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The German start-up Q.ANT, best known for light-based data processing, is now enabling practical exploration of photonic computing via the cloud with its proprietary Native Processing Unit (NPU). By processing data with light instead of electrons, Q.ANT's photonic native computing technology is designed to perform complex computing tasks more efficiently than today's chip technologies. Thanks to the granting of cloud access to the company's NPU, users can now practically understand this innovative photonic chip technology thanks to a vivid example. Q.ANT particularly invites innovators and researchers to participate in a change that could reshape the digital landscape. With this demonstration, Q.ANT offers a glimpse into next-generation computing applications for high-performance computing (HPC), physics simulations and artificial intelligence. Interested parties can view the demo on the Q.ANT website.

Light versus silicon: NPUs' energetic advantages in data processing

According to Q.ANT, this showcase is representative of tasks that can be found in every data center today. The fundamental difference is that Q.ANT's NPUs process data with light, unlike conventional CMOS processors. This paradigm shift enables Q.ANT to perform basic mathematical operations much more energy-efficiently. For example, While a conventional CMOS processor requires 1,200 transistors to perform a simple 8-bit multiplication, Q.ANT's NPUs achieve this with a single optical element. For this operation alone, the Q.ANT NPU is 30 times more energy efficient than its conventional CMOS counterpart. "With the growing demand for AI, the need for energy-efficient solutions is also increasing. Q.ANT is leading the way with a working photonic processor – well beyond the research phase in which most others still find themselves," says Dr. Michael Förtsch, CEO of Q.ANT. "This demonstration is an important step in addressing the energy demands of AI and the broader challenge of carbon emissions. We invite researchers and developers to explore the real potential of photonic computing through our practical demonstration."

The secret ingredient: the chip material

A key element of this breakthrough is Q.ANT's proprietary chip material platform based on thin-film lithium niobate (TFLN). It is the backbone of all Q.ANT NPUs and ensures precise light control at the chip level. The startup has developed this platform since its foundation in 2018 and controls the entire value chain – from raw material to finished chip. Combined with a deep understanding of light, this enables Q.ANT to increase the mathematical and algorithmic density even beyond conventional CMOS processors. For example, While the fundamental mathematical function of a Fourier transform requires thousands to tens of thousands of complex multiplications, which corresponds to millions of transistors, optics achieve the same result with a single element. "The key to unlocking the potential of light for data processing lies in end-to-end control of light. Any compromise drastically reduces the probability of success. That is why we at Q.ANT, unlike all our competitors, have taken the deep-tech approach and developed a superior chip platform for light processing," says Förtsch.

Shifts in the industry

The semiconductor industry is turning to new technologies to meet the growing demand for computing power. This demand for high-performance yet energy-efficient technologies is further fueled by the widespread use of AI. Besides training new large language models, AI inference is a particularly energy-intensive AI application, and Q.ANT's NPUs are a promising solution. Gartner analysts describe photonic computing as an emerging computing paradigm that could address the performance and energy challenges in AI and data centers, and has identified Q.ANT as a "sample vendor" in recent Gartner Hype Cycle reports.

Test of the Q.ANT NPU

In the now activated demonstration system, the user can select an image of a handwritten number from the Modified National Institute of Standards and Technology (MNIST) database. Using a trained neural network, the NPU predicts the number (0-9) and performs an efficient matrix-vector multiplication on the photon chip. With a recognition accuracy of 95 percent, this demonstration proves that Q.ANT's photonic processor – which is powered by light and based on TFLN technology – can perform complex AI tasks while consuming less power. This is the first time that such a photonic processor has been successfully used in a practical application, highlighting its potential for AI tasks.

Overcoming the current limitations of AI and machine learning

The web demonstration of the photonic NPU provides valuable insights into how photonic computing can be used in practice to overcome the current limitations of AI and machine learning, paving the way for future advances in this transformative field. "We are developing native processors that solve today's logical problems natively by using light as a medium," said Förtsch. "Imagine a future in which high-performance computers operate with minimal energy expenditure and are at least as powerful as our brains – that is the vision behind native computing."

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