826Z-Q1 is its integrated EIS engine. The technology measures a battery’s electrochemical behavior in real time, giving engineers a view into changes occurring inside individual cells.

“Just as an electrocardiogram (EKG) monitors the heart, EIS monitors a battery,” the Dallas-based company said in a release.

The company says the technology can identify fault conditions earlier than conventional monitoring approaches by detecting changes from within battery cells. This could help battery management systems flag safety risks before they develop into more serious issues.

“The electrification of transportation and the rapid expansion of energy storage are redefining what battery performance must deliver, and as a leader in battery management technology, TI is uniquely positioned to meet that challenge,” said Wenjia Liu, vice president and general manager of battery management systems at TI.

Liu added that the technology helps “shine a light” inside battery cells and provides chemical-state data that software can use to make real-time decisions on battery safety and performance.

Fewer chips required

Beyond safety, TI says the higher cell count could simplify battery pack architecture. By supporting 26 cells per device, the monitor can reduce the number of monitoring components needed across a battery pack.

The company claims the chip supports eight more cells than competing solutions, enabling designers to lower bill-of-material costs while maintaining reliability.

The monitor can also be paired with TI’s BQ79881-Q1 pack monitor and communications bridge, allowing developers to use the same architecture across different battery chemistries, module sizes and mechanical designs.

TI says the device delivers voltage accuracy of less than 2 mV across temperatures ranging from -40°F to 257°F. The company also claims its EIS measurements are five times faster than previous solutions, helping improve state-of-charge calculations and battery health monitoring.

The BQ79826Z-Q1 complies with Automotive Safety Integrity Level D requirements and ISO 26262 functional safety standards. TI is showcasing the technology at the Power Conversion, Intelligent Motion Expo and Conference (PCIM) 2026 in Nuremberg, Germany.

UK has unveiled a £1.1 billion ($1.47 billion) plan to build domestic artificial intelligence computing capacity, including a national AI supercomputer and major funding for homegrown chip development, as countries race to secure control over advanced computing infrastructure.

The strategy was announced as part of a wider push to strengthen the country’s sovereign computing capability, with investments spanning supercomputing, semiconductor design, skills development, and venture funding for AI hardware startups. The government said the plan is aimed at improving national security, boosting innovation, and supporting economic growth.

A key component of the plan is a £750 million ($1 billion) national AI supercomputer scheduled for deployment in 2030. It will use a mixed chip architecture combining existing processors with next-generation hardware designed to handle complex AI workloads more efficiently.

The government is also allocating £400 million ($534 million) from this budget toward next-generation chips, including £150 million ($200 million) for inference chips to be purchased this summer from UK-based firms. Additional funds will support later-stage chip procurement as technologies mature.

The initiative also includes a £120 million ($160 million) AI hardware innovation program to help British companies design and test new chip technologies, alongside £45 million ($60 million) in skills funding to train engineers, chip designers, and researchers.

Chip race heats up

The plan comes as global demand for AI compute continues to rise sharply, driven by large-scale model training and deployment across industries. Governments and companies are now competing to secure access to advanced chips and computing infrastructure, which have become central to AI development.

A fund led by US venture capital firm Playground Global, with backing of up to £150 million ($107 million) from the British Business Bank, will also invest in UK-based AI hardware companies. The British Business Bank said this represents its largest single fund investment to date.

Technology Secretary Liz Kendall said, “AI is the defining currency of economic and hard power in today’s world and the countries that control the hardware behind it will hold the keys to the future.”

She added that the UK aims to strengthen domestic capability by supporting chip design and production, while ensuring the benefits of AI development remain within the country.

Building computer power

The national AI supercomputer will form part of the UK’s broader AI Research Resource, which already includes systems such as Isambard-AI and Zenith. The goal is to provide researchers, startups, and public services with greater access to high-performance computing.

Officials said the system will combine proven and next-generation processors in what is known as a heterogeneous architecture, allowing different chip types to handle different workloads more efficiently.

The government is also expanding its semiconductor skills pipeline, including doctoral training centers and undergraduate bursaries. The number of supported students is expected to rise in the coming years as demand for chip expertise grows.

The plan is part of a wider effort to position the UK as a competitive player in the global AI hardware and computing race, where the US and China currently dominate infrastructure and chip design.

OpenAI has confidentially filed for an initial public offering in the U.S., joining a growing wave of artificial intelligence companies preparing for public market debuts as investor appetite for AI accelerates.

The ChatGPT maker made the filing with the U.S. Securities and Exchange Commission on Monday, though it has not disclosed the size or timing of the offering. The move places OpenAI alongside rivals such as Anthropic and SpaceX-linked AI ventures that are also moving toward listing.

According to reports, OpenAI is targeting a valuation of up to $1 trillion in what could become one of the most closely watched IPOs in recent years. The listing could take place as early as September, depending on market conditions. The company has not confirmed timing.

In a statement, OpenAI said: “We recently submitted a confidential S-1. We expect it to leak so we’re just announcing it. We have not decided on timing yet; it may be a while because there are things we want to do that are likely easier as a private company. But it’s a complicated set of tradeoffs and this gives us the option to go public sooner if that ends up being best.”

AI valuation race

The IPO push comes amid a rapid escalation in valuations across the artificial intelligence sector. OpenAI was valued at about $852 billion in its latest funding round, while competitor Anthropic has reportedly reached a valuation close to $965 billion following its own confidential IPO filing.

The developments suggest that multiple trillion-dollar AI firms could hit public markets within a short time window, marking a rare concentration of high-growth listings in a single sector.

OpenAI has also scaled its commercial footprint sharply. The company says ChatGPT now has more than 900 million weekly active users and over 50 million paid subscribers. It also reported monthly revenue of about $2 billion earlier this year.

Shifting industry landscape

The company’s IPO filing follows a period of internal restructuring and strategic realignment. OpenAI has been working to transition its governance model into a public benefit corporation structure, a change aimed at easing capital access and enabling broader partnerships.

The company has also deepened ties with major technology players including Microsoft, Amazon, and Google, even as competition intensifies across the AI industry.

At the same time, OpenAI faces rising competition from Anthropic, whose Claude AI models have gained traction among developers and enterprise users. The rivalry has intensified as both firms expand into coding tools, enterprise software, and infrastructure-heavy AI systems.

OpenAI CEO Sam Altman described the current phase of the company’s evolution as the beginning of a broader economic shift driven by artificial intelligence.

“The central question now is how to make advanced AI abundant, affordable, safe, useful, and easy enough for every person and organization to benefit from it,” he said in a blog post.

The IPO filing also comes after legal tensions involving Elon Musk, who co-founded OpenAI in 2015. A U.S. jury recently ruled against Musk in a lawsuit challenging OpenAI’s shift away from its original nonprofit structure, removing a legal uncertainty that had weighed on the company’s public market prospects.