Tech News

Major improvements in quantum fidelity

Firing up quantum fidelity
Researchers used the Oak Ridge Leadership Computing Facility’s Quantum Computing User Program to achieve major improvements in quantum fidelity via this error mitigation protocol. The protocol requires only a few additional gates and just one additional qubit. Credit: Ruslan Shaydulin/Argonne National Laboratory

Researchers used Oak Ridge National Laboratory’s Quantum Computing User Program (QCUP) to achieve major improvements in quantum fidelity, a potential step toward more accurate, reliable quantum networks and supercomputers.

Quantum computing relies on transfer and storage of information via quantum bits, known as qubits, rather than the traditional single-value bits used by classical computers. Qubits, unlike classical bits,

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Researchers demonstrate multimodal transistor in artificial neural networks

Researchers demonstrate multimodal transistor in artificial neural networks
Figure 1 The multimodal transistor (MMT). (a) Illustrative cross-section and (b) optical micrograph of a microcrystalline silicon (µ-Si) multimodal transistor (MMT). Charge dynamics in the source-gate overlap (SGO) and source-drain separation (d) regions are controlled by the current control gate (Gate 1), and channel gate (Gate 2), respectively19. (c) Simulated amorphous silicon (a-Si) MMT transfer characteristics showing Gate 1 (G1) sets drain current magnitude, while Gate 2 (G2) allows or blocks its flow without influencing its magnitude. (d) Simulated transfer characteristics for G2 further demonstrating that G2 does not influence charge injection processes and thus flatten once the channel is
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A new framework that could simplify imitation learning in robotics

A new framework that could simplify imitation learning in robotics
Figure showing the two ‘halves’ of the researchers’ method, with representation learning on the left and behavior imitation through nearest neighbors on the right. Credit: Pari et al.

Over the past few decades, computer scientists have been trying to train robots to tackle a variety of tasks, including house chores and manufacturing processes. One of the most renowned strategies used to train robots on manual tasks is imitation learning.

As suggested by its name, imitation learning entails teaching a robot how to do something using human demonstrations. While in some studies this training strategy achieved very promising results, it often

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The first AI breast cancer sleuth that shows its work

The first AI breast cancer sleuth that shows its work
Most AI for spotting pre-cancerous lesions in mammography scans don’t reveal any of their decision-making process (top). If they do, it’s often a saliency map (middle) that only tells doctors where they’re looking. A new AI platform (bottom) not only tells doctors where it’s looking, but which past experiences its using to draw its conclusions. Credit: Alina Barnett, Duke University

Computer engineers and radiologists at Duke University have developed an artificial intelligence platform to analyze potentially cancerous lesions in mammography scans to determine if a patient should receive an invasive biopsy. But unlike its many predecessors, this algorithm is interpretable,

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Computer model seeks to explain the spread of misinformation and suggest countermeasures

Computer model seeks to explain the spread of misinformation and suggest countermeasures
A graphical illustration of one time step of the POD model. In the left panel, (A) depicts the initial setup of a small network with institutional agent i1 with subscribers s1, s2, s3. All agents in the network are labeled with their belief strength. The right panel, (B) depicts one time step t = 0 of agent i1 sending messages M1(t = 0) = (m0, m1). (i) shows the initial sending of m0 = 4 to subscribers, and (ii) shows s1 and s3
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iDIRECT network framework could help scientists better understand biological systems

network
Credit: Pixabay/CC0 Public Domain

Despite the fundamental role networks play in how scientists understand the dynamics and properties of complex systems, reconstructing networks from large-scale experimental data is a challenge.

In systems biology and microbial ecology—the study of microbes in the environment and their interactions with each other—the challenges of reconstructing these networks can be compounded by difficulty unraveling direct and indirect interactions, or the ability of one element in a system to impact another, either with or without direct interaction.

Jizhong Zhou, director of the Institute for Environmental Genomics at the University of Oklahoma, is leading a research team

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