TLDR
- AWS unveiled Ocelot, its first quantum computing chip designed to advance error correction, potentially reducing the cost by up to 90%
- Ocelot uses “cat qubits” that intrinsically suppress errors, requiring fewer physical qubits for error-corrected logical qubits
- Amazon joins Microsoft and Google in the quantum chip race, with Microsoft recently revealing Majorana 1 and Google debuting Willow in December
- The chip integrates error-resistant qubits directly into the hardware, with AWS claiming this could accelerate their timeline to a practical quantum computer by up to five years
- Industry leaders disagree on the timeline for practical quantum computing, with estimates ranging from 5 to 20 years
Amazon Web Services (AWS) has introduced its first-ever quantum computing chip named Ocelot. The new chip was developed at the AWS Center for Quantum Computing at Caltech.
AWS designed Ocelot to tackle one of quantum computing’s biggest challenges: error correction. The company states that Ocelot can reduce the cost of implementing error correction by up to 90%.
Quantum computers use qubits instead of traditional bits. Unlike classical computers that use bits representing either 0 or 1, qubits can exist in multiple states at once through a phenomenon called superposition.
This property allows quantum computers to process information in ways that classical systems cannot. Quantum computing holds promise for applications in materials science, cryptography, and solving complex optimization problems.
A major obstacle in quantum computing is that qubits are extremely sensitive to environmental noise. Disturbances like electromagnetic waves, temperature changes, or cosmic radiation can cause calculation errors.
This sensitivity has made scaling quantum systems difficult while maintaining accuracy. Current systems struggle to maintain computational accuracy beyond a few hundred qubits.
AWS’s approach with Ocelot integrates error-resistant qubits directly into the hardware. The chip uses “cat qubits,” named after Schrödinger’s famous thought experiment.
These cat qubits intrinsically suppress certain errors. According to AWS, this method greatly reduces the number of physical qubits needed to form error-corrected logical qubits.
“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,” said Oskar Painter, AWS director of Quantum Hardware.
Painter added that future quantum chips built on the Ocelot architecture could cost as little as one-fifth of current approaches. This is due to the reduced resources required for error correction.
Ocelot is a small-scale prototype consisting of two integrated silicon microchips. Each chip measures about one square centimeter and contains superconducting circuits made from tantalum.
The architecture includes 14 core components: five data qubits, five buffer circuits, and four additional qubits to detect and correct errors. AWS researchers have tested Ocelot’s ability to store quantum information.
“We looked at how others were approaching quantum error correction and decided to take a different path,” Painter explained. “We didn’t take an existing architecture and then try to incorporate error correction afterwards.”
Amazon joins other tech giants in the quantum computing race
Amazon joins other tech giants in the quantum computing race. Microsoft recently revealed its Majorana 1 quantum chip, while Google debuted its Willow chip in December.
Gaining a lead in quantum computing would provide a massive opportunity for cloud providers. It could ensure first-mover advantage in a field that might impact many industries and drive revenue growth.
Industry leaders disagree on the timeline for practical quantum computing. Nvidia CEO Jensen Huang suggests practical quantum computers are up to 20 years away, while Google CEO Sundar Pichai estimates five to ten years.
Despite its promise, Ocelot remains an early-stage prototype. AWS plans to continue refining its approach and scaling up the technology in coming years.
“We’re just getting started and we believe we have several more stages of scaling to go through,” Painter said. “It’s a very tough problem to tackle, and we will need to continue investing in basic research.”
AWS has published its findings in the journal Nature and provided additional details on the Amazon Science website.
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