Investors Lab

Tensor networks and the quantum bargain: startups to watch

A 3-D of multi-colored balls connected by small, cylindrical tubes.

Illustration by Hawaii

By Susanna Camp

The future of the spatial economy is quite literally being built on the dust of the past.

As large language models (LLMs) like OpenAI’s GPT, Google’s Gemini, and Anthropic’s Claude continue to balloon in size, the infrastructure costs for training and operating them are becoming economically unsustainable. LLMs and the data centers they run on are also notorious ​energy hogs​. From the deepest-pocketed tech giants to municipalities to home consumers, we are all shouldering the financial load. Now, physicists are experimenting with quantum-inspired algorithms to fine-tune neural networks and compress their data footprints — which may just help keep the AI revolution from collapsing under its own weight. Science News’s Emily Conover brings the ​full technical breakdown​.

⚖️ Squishing the matrix: The art of tensor networks

Scientists are working to scrunch AI models with tensor networks, a mathematical framework borrowed from quantum physics that describes how complex systems interact. By applying this logic to AI models, they can essentially zip the data, identifying and keeping only the most critical correlations in the data while discarding the redundant noise. In a telling test of one LLM, where billions of numbers in parameters in the code determined how a chatbot processed prompts, compressing redundancies resulted in 30 to 40% less energy usage without sacrificing power.

😇 Computing with a conscience: Green AI models

The current trajectory of AI energy consumption is a major sustainability bottleneck, with data centers on track to consume a significant percentage of global electricity. Using quantum-inspired compression allows for the deployment of sophisticated models on “edge” devices (like smartphones or local sensors) rather than massive, heat-spewing server farms. Localizing the load can dramatically reduce the carbon footprint per query.

👀 The new infrastructure guard: Startups to watch

As industry trends shift from bigger-is-better to leaner-is-cleaner, IBM, Google and Microsoft are developing specialized quantum chips. A few startups are moving toward applying the quantum trick of tensor networks to streamline AI models and quantum computing:

  1. ​SandboxAQ​: A high-profile spin-off from Alphabet, this company focuses on the intersection of AI and large quantitative models (LQMs) to solve complex issues in drug discovery and cybersecurity, partnering with MIT and the U.S. Air Force, among others. The company has recently announced a collaboration with NVIDIA to use tensor networks to predict chemical reactions in applications such as drug discovery, advanced battery design, and clean energy. SandboxAQ has raised over $900 million, attracting heavyweight investors like T. Rowe Price, Allianz and BNP Paribas.
  2. ​Terra Quantum​: This Swiss “Quantum-as-a-Service” leader provides a platform that helps enterprises run quantum algorithms without specialized computing hardware. Their focus on tensor networks and model optimization helps firms run complex AI simulations with significantly less compute power. Terra Quantum has raised over $80million to date, positioning the company as a key European player in the quantum infrastructure race.
  3. ​Multiverse Computing​: Specializing in tensor networks for finance and manufacturing, this Spanish startup uses quantum-inspired techniques to optimize portfolios and supply chains. They’ve raised over $344 million, including a recently closed $220 million Series B funding round led by Toshiba, HP and Santander.

In the future, less will be more.

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