Rigetti Eyes Scaling with 128-Qubit Architecture

By George Leopold

August 10, 2018

Rigetti Computing plans to build a 128-qubit quantum computer based on an equivalent quantum processor that leverages emerging hybrid computing algorithms used to test programs and potential applications.

Founded in 2013 and based in Berkeley, Calif., Rigetti bills itself as “a full-stack quantum computing company.” It announced plans this week to deploy the 128-qubit machine over the next 12 months. It also plans additional investments “at the application layer to encourage more experimentation on our quantum computers,” the company said in a blog post.

The scaling of quantum chips up to 128 qubits has allowed researchers to test quantum programs on Rigetti’s hybrid computers that combine quantum and digital computing capabilities. “We must be able to scale and improve the performance of the chips and connect them to the electronics on which they run — which has proven to be one of the most challenging aspects of quantum computing,” noted company founder Chad Rigetti.

New 128-qubit chip is based on a scalable 16-qubit form factor (Source: Rigetti Computing)

The 128-qubit chip was designed with scaling in mind. Rigetti said the new chip is based on a “scalable 16-qubit” architecture. The company CEO also noted that designers have begun integrating an architecture known as 3-D signaling that improves the scaling of quantum chips by isolating individual quantum integrated circuits, thereby boosting performance.

The quantum computer maker recently upgraded its open source Forest platform that provides developers with access to its cloud-based quantum simulator. The upgrade included new tools for debugging quantum programs.

Meanwhile, the emerging application layer is focused on the main areas: machine learning, system optimization and quantum simulation, Rigetti said Wednesday (Aug. 8).

Simulation is emerging as a key focus for quantum computing developers. Last month, researchers at D-Wave Systems  published a paper on the simulation of a quantum magnetism problem that has potential applications in materials science research.

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