Co-Founder & CEO at Conductor Quantum
AI software for operating quantum computers
Building quantum computers on silicon chips using AI — creating qubits 1,000x faster than the manual methods that currently take engineers days or weeks per chip. PhD from the University of Oxford's Natalia Ares Group, specializing in AI for quantum computing in silicon. Worked with four quantum institutions globally and published multiple papers, including one in Nature. Also holds an MEng in Materials Science from Oxford. Conductor Quantum (YC S24) offers two products: Coda, a natural language interface for quantum computers, and Control, a low-level calibration platform supporting superconducting, spin, trapped-ion, and photonic systems.
Conductor Quantum builds AI-powered software for operating and scaling quantum computers. Their tools can create qubits up to 1,000x faster than manual methods by learning how quantum transport works in semiconductor chips, cutting the setup time from weeks to minutes.
They have two products. Coda is a natural language interface that converts problem descriptions into quantum circuits and runs them on processors like Rigetti's 84-qubit system and IonQ. Control is a calibration platform that handles gate tune-up, readout optimization, and crosstalk mitigation for any qubit type.
Right now, quantum engineers spend days or weeks manually configuring silicon chips to create a single qubit. But a useful quantum computer needs millions or billions of them. Conductor's AI automates that bottleneck and makes quantum hardware accessible to people who aren't quantum physicists.
Conductor Quantum (YC S24) has raised about $4M from 18 investors including Andreessen Horowitz, Qubits Ventures, Bullock Capital, DG Daiwa Ventures, and Gaingels. They've partnered with Finnish chip maker SemiQon and ran their software across 64 quantum devices. Brandon was named to Forbes 30 Under 30 in 2026.
Brandon Severin (CEO) did his PhD at the University of Oxford in the Natalia Ares Group, focusing on AI for quantum computing in silicon. He published several papers including one in Nature. Joel Pendleton (CTO) has an MSc in Physics from UCL and built ML-based control software at QuantrolOx, with research stints at Quantum Motion and C12. They met during their PhDs at Oxford.
The software is hardware-agnostic and works across all major qubit types: superconducting (like Rigetti), spin qubits in silicon (like SemiQon), trapped-ion (like IonQ), and photonic systems. Not being locked to one vendor is a big part of what sets them apart.
