Researchers at the John Hopkins Applied Physics Laboratory (APL) teamed up with their counterparts at John Hopkins University to develop a comprehensive framework to model quantum noise for superconducting quantum processors. This is expected to help develop better error correction strategies leading to fault tolerant quantum computers in the future. 

Quantum computing is the next big leap in computing that researchers are keen to develop thanks to its exponentially fast computational speeds. Even the world’s fastest supercomputers fall exceedingly short when compared to a quantum computer with just a few hundred qubits. 

Yet, quantum computers aren’t being deployed to solve real-world problems. The reason is the error accumulation in quantum computation. Quantum bits or qubits are naturally prone to noise, where a change in temperature, electric or magnetic field can lead to the accumulation of errors, prompting the need to build robust error correction mechanisms. 

Modeling noise in quantum computers

A research team led by Gregory Quiroz, a physicist at APL decided to study quantum noise in real operating environments for multi-qubit quantum computers. So, they accessed a quantum cloud consisting of 39 qubits across seven superconducting devices. 

The researchers were keen to study transmons, a specific type of superconducting qubit which is known for its reduced sensitivity to noise from electric fields. Working with a cloud-based system also presented another challenge since the researchers did not have low-level access to the hardware. 

However Quiroz noted that the challenge was much like a real world simulation, where users do not have low-level access to hardware in proprietary systems either. Instead of attempting to study noise effects on a single operation in detail, the researchers ran repeated computations on the quantum cloud to accumulate errors. 

By looking into how errors accumulated and how deviant the answers were from those expected, the researchers were able to get valuable insights into how the physical system was working. 

Building a comprehensive model

Attempts at studying errors in quantum computation have traditionally worked with only one type of error, either coherent or incoherent. The latter are errors where information stored on qubits is lost and not much can be done about them. 

However, the coherent ones are caused by flaws in the hardware and are therefore fixable. Interestingly, APL’s approach of working with a quantum cloud allowed them to study both incoherent and coherent errors in the same framework. 

We were able to put a wide variety of errors together into one model, which is simple in terms of parameters but also comprehensive in the types of phenomena it can describe — even predicting the performance of small quantum algorithms,” added Yasuo Oda, postdoctoral researcher, who worked with Quiroz on the model. “That’s our biggest contribution.”

“Now that we have this low-weight noise model, we have the opportunity to apply it across all levels of the quantum computing stack, from hardware design to algorithm design to error correction,” said Quiroz in a press release. “The information we can get from the model can inform every level of the quantum computing stack.”

The research findings were published in the journal PRX Quantum.

Scientists at the Ames National Laboratory in the U.S. have developed a systematic path to the discovery of new approaches to building permanent magnets without relying on rare-earth elements. The approach combines physics-based modeling and high-throughput simulations with reasoning-based artificial intelligence (AI) tools to guide the discovery process even before materials are made in the laboratory. 

Permanent magnets are a crucial part of our everyday lives. From aiding data storage to helping us drive electric vehicles (EVs) to enabling high-quality medical imaging, permanent magnets are used everywhere. 

High-performance permanent magnets made with rare-earth elements are used in defense applications and energy generation. However, the U.S. is highly dependent on other countries to refine its rare earth elements, which increases costs and security risks. Scientists at the Ames National Laboratory have therefore been tasked with developing permanent magnets that do not use rare-earth elements. 

AI, AI everywhere

At a time when AI adoption is soaring and no organization wants to remain behind, another story about AI workflows is not a major surprise. However, for an AI model to perform perfectly, it must be trained on the right data. 

For a general use case, an AI model can be trained on general data. However, for an AI that needs to excel in materials science, a research team led by Prashant Singh, a scientist at Ames, ensured that models were trained on experimentally measured and scientifically calculated material properties. Only this can ensure that the predictions the AI model is making are grounded in real-world behavior. 

“Understanding the physics of materials is important to include in AI frameworks when you are trying to design new materials. If you just use the data to train your models, you are going to get only the predictions within the range of information you have,” said Singh in a press release. 

“But once you understand the physics of what controls specific properties, then you and your agentic tools or AI frameworks can search arbitrary material space,” added Singh. 

Addressing the full pipeline

The advantage of using such an AI-based approach is that it uses a material’s atomic structure and electronic behavior to determine its properties. Magnetization strength, resistance to demagnetization, energy storage capacity, and the permanent magnet’s behavior at high temperatures are major parameters that determine its usefulness. 

Using an AI model that accounts for this allows researchers to identify the most promising material candidates and arrive at likely outcomes through computations rather than real-world iterations. 

“Ames Lab’s strength comes from its deep expertise and a long history of data in the magnet space that no other institution has,” elaborated Singh in the press release. 

“In any material design problem, you need to know how combining two elements will change their performance before you ever run an experiment. We have been building both theoretical and analytical tools to answer that question, and we are now bringing AI into that process to make it faster and broader in scope.”

However, the researchers did not just stop there. The AI tools also take into account the availability and cost of the material. Since the frailty of supply chains has been repeatedly exposed in the past decade, the AI models also factor in these conditions during the discovery process itself to ensure that the material developed is practically possible and can be scaled. 

In doing so, the researchers have addressed the complete pipeline from discovery to industrial availability as the U.S. looks to reduce its dependence on other countries for critical components. 

The research findings were published in the journal Materials Science and Engineering.