AWS Unveils Ocelot Chip to Accelerate Quantum Timeline

Amazon Web Services has unveiled a new quantum computing chip named Ocelot that reduces the costs of implementing quantum error correction by up to 90% compared with current approaches.

The chip, developed by the AWS Center for Quantum Computing at the California Institute of Technology, uses an architecture built on ‘cat qubits’ – named after the Schrödinger’s cat thought experiment – which intrinsically suppresses certain forms of errors.

“With the recent advancements in quantum research, it is no longer a matter of if, but when practical, fault-tolerant quantum computers will be available for real-world applications,” says Oskar Painter, AWS Director of Quantum Hardware. “Ocelot is an important step on that journey.”

According to AWS, quantum chips built with the Ocelot architecture could cost as little as one-fifth of current approaches due to the reduced resources required for error correction. The company believes this will accelerate its timeline to a practical quantum computer by up to five years.

AWS quantum Ocelot architecture builds error correction from ground up

Quantum computers use quantum bits, or qubits – typically elementary particles such as electrons or photons – as their most basic unit of information, in contrast to the digital bits (1s and 0s) used in classical computing. Through manipulating the “quantum state” of the qubit, where it can be both 1 and 0 simultaneously, quantum computers aim to solve specific problems exponentially faster than classical computers.

One of the central challenges in quantum computing is sensitivity to environmental factors. Vibrations, heat, electromagnetic interference, cosmic rays and radiation can knock qubits out of their quantum state, causing errors in computation.

While quantum error correction using ‘logical’ qubits across multiple physical qubits can shield quantum information, the number of qubits required for accurate results has made current approaches prohibitively expensive.

“The biggest challenge isn't just building more qubits,” says Oskar. “It’s making them work reliably.”

The AWS team designed Ocelot with error correction as the foundational requirement. “We looked at how others were approaching quantum error correction and decided to take a different path,” Oskar says. “We didn’t take an existing architecture and then try to incorporate error correction afterwards. We selected our qubit and architecture with quantum error correction as the top requirement.”

AWS 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.”

AWS quantum computing pathway follows Graviton development model

The Ocelot chip consists of two integrated silicon microchips, each with an area of roughly 1cm². The chips are bonded one on top of the other in an electrically-connected stack with thin layers of superconducting materials forming the quantum circuit elements.

The architecture contains 14 core components: five data qubits (the cat qubits), five buffer circuits for stabilising the data qubits and four additional qubits for detecting errors on the data qubits.

The cat qubits store quantum states using components called oscillators, which generate repetitive electrical signals with steady timing. These oscillators are made from a thin film of superconducting Tantalum, processed in a specific way to boost performance.

Despite the breakthrough, AWS acknowledges that Ocelot remains a prototype. The company plans to continue investing in quantum research and refining its approach, similar to its development pathway for the Graviton processor.

“We’re just getting started and we believe we have several more stages of scaling to go through,” says Oskar. “It’s a tough problem to tackle, and we will need to continue investing in basic research, while staying connected to, and learning from, important work being done in academia. 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.”

“We believe that if we’re going to make practical quantum computers, quantum error correction needs to come first,” says Oskar.

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