Qubits tagged posts

Rethinking the Quantum Chip

Researchers in Cleland Lab at the University of Chicago Pritzker School of Molecular Engineering, including (from left) alumnus Haoxiong Yan, PhD candidate Xuntao Wu, and Prof. Andrew Cleland, have realized a new design for a superconducting quantum processor. (Photo by John Zich)

New research demonstrates a brand-new architecture for scaling up superconducting quantum devices. Researchers at the UChicago Pritzker School of Molecular Engineering (UChicago PME) have realized a new design for a superconducting quantum processor, aiming at a potential architecture for the large-scale, durable devices the quantum revolution demands.

Unlike the typical quantum chip design that lays the information-processing qubits onto a 2-D grid, the team from the Cleland Lab has designed a modular qua...

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Quantinuum Quantum Computer using Microsoft’s ‘Logical Quantum Bits’ runs 14,000 Experiments with No Errors

Quantinuum quantum computer using Microsoft's 'logical quantum bits' runs 14,000 experiments with no errors
High-level depiction of the logical program of the Bell resource-state preparation using the Steane code. Credit: arXiv (2024). DOI: 10.48550/arxiv.2404.02280

A team of computer engineers from quantum computer maker Quantinuum, working with computer scientists from Microsoft, has found a way to greatly reduce errors when running experiments on a quantum computer. The combined group has published a paper describing their work and results on the arXiv preprint server.

Computer scientists have been working for several years to build a truly useful quantum computer that could achieve quantum supremacy. Research has come a long way, most of which has involved adding more qubits.

But such research has been held up by one main problem—quantum computers make a lot of errors...

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New Study uses Machine Learning to Bridge the Reality Gap in Quantum Devices

New study uses machine learning to bridge the reality gap in quantum devices
(a) Device geometry including the gate electrodes (labeled G1–G8), donor ion plane, and an example disorder potential experienced by confined electrons. Typical flow of current from source to drain is indicated by the white arrow. (b) Schematic of the disorder inference process. Colors indicate the following: red for experimentally controllable variables, green for quantities relevant to the electrostatic model, blue for experimental device, and yellow for machine learning methods. Dashed arrows represent the process of generating training data for the deep learning approximation and are not part of the disorder inference process. Credit: Physical Review X (2024). DOI: 10.1103/PhysRevX.14.011001

A study led by the University of Oxford has used the power of machine learning to ove...

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Machine Learning Contributes to Better Quantum Error Correction

ai-generated image
An AI-generated image illustrating the work

Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers—a crucial step for making these devices practical—using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections.

In contrast to classical computers, which operate on bits that can only take the basic values 0 and 1, quantum computers operate on “qubits”, which can assume any superposition of the computational basis states...

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