The researchers have successfully simulated quantum computations with an error correction code known as the Gottesman-Kitaev-Preskill (GKP) code. This code is commonly used in leading implementations of quantum computers and allows for the correction of errors without destroying the quantum information.

The method developed by the researchers consists of an algorithm capable of simulating quantum computations using a bosonic code, specifically the GKP code. This achievement has been deemed impossible until now due to the immense complexity of quantum computations.

“We have discovered a way to simulate a specific type of quantum computation where previous methods have not been effective,” says Cameron Calcluth, PhD in Applied Quantum Physics at Chalmers and first author of the study published in Physical Review Letters. “This means that we can now simulate quantum computations with an error correction code used for fault tolerance, which is crucial for being able to build better and more robust quantum computers in the future.”

The researchers’ breakthrough has far-reaching implications for the development of stable and scalable quantum computers, which are essential for solving complex problems in various fields. The new method will enable researchers to test and validate a quantum computer’s calculations more reliably, paving the way for the creation of truly reliable quantum computers.

The article Classical simulation of circuits with realistic odd-dimensional Gottesman-Kitaev-Preskill states has been published in Physical Review Letters. The authors are Cameron Calcluth, Giulia Ferrini, Oliver Hahn, Juani Bermejo-Vega, and Alessandro Ferraro.

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.”