Current quantum computers are stuck using binary processes designed for traditional computers.
It’s no secret that quantum computers are advancing in both power and efficiency every day, and may eventually become a strategic asset that could even give governments advantages over one another. Simply looking at the timeline, quantum computing has come a long way in a very short period of time, having achieved quantum supremacy by demonstrating the ability to solve complex problems faster than a traditional computer back in 2019.
Meanwhile, the federal government is planning for a post-quantum future where powerful quantum machines shred today’s encryption protections. NIST and private companies are currently scrambling to create new cybersecurity protections that can withstand a quantum assault. All of that points to a bright future for quantum computers, which only started generating attention and interest around 2016 with their non-traditional computing methods—apparently breaking the laws of physics—to solve complex problems.
However, beneath the surface, quantum computers still face a lot of obstacles. The biggest one is that while they are able to quickly solve most problems, they also return a lot of wrong answers along with the proper solutions. Imagine a student being given a lengthy exam in school, completing it in just a few seconds, and then turning it in with all of the correct answers buried within pages of incorrect responses. That student probably wouldn’t earn a very good grade. And yet, that is the kind of data that quantum computers normally send back in response to queries. This is called “noise” by quantum scientists, and every quantum computer operating today, regardless of how powerful it is, generates a lot of it.
There are many reasons why quantum computers generate noise. They are very fragile, with their calculations influenced by almost any outside factor, including temperature, soundwaves, vibrations, light, invisible quantum entanglements and even background radiation. That is why most quantum machines are housed in dark, vault-like boxes that are kept close to absolute zero.
Beyond environmental factors, one of the biggest reasons for the noise is the fact that a quantum computer’s qubits, which are the equivalent of traditional computing bits, are able to exist in billions of possible states at the same time, whereas traditional computer bits are either a one or a zero, with nothing in between. That is why quantum computers are able to process complex problems so quickly, but also a big reason why there is so much noise in their results. They are able to use superposition—having their qubits exist in multiple states—to solve a problem, but are still bound to binary computing constructs when trying to finalize their results.
Several post-processing solutions have been suggested to improve quantum computing accuracy. For example, combining a traditional supercomputer with a quantum machine and then charging that supercomputer with helping to eliminate the noise might speed up the time needed to get to valid solutions, as might employing artificial intelligence to remove much of the obvious noise. Earlier this year, computer scientists suggested creating better software that would allow users to ask quantum computers better questions from the start.
Most potential solutions to the quantum noise problem involve dealing with the problem after the fact. But now, several scientists have proposed a way to modify the hardware of quantum computers to finally remove their dependency on binary computing. Essentially, their new quantum computer design would allow systems to escape having to adapt to a binary environment.
In an article published in Nature Physics, computer scientists Martin Ringbauer, Michael Meth and others put forward a design for a quantum processor that uses trapped ions as the core processor. This would allow their quantum machine to “think” in nonbinary ways where its qubits were not subjected to just two states of matter (the one and the zero of traditional binary computing). Theoretically, this could not only speed up calculations, but also eliminate the noise that happens when trying to shoehorn quantum computing solutions back into a binary structure.
As they explain it, “Most quantum computers use binary encoding to store information in qubits—the quantum analogue of classical bits. Yet, the underlying physical hardware consists of information carriers that are not necessarily binary, but typically exhibit a rich multilevel structure. Operating them as qubits artificially restricts their degrees of freedom to two energy levels.”
According to the paper, what quantum computers need to improve their accuracy is the ability to operate in higher-dimensional areas known as Hilbert spaces, essentially freeing them from binary computing constructs altogether. Their new hardware design is apparently able to do that, at least in theory. They even call the bits that their machine would use qudits instead of a qubit.
“Here we demonstrate a universal quantum processor using trapped ions that act as qudits with a local Hilbert-space dimension of up to seven,” the authors state in their paper. “With a performance similar to qubit quantum processors, this approach enables the native simulation of high-dimensional quantum systems, as well as more efficient implementation of qubit-based algorithms.”
If quantum computers can be made to operate more efficiently, while also eliminating the noise they generate with their answers, it could boost the already fast-moving quantum development timeline. It might also complicate government efforts to create post-quantum encryption security, which currently is operating on the assumption that quantum computers will still be at least somewhat dependent on binary operations. A computer that could break that bond, could also likely break any post-quantum encryption that still relies on it.
John Breeden II is an award-winning journalist and reviewer with over 20 years of experience covering technology. He is the CEO of the Tech Writers Bureau, a group that creates technological thought leadership content for organizations of all sizes. Twitter: @LabGuys