Fernando Brandão and Oskar Painter of Amazon Science report that AWS has introduced Ocelot, a quantum computing chip that cuts error correction costs by up to 90%. Using “cat qubits” reduces errors and resource needs, potentially accelerating practical quantum computing by five years. They write:
Today we are happy to announce Ocelot, our first-generation quantum chip. Ocelot represents Amazon Web Services’ pioneering effort to develop, from the ground up, a hardware implementation of quantum error correction that is both resource efficient and scalable. Based on superconducting quantum circuits, Ocelot achieves the following major technical advances:
The first realization of a scalable architecture for bosonic error correction, surpassing traditional qubit approaches to reducing error correction overhead;
The first implementation of a noise-biased gate — a key to unlocking the type of hardware-efficient error correction necessary for building scalable, commercially viable quantum computers;
State-of-the-art performance for superconducting qubits, with bit-flip times approaching one second in tandem with phase-flip times of 20 microseconds. […]
From the billions of transistors in a modern GPU to the massive-scale GPU clusters powering AI models, the ability to scale efficiently is a key driver of technological progress. Similarly, scaling the number of qubits to accommodate the overhead required of quantum error correction will be key to realizing commercially valuable quantum computers.
But the history of computing shows that scaling the right component can have massive consequences for cost, performance, and even feasibility. The computer revolution truly took off when the transistor replaced the vacuum tube as the fundamental building block to scale.
Ocelot represents our first chip with the cat qubit architecture, and an initial test of its suitability as a fundamental building block for implementing quantum error correction. Future versions of Ocelot are being developed that will exponentially drive down logical error rates, enabled by both an improvement in component performance and an increase in code distance.
Codes tailored to biased noise, such as the repetition code used in Ocelot, can significantly reduce the number of physical qubits required. In our forthcoming paper “Hybrid cat-transmon architecture for scalable, hardware-efficient quantum error correction”, we find that scaling Ocelot could reduce quantum error correction overhead by up to 90% compared to conventional surface code approaches with similar physical-qubit error rates.
We believe that Ocelot’s architecture, with its hardware-efficient approach to error correction, positions us well to tackle the next phase of quantum computing: learning how to scale. Using a hardware-efficient approach will allow us to more quickly and cost effectively achieve an error-corrected quantum computer that benefits society.
Over the last few years, quantum computing has entered an exciting new era in which quantum error correction has moved from the blackboard to the test bench. With Ocelot, we are just beginning down a path to fault-tolerant quantum computation. For those interested in joining us on this journey, we are hiring for positions across our quantum computing stack.
Read more here.
