Quantum Companies, Quantum Funding

Alice & Bob’s €100M Round Gains NVIDIA Venture Capital Support

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May 31, 2026 by Ivy Delaney

Alice & Bob has extended its Series B funding round to €100 million with new investment from NVentures, the venture capital arm of NVIDIA, signaling strong confidence in the pursuit of practical quantum computing. This expansion builds on prior backing from Future French Champions, AVP, and Bpifrance, and underscores the potential of Alice & Bob’s unique technology. The companies have already established a substantial technical collaboration, integrating Alice & Bob’s qubits with NVIDIA technologies like CUDA-Q, cuQuantum, and Dynamiqs. “We’ve been working alongside NVIDIA to connect our cat-qubit architecture with its full accelerated computing ecosystem,” says Théau Peronnin, CEO of Alice & Bob, adding that the investment “reinforces our common view that the future of quantum will be hybrid, combining quantum and classical computing to solve real-world problems.”

NVentures Extends Series B for Fault-Tolerant Quantum Computing

Alice & Bob specializes in “cat qubits,” a proprietary technology developed by its founders, and recently demonstrated a potential reduction in hardware requirements for large-scale quantum computing by up to 200 times compared to alternative methods. The investment is not simply financial; a pre-existing technical collaboration between Alice & Bob and NVIDIA has been deepening, with integration work already underway utilizing NVIDIA’s CUDA-Q, cuQuantum, and Dynamiqs platforms. This collaboration aims to integrate cat qubits with NVIDIA’s accelerated computing infrastructure and software, bringing quantum computers to high-performance computing centers globally. NVIDIA views this partnership as crucial for developing hybrid quantum-GPU supercomputers.

Cat-Qubit Architecture Reduces Hardware Requirements for Scalability

Alice & Bob’s advancements in cat-qubit technology are reshaping the projected scale of practical quantum computers, potentially circumventing a major obstacle to widespread adoption. Recent demonstrations reveal their architecture can reduce the hardware needed for a functional, large-scale quantum computer by as much as 200 times when contrasted with alternative qubit designs, and this efficiency stems from the unique properties of “cat qubits” developed by the company’s founders. This reduction in hardware demands is particularly significant given the escalating costs and complexity associated with building and maintaining quantum processors, which currently require extensive infrastructure for error correction. The company’s progress has attracted further investment, with NVentures, NVIDIA’s venture capital arm, extending Alice & Bob’s Series B round to €100 million, signaling strong investor confidence in the potential of fault-tolerant quantum computing despite the field’s early stage. Timothy Costa, Vice President and General Manager of Quantum at NVIDIA, affirmed that “Alice & Bob shares NVIDIA’s vision for accelerated quantum supercomputing,” and that ongoing integration projects are underway to deploy these cat-qubits within high-performance computing centers globally.

We’ve been working alongside NVIDIA to connect our cat-qubit architecture with its full accelerated computing ecosystem, from hardware to software, in support of the first fault-tolerant quantum computers.

Théau Peronnin, CEO, Alice & Bob
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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