Silicon Quantum Computing Launches Innovative Quantum Twins Technology

On the heels of their recent $35M Series A financing, Silicon Quantum Computing (SQC) has announced their recent development – the Quantum Twins product. This new silicon quantum simulator is available directly to customers under contract. From quantum twins to climate models, this is a new technology that lets designers create quantum twins for many…

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Silicon Quantum Computing Launches Innovative Quantum Twins Technology

On the heels of their recent $35M Series A financing, Silicon Quantum Computing (SQC) has announced their recent development – the Quantum Twins product. This new silicon quantum simulator is available directly to customers under contract. From quantum twins to climate models, this is a new technology that lets designers create quantum twins for many different two-dimensional problems. This is a big breakthrough in the rapidly advancing area of quantum computing.

The Quantum Twins technology is a key enabler of a fundamental new approach to solving complex material challenges. SQC team improvises clusters of ten to fifty phosphorus atoms to accomplish this. They build application-spec chips that can replicate all kinds of material states. This feature really comes to the fore when modeling the metal-insulator transition. This phenomenon presents a deep challenge for classical computers to imitate.

A Leap Forward in Quantum Simulation

SQC’s team ran an impressively successful demonstration with the new minutes on showcasing the quantum twin technology. They took on a very ambitious task – the metal-insulator transition in a 2D material. This new model is critical for expanding our understanding to intersecting social, economic, and environmental determinants of health. It allows us to study dynamics that would be infeasible for classical computers to simulate.

>Sam Gorman, SQC systems engineer lead, stressed how different this process was compared to traditional development with quantum Twins. He stated,

“Instead of using qubits, as you would typically in a quantum computer, we just directly encode the problem into the geometry and structure of the array itself.”

Such an innovative approach paves the way for further research and application, especially in areas where large-scale simulation and modeling are most critical. The team has created an incredibly complex 38-stage process to pattern phosphorus atoms into a silicon substrate. This new approach ensures expressive and precise accuracy in their stunning simulations.

Building on Past Successes

Our Quantum Twins technology rests on the solid foundation established by Michelle Simmons. With her role as the founder of SQC, she has led academic research efforts in quantum computing for more than 25 years. Last year, Simmons’ team had an extraordinary accomplishment. They used a previous incarnation of their technology to run a simulation for a polyacetylene molecule that needed a whopping ten registers. The current model, though, uses a staggering 15,000 quantum dots to boost its performance.

This clean environment is essential for the accurate functioning of quantum devices and reflects SQC’s commitment to high standards in quantum research.

“It’s done in ultra-high vacuum. So it’s a very pure, very clean system.”

Simmons sees big industrial use cases for Quantum Twins, especially in drug discovery. She explained,

Future Applications and Challenges

Gorman seconded her sentiments about their tech’s influence on big, high-stakes issues. He remarked,

“If you look at different drugs, they’re actually very similar to polyacetylene. They’re carbon chains, and they have functional groups. So, understanding how to map it [onto our simulator] is a unique challenge. But that’s definitely an area we’re going to focus on.”

They tell us that their technology, especially when paired with quantum computing, is uniquely positioned to tackle the complex regimes that have long stumped classical computing solutions. Gorman added,

“Now that we’ve demonstrated that the device is behaving as we predict, we’re looking at high-impact issues or outstanding problems.”

The team believes that their technology can navigate complex regimes that are traditionally difficult for classical computing solutions. Gorman added,

“That is the part which is challenging for classical computing. But we can actually put our system into this regime quite easily.”