Adapted by:
Juan Carlos López
Senior WriterAn engineer by training. A science and tech journalist by passion, vocation, and conviction. I've been writing professionally for over two decades, and I suspect I still have a long way to go. At Xataka, I write about many topics, but I mainly enjoy covering nuclear fusion, quantum physics, quantum computers, microprocessors, and TVs.
Karen Alfaro
WriterCommunications professional with a decade of experience as a copywriter, proofreader, and editor. As a travel and science journalist, I've collaborated with several print and digital outlets around the world. I'm passionate about culture, music, food, history, and innovative technologies.
To solve the problems scientists hope to tackle in the future—such as optimization, cryptography, and AI development—quantum computers must correct their own errors. These systems will need several million, possibly hundreds of millions, of qubits. IBM currently holds the most advanced quantum processor, with just over 1,000 qubits. Engineers still need to overcome many technological hurdles.
There’s no single approach. Organizations researching quantum computing are developing multiple types of qubit technologies, each at a different stage of maturity. Large companies like IBM, Intel, and Google have invested in superconducting qubits, along with smaller organizations such as Atlantic Quantum, IQM, Anyon Systems, Rigetti Computing, and Bleximo.
The number of companies backing superconducting qubits suggests it’s the most widely supported and heavily funded technology. In many respects, it leads the way. While this method may scale up to more qubits, it is also more error-prone than ion trap qubits, a viable alternative. These superconducting qubits operate at roughly 20 millikelvins—around minus 273 degrees Celsius—to achieve maximum isolation from environmental interference.
Ion Traps Are Ideal for Proteins
Ion traps now represent the primary alternative to superconducting qubits. Companies like IonQ and Honeywell are developing this technology, which uses ionized atoms with a non-neutral electric charge. This property allows the atoms to be isolated and confined using electromagnetic fields.
IonQ modifies the quantum states of its qubits using ion traps and cools them to reduce computational noise. Lasers then operate on the qubits. Instead of using a single laser, IonQ uses one per ion and a global laser that targets them all simultaneously. Honeywell also works with ionized atoms and lasers, but its method of entangling ions and controlling them with lasers differs from IonQ’s approach.
For scientists, it is crucial to understand the protein folding that triggers Alzheimer’s and Parkinson’s disease.
A research team from IonQ and the German quantum start-up Kipu Quantum used a 36-qubit ion trap computer to solve protein folding problems involving up to 12 amino acids. They designed a quantum optimization method to find the most stable folding configuration.
While this sounds complex, the key takeaway is this: With the right algorithm, quantum computers can help researchers understand the folding process that triggers diseases like Alzheimer’s and Parkinson’s. Understanding that process marks the first step toward developing effective treatments.
This result is promising, but researchers still have significant work ahead before quantum computers can reliably help combat these diseases. Folding models must become more reliable and realistic, and the classical algorithms that refine quantum outputs must also improve. Even so, this research marks an encouraging starting point.
Image | IonQ
View 0 comments