Building Accessible Quantum Systems with Silicon

The industry sees varied approaches. Equal One focuses on scalable, accessible applications with silicon-based systems. “Our team is different from typical quantum teams,” Lynch notes. “We have the academic prowess and some of the best scientists in the world, but we also have some of the best technology commercialization experts.”

Leveraging Silicon for Quantum Advancement

Equal One builds on existing semiconductor technology rather than starting from scratch. “All of computing has advanced with silicon and the semiconductor industry. Our belief is that scalable quantum will follow the same path,” says Lynch, outlining the company’s “Quantum System-on-Chip” strategy.

Quantum Computing in Drug Discovery: Redefining Possibilities

“We can’t even simulate the simplest of molecules,” Lynch explains, highlighting how traditional computers fall short in drug discovery. Today’s computers struggle to predict how drugs interact with the human body, making human trials essential. Scalable quantum computing could transform this process.

Richard Feynman’s Insight on Quantum Modeling

Richard Feynman once stated that “you’re going to need a quantum computer to model quantum mechanics, to model nature, because it’s quantum mechanical.” This insight forms the foundation of Equal One’s approach to drug discovery, focusing on complex molecule simulations.

Targeting Chemistry, Optimization, and AI Integration

Equal One’s quantum system targets three main applications:

1. Chemistry
2. Optimization
3. AI integration

Reaching approximately 1,000 qubits is a benchmark for simulating complex molecules—a goal Equal One aims to achieve within two years.

Quantum-AI Integration: Efficiency in AI Development

Lynch highlights a major inefficiency in AI: “If you look at the power it takes to train these models and compare it to our brain, it’s megawatts versus about 20 watts.” To address this, Equal One integrates neural networks directly on quantum chips for real-time error detection and correction. This merger of classical and quantum computing paves the way for greater AI efficiency.