Amazon Web Services (AWS) has unveiled Ocelot, its first-generation quantum computing chip, marking a significant step in the journey toward practical quantum computing. While the chip possesses only rudimentary computational capabilities, the company presents it as a proof-of-principle demonstration. This prototype, designed to pave the way for larger, more powerful machines, is slated to unlock the potential of quantum computing, including rapid and precise simulations of advanced battery materials.
“This is a first prototype that demonstrates that this architecture is scalable and hardware-efficient,” explained Oskar Painter, head of quantum hardware at AWS. According to Amazon, its approach simplifies error correction, a pivotal technical hurdle in developing quantum computing.
Ocelot features nine quantum bits, known as qubits, arranged on a chip approximately one centimeter square. The chip must be cryogenically cooled to near absolute zero to operate effectively, a requirement shared by certain quantum computing hardware. Five of these qubits employ a design the field terms a “cat qubit,” named for Schrödinger’s cat, the iconic 20th-century thought experiment concerning the concept of superposition. This superposition of states is a fundamental principle in quantum computing. AWS’s cat qubits are constructed from minuscule hollow structures of tantalum containing microwave radiation, which are connected to a silicon chip structure. The remaining four qubits are transmons, which are each electrical circuits made of superconducting material.
In this architecture, AWS employs cat qubits for information storage, while transmon qubits monitor the captured data. This approach contrasts with the designs used by Google and IBM, whose quantum computers utilize transmons for all computational elements.
A notable achievement is the implementation of a more efficient form of quantum error correction using Ocelot. Like conventional computers, quantum computers are susceptible to errors that can accumulate and undermine computing power. This is why effective error correction is so critical. “The only way you’re going to get a useful quantum computer is to implement quantum error correction,” Painter said.
The algorithms required for quantum error correction often impose considerable hardware demands. Last year, Google encoded a single error-corrected bit of quantum information using 105 qubits. AWS’s design strategy requires a tenth as many qubits per bit of information, Painter noted.
In a paper published in Nature, the team encoded a single error-corrected bit of information using Ocelot’s nine qubits. Painter asserted that this hardware design should theoretically be easier to scale up to a larger machine compared to a solely transmon-based design.
Shruti Puri, a physicist at Yale University, who was not involved in the research, praised the design combining cat qubits and transmons. “Basically, you can decompose all quantum errors into two kinds—bit flips and phase flips,” Puri explained. Quantum computers represent information as 1s, 0s, and probabilities, or superpositions, of both. A bit flip, which also occurs in conventional computing, occurs when the computer mistakenly encodes a 1 that should be a 0, or vice versa. In the case of quantum computing, the bit flip occurs differently: when the computer encodes the probability of a 0 as the probability of a 1, or vice versa. A phase flip is a type of error unique to quantum computing, linked to the wavelike properties of the qubit.
The cat-transmon design enabled Amazon to engineer the quantum computer to predominantly exhibit phase-flip errors. Due to this, the company could use a much simpler error correction algorithm than Google’s—one that did not require as many qubits. “Your savings in hardware is coming from the fact that you need to mostly correct for one type of error,” said Puri. “The other error is happening very rarely.”
The hardware savings are also a benefit of AWS’s careful implementation of a C-NOT gate, which is used in error correction. Amazon’s researchers showed that the C-NOT operation did not disproportionately introduce bit-flip errors. This discovery meant that each round of error correction would still predominantly result in phase-flip errors, allowing the use of a simpler, hardware-efficient error correction code.
Painter revealed that AWS began working on Ocelot’s designs as early as 2021, describing it as a “full-stack problem.” Fabricating high-performing qubits capable of error correction required developing a novel method for growing tantalum on a silicon chip. This crucial process allowed the researchers to minimize atomic-scale defects. Puri noted that AWS’s capacity to fabricate and control multiple cat qubits within a single device is a considerable advancement. “Any work that goes toward scaling up new kinds of qubits, I think, is interesting,” she said.
Despite this progress, years of further development are anticipated. Other experts estimate that quantum computers will need thousands, if not millions, of qubits to perform a useful task. Puri noted that Amazon’s work “is a first step,” adding that the researchers will need to further mitigate bit-flip errors as they scale up the number of qubits. Painter believes that this architecture will guide the company’s efforts going forward.
“We really became convinced that this needed to be our mainline engineering effort,” he said. “We’ll still do some exploratory things, but this is the direction we’re going.” (The startup Alice & Bob, based in France, is also building a quantum computer composed of cat qubits.)
As it stands, Ocelot is essentially a demonstration of quantum memory, Painter stated. The next objectives include integrating more qubits into the chip, encoding greater amounts of information, and performing actual computations. However, numerous challenges remain, including how to connect all the wires and link multiple chips together. “Scaling is not trivial,” he said.
