Insider Brief
- Amazon Web Services (AWS) introduced Ocelot, its first quantum computing chip, designed to advance error correction and improve scalability for practical quantum computing.
- Ocelot utilizes “cat qubits” to intrinsically suppress certain errors, reducing the number of physical qubits needed for error-corrected logical qubits by up to 90%.
- AWS researchers tested Ocelot’s ability to store quantum information, concluding that its architecture is a viable path forward for building larger, more reliable quantum systems.
Amazon Web Services (AWS) introduced Ocelot, its first quantum computing chip, which the company labels a significant step toward building practical quantum computers capable of solving complex problems beyond the reach of conventional machines.
The chip, developed at the AWS Center for Quantum Computing at Caltech, is designed to address one of the biggest hurdles in quantum computing: error correction. AWS claims that Ocelot can reduce the cost of implementing error correction by up to 90%, a key metric for making quantum computers more scalable and useful for real-world applications.
Addressing Noise
Unlike classical computers, which use bits that represent either a 0 or a 1, quantum computers rely on quantum bits, or qubits, which can exist in multiple probabilistic states due to a phenomenon called superposition. This property allows quantum computers to process information in ways that classical systems cannot, with potential applications in materials science, cryptography and optimization problems.
However, quantum computing faces a major challenge: qubits are highly sensitive to environmental noise. Small disturbances — such as electromagnetic waves, temperature changes, or even cosmic radiation — can cause errors in calculations. This fragility has made it difficult to scale quantum systems beyond a few hundred qubits while maintaining computational accuracy.
AWS’s Solution: Cat Qubits and Hardware-Efficient Error Correction
AWS has designed Ocelot specifically for error correction, using an approach that integrates error-resistant qubits directly into the hardware. The chip leverages “cat qubits,” a type of qubit that intrinsically suppresses certain errors, named after Schrödinger’s famous thought experiment involving a cat that is simultaneously alive and dead.
According to AWS, this method significantly reduces the number of physical qubits required to form error-corrected logical qubits, cutting the resource cost by a factor of five to ten compared to conventional approaches.
“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. Ocelot is an important step on that journey,” said Oskar Painter, AWS director of Quantum Hardware, in a statement. “In the future, quantum chips built according to the Ocelot architecture could cost as little as one-fifth of current approaches, due to the drastically reduced number of resources required for error correction. Concretely, we believe this will accelerate our timeline to a practical quantum computer by up to five years.”
How Ocelot Works
Ocelot is a small-scale prototype chip designed to test AWS’s approach. It consists of two integrated silicon microchips, each about one square centimeter in size, bonded together in a stack. The chips contain superconducting circuits made from a thin film of tantalum, a material AWS scientists processed to improve its performance.
The architecture includes 14 core components:
- Five data qubits (cat qubits): Store quantum states for computation.
- Five buffer circuits: Help stabilize the data qubits.
- Four additional qubits: Detect and correct errors.
AWS researchers tested Ocelot’s ability to store quantum information using repeated rounds of error correction.
The AWS researchers didn’t take an existing architecture and then try to incorporate error correction afterwards. They selected the qubit and architecture with quantum error correction as the main requirement.
“We looked at how others were approaching quantum error correction and decided to take a different path,” said Painter. “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. We believe that if we’re going to make practical quantum computers, quantum error correction needs to come first.”
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.”
How It Compares to Other Quantum Efforts
Many quantum computing efforts have focused on increasing qubit counts, but without robust error correction, adding more qubits does not necessarily translate into more computing power.
AWS’s approach prioritizes reducing error rates first. The team reports that quantum computing faces significant challenges due to the fragility of qubits, which can be disrupted by environmental factors such as vibrations, heat, electromagnetic interference, and cosmic radiation. These disturbances introduce errors in computations, making it difficult to achieve reliable, large-scale quantum processing.
“The biggest challenge isn’t just building more qubits,” said Painter. “It’s making them work reliably.”
What’s Next?
Despite its promise, Ocelot is still an early-stage prototype. AWS plans to continue refining its approach, aiming to scale up the technology in the coming years.
“We’re just getting started and we believe we have several more stages of scaling to go through,” said Painter. “It’s a very 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. It’s a flywheel of continuous improvement and scaling.”
AWS has published its findings in the journal Nature and provided further details on the Amazon Science website.
