Conductor Quantum: Scaling Silicon Quantum Computing

Conductor Quantum is a San Francisco–based deep-tech startup founded in 2024 with an ambitious goal: to make large-scale quantum computing on silicon chips possible by removing humans from the most fragile and time-consuming parts of the process. Operating at the intersection of quantum hardware, artificial intelligence, and automation software, the company is tackling one of the most fundamental bottlenecks in quantum computing today—scalability.

Quantum computing has long promised transformative breakthroughs in areas such as drug discovery, materials science, chemistry, and fundamental physics. Yet despite decades of research and billions of dollars invested globally, practical quantum computers remain elusive. The challenge is not theoretical potential, but execution. Conductor Quantum exists because the current approach to building and operating quantum systems simply does not scale.

By developing AI-driven software that automates quantum chip control and abstracts away low-level complexity, Conductor Quantum is positioning itself as a foundational layer for the future of silicon-based quantum computing.

Why Is Scaling Quantum Computers So Difficult?

At the heart of every quantum computer is the qubit—the quantum equivalent of a classical bit. While classical computers rely on billions of stable, identical transistors, quantum computers require qubits that are exquisitely sensitive to their environment and notoriously difficult to control.

Today, quantum engineers often spend days or even weeks manually tuning silicon quantum chips just to bring two qubits into an operational state. This process involves adjusting voltages, magnetic fields, and control parameters at an extremely fine level. Each device behaves slightly differently, and small variations can completely break quantum behavior.

This manual, artisanal approach is fundamentally incompatible with the long-term vision of quantum computing. To unlock real-world impact, quantum computers will need not tens or hundreds of qubits, but millions—eventually billions. Without automation, that scale is unreachable.

Conductor Quantum is built on the belief that quantum computing will never scale if every qubit must be individually handcrafted by human experts.

How Does Silicon-Based Quantum Technology Fit Into the Future?

Unlike some quantum platforms that rely on exotic materials or extreme experimental setups, silicon-based quantum computing builds on the same semiconductor foundations that power today’s classical computing industry. This makes silicon an especially attractive platform for scaling quantum hardware using existing fabrication infrastructure.

However, silicon quantum devices introduce their own challenges. Variability at the atomic level, complex electrostatic environments, and tight coupling between control parameters make silicon qubits exceptionally hard to tune manually.

Conductor Quantum focuses specifically on silicon because it represents the most realistic path toward mass-manufactured quantum processors. The company’s strategy is not to fight complexity with more human labor, but to embrace it with intelligent automation.

What Role Does AI Play in Conductor Quantum’s Vision?

Artificial intelligence is central to Conductor Quantum’s approach. Rather than relying on static calibration routines or handcrafted control scripts, the company develops AI systems that learn how to form, tune, and control qubits automatically.

These systems analyze device behavior, adapt to variability, and optimize performance without human intervention. In effect, the software learns how each quantum chip behaves and continuously improves its control strategies over time.

This “remove the human from the loop” philosophy is not about replacing researchers—it is about freeing them from repetitive, low-level tasks so they can focus on higher-level system design and scientific discovery. Automation, in this context, becomes the only viable path to scaling quantum technology beyond laboratory demonstrations.

Who Are the Founders Behind Conductor Quantum?

Conductor Quantum was founded by Brandon Severin and Joel Pendleton, two researchers with deep roots in quantum physics and hands-on experience across multiple quantum computing paradigms.

Brandon Severin, Co-Founder and CEO, completed his PhD at Oxford, where he worked with leading quantum institutions including IST Austria, the University of Basel, UNSW, and Diraq. His research focused on semiconductor quantum device control, and he published multiple papers on the topic, including work in Nature. During this time, he also developed early AI systems for automated qubit formation—ideas that later became foundational to Conductor Quantum.

Joel Pendleton, Co-Founder and CTO, brings a broad perspective from working across diverse quantum technologies, including carbon nanotubes and superconducting transmon qubits. Before founding Conductor Quantum, he built machine-learning-based control software for superconducting qubits at QuantrolOx and held roles at Quantum Motion and C12. He left his PhD program at Oxford to pursue Conductor Quantum full-time, signaling strong conviction in the company’s mission.

Together, the founders combine academic depth with startup execution, positioning Conductor Quantum at the intersection of research and real-world deployment.

What Is Coda and How Does It Change Quantum Computing?

