In 2024, quantum computing crossed a threshold it had failed to cross for 35 years: the entire industry went from noisy, unusable qubits to logical qubits - error-corrected, reliable, and ready to compute.

Infleqtion was one of the first companies through. Nvidia built the bridge.

Mark and Jeremy sit down with Pranav Gokhale, CTO of Infleqtion, and Sam Stanwyck, Group Product Manager for Quantum Computing at Nvidia, to understand how a four-microsecond connection between a GPU supercomputer and a quantum processor makes hybrid classical-quantum computing real for the first time.

This episode covers:

Why GPUs and quantum computers are complementary, not competing, one simulates nature, one parallelises data
How drug discovery, battery design, and material science become the first real quantum use cases
Why a 1,600-qubit quantum computer uses the same power as ten hairdryers
Infleqtion's roadmap to 100 logical qubits by 2028 and why that's the tipping point
A $20M NASA program sending a quantum gravity sensor to space. What Pranav calls "a telescope for underground"

⁠⁠Listen to every podcast⁠⁠
Follow us on⁠ ⁠Instagram⁠⁠
Follow us on⁠ ⁠X⁠⁠
Follow Mark on⁠ ⁠LinkedIn⁠⁠
Follow Jeremy on⁠ ⁠LinkedIn⁠⁠
Read our⁠ ⁠Substack⁠⁠
Email: ⁠hello@thinkingonpaper.xyz⁠

Chapters

(00:00) Why quantum computing matters right now

(01:20) Why Nvidia is betting big on quantum

(02:52) NVQ-Link: the bridge between quantum and classical computing

(09:29) Who decides what runs on the quantum computer vs the GPU?

(12:33) AI helping quantum, quantum helping AI

(16:56) Building a space elevator battery: a real quantum workflow

(20:09) The quantum algorithm zoo

(22:04) From noisy qubits to logical qubits

(24:00) How much energy does a quantum computer actually use?

(27:05) The no-cloning theorem: why you can't copy-paste quantum data

(27:20) The biggest unanswered question in quantum computing

(30:47) A $20M NASA program and a telescope for underground

(33:32) What do we want humans to be?