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.”

A team of researchers from the Graduate School of Engineering Science at Osaka University has made a groundbreaking discovery that could bring quantum computers one step closer to reality. In an article published in PRX Quantum, they have developed a method for preparing high-fidelity “magic states” with unprecedented accuracy and significantly less overhead.

Quantum computers are machines that harness the unique properties of quantum mechanics to perform calculations at speeds millions of times faster than classical computers. These machines could revolutionize fields like engineering, finance, and biotechnology. However, there’s been a significant obstacle holding them back: noise.

Noise is an enemy of quantum computers because even the slightest disturbance can ruin a setup, making it useless. To overcome this challenge, scientists have been exploring ways to build fault-tolerant quantum computers that can continue computing accurately despite noise.

One popular method for creating such systems is called magic state distillation. This process involves preparing a single high-fidelity quantum state from many noisy ones. However, traditional magic state distillation is computationally expensive and requires many qubits (the basic units of quantum information).

The research team was inspired to create a new version of magic state distillation, which they call “level-zero.” In this approach, a fault-tolerant circuit is developed at the physical level of qubits, rather than higher, more abstract levels. This innovation has led to a significant decrease in spatial and temporal overhead compared to traditional methods.

According to lead researcher Tomohiro Itogawa, this breakthrough could bring quantum computers closer to reality: “Noise is absolutely the number one enemy of quantum computers. We’re optimistic that our technique will help make large-scale quantum computers more feasible.”

Keisuke Fujii, senior author of the study, added: “We wanted to explore if there was any way of expediting the preparation of high-fidelity states necessary for quantum computation. Our results show that this is indeed possible, and we’re excited about the potential implications.”