Technology that seems from the realms of the future has a habit of taking seismic leaps and becoming mainstream with a seeming snap of a finger. We just need to look at AI and Microsoft's new Bing Chat to see how next-generation concepts can become part of everyday services. Well, one of the next technologies seeking that mainstream breakthrough is quantum computing.
Quantum computing uses particles on a subatomic level such as photos and electrons and uses quantum mechanics. It creates something known as qubits, which essentially have two states at the same time. This means quantum computers can handle massive amounts of calculations far beyond even the most powerful supercomputers.
Of course, a lot of work goes in between the steps of conception and mainstream success. And so, it is left to researchers to fill that gap and essentially make the technology work. Quantum computing faces numerous challenges. One of them has been how current quantum computers handle tasks.
Encoding Breakthrough
Boston's QuEra Computing says its researchers have found a new encoding method that will allow its quantum computers to handle more tasks. QuEra, a developer of its own computer known as Aquila says until now quantum machines have had limitations:
“Specifically, the native connectivity of the qubits for a given platform often restricts the class of problems that can be addressed. For instance, Rydberg atom arrays naturally allow solving for maximum independent set (MIS) problems, but native encodings are restricted to so-called unit-disk graphs.”
With its new encoding method, Aquila is no longer held back by those limitations:
“Now, new classes of optimization problems can be solved by neutral-atom machines. These include maximum independent sets on graphs with arbitrary connectivity, and quadratic unconstrained binary optimization (QUBO) problems with arbitrary or restricted connectivity.”
Essentially, it means the quantum computer can now handle more tasks/applications.
“Through QuEra's new encoding method, optimized protein design becomes a possibility. In this way, machines such as Aquila will be able to support researchers to more efficiently identify the best samples to press on with in trials. This reduces the resources required to get new types of drugs through the development process and enhances the probability of approval. Consequently, makers of pharmaceuticals may see increased revenue and reduced cost.”
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