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? Amazon Web Services has unveiled Ocelot, its first quantum computing chip, which aims to tackle one of the biggest barriers in the field: error correction. AWS claims the chip could slash the cost of quantum error correction by up to 90%, a breakthrough that could accelerate the race to build practical, fault-tolerant quantum computers.

[hotlink]Amazon[/hotlink] Web Services has announced its first quantum computing chip, Ocelot, as Big Tech companies accelerate breakthroughs in the field.

The chip is designed to improve quantum error correction, a fundamental challenge in the field. AWS says that Ocelot could reduce the cost of error correction by up to 90%, addressing one of the key obstacles to building practical quantum computers capable of solving problems beyond the reach of currently available systems.

The chip is a small-scale prototype, which AWS said is designed to test the effectiveness of the company’s quantum error correction architecture. It consists of two integrated silicon microchips, each measuring approximately one square centimeter.

The company says it’s an important step forward in the race to build fault-tolerant quantum computers.

Quantum computing has the potential to transform multiple industries ranging from pharmaceuticals to weather forecasting by solving complex problems that classical computers cannot efficiently handle.

Big Tech companies, including Google, Microsoft, and IBM, have all announced quantum computing breakthroughs in the last few months.

Google has announced its Willow chip, while IBM announced it had successfully developed a quantum processor chip that consists of more than 1,000 qubits. Microsoft has also announced its Majorana 1 chip, which the company said would accelerate the timeline for quantum computers capable of solving meaningful, industrial-scale problems from decades to just years.

Error correction

Quantum computers are especially sensitive to small changes in their environment—such as vibrations, heat, or even electromagnetic interference from Wi-Fi networks and cell phones—and this “noise” can interfere with qubits and cause computational errors. To solve this problem and ensure that quantum computers perform error-free calculations, scientists rely on a process called quantum error correction, which has been hugely expensive—at least until now.

AWS said Ocelot was designed with error correction as a foundational principle rather than a later addition. The chip utilizes a specialized type of qubit known as a “cat qubit,” which gets its name from the Schr?dinger’s cat thought experiment.

“We believe that if we’re going to make practical quantum computers, quantum error correction needs to come first,” said Oskar Painter, AWS’s head of quantum hardware. “That’s what we’ve done with Ocelot. We didn’t take an existing architecture and then try to incorporate error correction afterward. We selected our qubit and architecture with quantum error correction as the top requirement.”

Painter said his team estimates that scaling Ocelot to a “fully-fledged quantum computer capable of transformative societal impact would require as little as one-tenth of the resources associated with standard quantum error correcting approaches.”

Cat qubits are engineered to intrinsically suppress certain types of errors, reducing the complexity and resource demands of quantum error correction. AWS estimates that Ocelot’s architecture can reduce the resources required for error correction by a factor of five to 10 compared with conventional approaches.

“Quantum error correction relies on continued improvements in the physical qubits. We can’t just rely on the conventional approaches to how we fabricate chips,” Fernando Brandao, AWS director of applied science, said. “We have to incorporate new materials, with fewer defects, and develop more robust fabrication processes.”

Future implications

Ocelot is still a research laboratory prototype, but AWS intends to refine and scale the system.

“We believe we have several more stages of scaling to go through. It’s a very hard problem to tackle, and we will need to continue to invest in basic research, while staying connected to, and learning from, important work being done in academia,” Painter said.

“Right now, our task is to keep innovating across the quantum computing stack, to keep examining whether we’re using the right architecture, and to incorporate these learnings into our engineering efforts. It’s a flywheel of continuous improvement and scaling.”