Quantum computers have long been touted as revolutionary machines capable of solving complex problems that stymie conventional supercomputers. However, their full potential has been hindered by the limitations of qubit amplifiers – essential components required to read and interpret quantum information. Researchers at Chalmers University of Technology in Sweden have taken a significant step forward with the development of an ultra-efficient amplifier that reduces power consumption by 90%, paving the way for more powerful quantum computers with enhanced performance.

The new amplifier is pulse-operated, meaning it’s activated only when needed to amplify qubit signals, minimizing heat generation and decoherence. This innovation has far-reaching implications for scaling up quantum computers, as larger systems require more amplifiers, leading to increased power consumption and decreased accuracy. The Chalmers team’s breakthrough offers a solution to this challenge, enabling the development of more accurate readout systems for future generations of quantum computers.

One of the key challenges in developing pulse-operated amplifiers is ensuring they respond quickly enough to keep pace with qubit readout. To address this, the researchers employed genetic programming to develop a smart control system that enables rapid response times – just 35 nanoseconds. This achievement has significant implications for the future of quantum computing, as it paves the way for more accurate and powerful calculations.

The new amplifier was developed in collaboration with industry partners Low Noise Factory AB and utilizes the expertise of researchers at Chalmers’ Terahertz and Millimeter Wave Technology Laboratory. The study, published in IEEE Transactions on Microwave Theory and Techniques, demonstrates a novel approach to developing ultra-efficient amplifiers for qubit readout and offers promising prospects for future research.

In conclusion, the development of this highly efficient amplifier represents a significant leap forward for quantum computing. By reducing power consumption by 90%, researchers have opened doors to more powerful and accurate calculations, unlocking new possibilities in fields such as drug development, encryption, AI, and logistics. As the field continues to evolve, it will be exciting to see how this innovation shapes the future of quantum computing.

The Massachusetts General Brigham researchers have developed an innovative artificial intelligence (AI) tool called AI-CAC to analyze previously collected CT scans and identify individuals with high coronary artery calcium (CAC) levels, indicating a greater risk for cardiovascular events. Their research, published in NEJM AI, demonstrated the high accuracy and predictive value of AI-CAC for future heart attacks and 10-year mortality.

Millions of chest CT scans are taken each year, often in healthy people, to screen for lung cancer or other conditions. However, this study reveals that these scans can also provide valuable information about cardiovascular risk, which has been going unnoticed. The researchers found that AI-CAC had a high accuracy rate (89.4%) at determining whether a scan contained CAC or not.

The gold standard for quantifying CAC uses “gated” CT scans, synchronized to the heartbeat to reduce motion during the scan. However, most chest CT scans obtained for routine clinical purposes are “nongated.” The researchers developed AI-CAC, a deep learning algorithm, to probe through these nongated scans and quantify CAC.

The AI-CAC model was 87.3% accurate at determining whether the score was higher or lower than 100, indicating a moderate cardiovascular risk. Importantly, AI-CAC was also predictive of 10-year all-cause mortality, with those having a CAC score over 400 having a 3.49 times higher risk of death over a 10-year period.

The researchers hope to conduct future studies in the general population and test whether the tool can assess the impact of lipid-lowering medications on CAC scores. This could lead to the implementation of AI-CAC in clinical practice, enabling physicians to engage with patients earlier, before their heart disease advances to a cardiac event.

As Dr. Raffi Hagopian, first author and cardiologist at the VA Long Beach Healthcare System, emphasized, “Using AI for tasks like CAC detection can help shift medicine from a reactive approach to the proactive prevention of disease, reducing long-term morbidity, mortality, and healthcare costs.”

The relationship between artificial intelligence (AI) and worker well-being has been a topic of concern. However, a recent study suggests that AI exposure may not be causing widespread harm to mental health or job satisfaction. In fact, the data indicates that AI might even be linked to modest improvements in physical health, particularly among employees with less than a college degree.

The study, “Artificial Intelligence and the Wellbeing of Workers,” published in Nature: Scientific Reports, analyzed two decades of longitudinal data from the German Socio-Economic Panel. The researchers explored how workers in AI-exposed occupations fared compared to those in less-exposed roles.

“We find little evidence that AI adoption has undermined workers’ well-being on average,” said Professor Luca Stella, one of the study’s authors. “If anything, physical health seems to have slightly improved, likely due to declining job physical intensity and overall job risk in some of the AI-exposed occupations.”

However, the researchers also highlight reasons for caution. The analysis relies primarily on a task-based measure of AI exposure, which may not capture the full effects of AI adoption. Alternative estimates based on self-reported exposure reveal small negative effects on job and life satisfaction.

“We may simply be too early in the AI adoption curve to observe its full effects,” Stella emphasized. “AI’s impact could evolve dramatically as technologies advance, penetrate more sectors, and alter work at a deeper level.”

The study’s key findings include:

1. Modest improvements in physical health among employees with less than a college degree.
2. Little evidence of widespread harm to mental health or job satisfaction.
3. Small negative effects on job and life satisfaction reported by workers with self-reported exposure to AI.

The researchers note that the sample excludes younger workers and only covers the early phases of AI diffusion in Germany. They caution that outcomes may differ in more flexible labor markets or among younger cohorts entering increasingly AI-saturated workplaces.

“This research is an early snapshot, not the final word,” said Professor Osea Giuntella, another author of the study. “As AI adoption accelerates, continued monitoring of its broader impacts on work and health is essential.”

Ultimately, the study suggests that the impact of AI on worker well-being may be more complex than initially thought. While it is too soon to draw definitive conclusions, the research highlights the need for ongoing monitoring and analysis of AI’s effects on the workforce.