The AI model significantly outperformed traditional clinical guidelines, achieving an accuracy rate of 89% across all patients, with a remarkable 93% accuracy for individuals between 40-60 years old – the population most at-risk for sudden cardiac death. By accurately predicting patient risk, doctors can tailor medical plans to suit individual needs, reducing unnecessary interventions and saving lives.

Led by researcher Natalia Trayanova, the team’s findings were published in Nature Cardiovascular Research. The study demonstrates the potential of AI to transform clinical care, particularly in high-risk areas such as sudden cardiac death prediction. With further testing and expansion to other heart diseases, this technology has the potential to save many lives and improve patient outcomes.

In an interview, Trayanova noted that current clinical guidelines for identifying patients at risk have about a 50% chance of success – “not much better than throwing dice.” The AI model’s accuracy is a significant improvement, with Trayanova stating that it can predict with high accuracy whether a patient is at very high risk for sudden cardiac death or not.

The team tested the MAARS model against real patients treated with traditional clinical guidelines at Johns Hopkins Hospital and Sanger Heart & Vascular Institute in North Carolina. The results showed that the AI model was more accurate than human clinicians, with an impressive 93% accuracy rate for individuals between 40-60 years old.

The study’s co-author, Jonathan Crispin, a Johns Hopkins cardiologist, stated that the research demonstrates the potential of AI to transform clinical care and enhance patient outcomes. The team plans to further test the MAARS model on more patients and expand its use to other heart diseases, including cardiac sarcoidosis and arrhythmogenic right ventricular cardiomyopathy.

The development of this AI model offers a glimmer of hope for those affected by hypertrophic cardiomyopathy and sudden cardiac death, providing a new tool for doctors to accurately predict patient risk and tailor medical plans accordingly. As the research continues to evolve, it has the potential to save many lives and improve patient outcomes worldwide.

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 been touted as potential game-changers for computation, medicine, coding, and material discovery – but only when they truly function. One major obstacle has been noise or errors produced during computations on a quantum machine, making them less powerful than classical computers – until recently.

Daniel Lidar, holder of the Viterbi Professorship in Engineering and Professor of Electrical & Computing Engineering at USC Viterbi School of Engineering, has made significant strides in quantum error correction. In a recent study with collaborators at USC and Johns Hopkins, he demonstrated a quantum exponential scaling advantage using two 127-qubit IBM Quantum Eagle processor-powered quantum computers over the cloud.

The key milestone for quantum computing, Lidar says, is to demonstrate that we can execute entire algorithms with a scaling speedup relative to ordinary “classical” computers. An exponential speedup means that as you increase a problem’s size by including more variables, the gap between the quantum and classical performance keeps growing – roughly doubling for every additional variable.

Lidar clarifies that this type of speedup is unconditional, meaning it doesn’t rely on unproven assumptions. Prior speedup claims required assuming there was no better classical algorithm against which to benchmark the quantum algorithm. This study used an algorithm modified for the quantum computer to solve a variation of “Simon’s problem,” an early example of quantum algorithms that can solve tasks exponentially faster than any classical counterpart, unconditionally.

Simon’s problem involves finding a hidden repeating pattern in a mathematical function and is considered the precursor to Shor’s factoring algorithm, which can be used to break codes. Quantum players can win this game exponentially faster than classical players.

The team achieved their exponential speedup by squeezing every ounce of performance from the hardware: shorter circuits, smarter pulse sequences, and statistical error mitigation. They limited data input, compressed quantum logic operations using transpilation, applied dynamical decoupling to detach qubits from noise, and used measurement error mitigation to correct errors left over after dynamical decoupling.

Lidar says that this result shows today’s quantum computers firmly lie on the side of a scaling quantum advantage. The performance separation cannot be reversed because the exponential speedup is unconditional – making it increasingly difficult to dispute. Next steps include demonstrating practical real-world applications, reducing noise and decoherence in ever larger quantum computers, and addressing the lack of oracle-based speedups.