One of Conductor Quantum’s most visible products is Coda, a natural language interface for quantum computing. Coda represents a shift in how people interact with quantum hardware, moving away from hand-written quantum circuits toward intent-based computing.

Instead of writing low-level code, users describe what they want to achieve in natural language. Coda then constructs the appropriate quantum circuit, verifies the program, and executes it on real quantum computers.

This abstraction mirrors how modern software development evolved—from assembly language to high-level programming languages. Conductor Quantum believes quantum computing must follow a similar trajectory if it is to reach a broader audience.

Why Is Natural Language So Important for Quantum Adoption?

Today, using a quantum computer requires deep expertise in quantum mechanics, device physics, and specialized tooling. This limits access to a small group of experts and slows innovation.

Coda lowers this barrier by allowing users to focus on problems rather than implementations. Researchers in chemistry, materials science, or physics can describe their objectives directly, without needing to become quantum engineers themselves.

For newcomers, Coda includes a “learn mode” that introduces quantum concepts interactively. This educational layer helps bridge the gap between theory and practice, making quantum computing more approachable without oversimplifying its power.

What Hardware Does Coda Support Today?

Coda already runs on real quantum hardware, including Rigetti’s 84-qubit quantum computer (S14). In addition to physical quantum processors, the platform supports quantum simulations of up to 34 qubits using NVIDIA’s cuQuantum GPU-accelerated framework.

This hybrid approach allows users to prototype, test, and scale quantum workloads while balancing realism and accessibility. It also lays the groundwork for tighter integration between classical and quantum computation.

What Has Conductor Quantum Achieved So Far?

Despite being a young company, Conductor Quantum has already reached several significant milestones.

The team built the first API for low-level silicon quantum chip control, providing a standardized interface for interacting with complex quantum hardware. They partnered with silicon quantum chip maker SemiQon and successfully ran their software across 64 quantum devices, demonstrating robustness at scale.

In addition, Conductor Quantum has shipped quantum control software to companies such as Quobly and EeroQ, proving that their tools can operate beyond the lab and deliver real value to hardware teams.

What Is the Roadmap for Conductor Quantum?

Looking ahead, Conductor Quantum is focused on closing the loop between high-level intent and device-level execution.

One major goal is end-to-end GPU and QPU orchestration, enabling classical and quantum computation to work together in a single, unified workflow. This hybrid model is widely seen as essential for near-term quantum advantage.

Another key milestone is connecting Coda directly to Conductor Quantum’s lower-level control and tune-up software. This would allow a user’s natural language request to propagate all the way down to automated qubit formation and hardware execution, without manual intervention at any stage.

As quantum hardware scales to larger numbers of qubits, Conductor Quantum plans to push its control software to match that growth, ensuring automation keeps pace with physical progress.

Who Else Is Building Conductor Quantum?

Beyond its founders, Conductor Quantum’s team includes Raymond, a founding engineer with an unusually diverse background. Formerly a guidance, navigation, and control software engineer at SpaceX, his code flew on more than 100 Falcon 9 missions. He has also worked on robotics at NASA and quantum machine learning at Cornell.

With degrees in Physics and Computer Science from Harvard, Raymond brings a systems-engineering mindset that complements the company’s focus on reliability, scalability, and automation.

What Could Conductor Quantum Enable in the Long Term?

If successful, Conductor Quantum could play a pivotal role in unlocking the next era of computation. By automating quantum control and abstracting complexity, the company is laying groundwork for quantum systems that scale like classical ones once did.

Such systems could dramatically accelerate drug discovery, enable the design of entirely new materials, and help scientists model physical phenomena that remain inaccessible to classical supercomputers.

In this sense, Conductor Quantum is not just building software—it is attempting to redefine how humanity interacts with quantum machines.

Why Is Conductor Quantum a Startup to Watch?

Conductor Quantum stands out because it addresses quantum computing’s hardest problem not with more hardware alone, but with intelligence, abstraction, and automation. Its focus on silicon, AI-driven control, and natural language interfaces reflects a holistic understanding of what it will take to move quantum computing from experimental labs to widespread use.

At a time when quantum hype often outpaces practical progress, Conductor Quantum is betting on fundamentals: scalability, usability, and systems engineering. If those bets pay off, the company could become a critical conductor in the quantum computing ecosystem—coordinating hardware, software, and human intent into a single, scalable whole.