The Missing Engine Behind Quantum Computing Is Literally All Around Us
The answer to one of the biggest questions in quantum computing might have been all around us this whole time: light. In newly published research, a team of scientists from Louisiana and Mexico say they've found a way to corral up to 40 viable photons of light "at room temperature under high levels of noise and decoherence," for use in quantum information crunching. If this finding is validated by further research and implementation, it could help move quantum computing closer to real life.
The paper was recently published in the peer-reviewed journal Advanced Science . The research starts from an understanding that quantum computing, or quantum information processing, is something fundamentally different from the paradigms we have today. The team explains in the paper how their field, reservoir computing, differs from the von Neumann setup that the vast majority of computers have now. Instead, it's closer to a neural network , with "highly-interconnected quantum networks as reservoirs."
In a von Neumann system, information marches through a central processing unit (CPU) almost on a ticker tape. One minuscule math problem at a time is crunched onto that ticker tape, then returned to its host area of the computer —like memory, hard drive, software, or your mouse—to be applied. Everything the computer does gets crunched, one math operation at a time, through the CPU. In a neural net, including a reservoir computer, multiple things can happen at the same time. The computing style is nonlinear, involving clouds of quantum particles.
If that setup sounds thrilling, you've now joined a large group of people who feel excited about it , even if they aren't sure yet how it can be implemented. The industry is bottlenecked by logistics. There's no general purpose quantum computer. "Scalability and fragility have long represented two major challenges to the practical realization of quantum technologies," the team writes. But in their simulation, the scientists were able to extract little quantum pockets from everyday "classical" light sources, corralling reservoirs that they say show a marked improvement over classical computing.
"We demonstrate the potential of our platform to perform complex tasks by using random multiphoton reservoirs to map input signals into higher-dimensional spaces, which are then optimized by low-level neural networks," the team writes. In other words, the simulation shows that the reservoir system can set up and explore functions, taking an input and turning out the function performed on that input, like an algebraic expression and then the integral of that expression.
The secret in this experimental setup is the Fock space, which lets scientists gather particles under one umbrella in order to study how they behave, mathematically. Once a number of particles within the classical light source are corralled in a special circuit, they can be used to do quantum computing, despite their "losses and noise." Light is bent and manipulated at "room temperature," which in quantum mechanics means above freezing .
This work is the next step down a path that has, so far, worked only in much smaller setups. "While these effects have been explored in the context of nanophotonic systems and quantum sensors, their implications for multiphoton quantum information processing have yet to be investigated," the team explains. "These results highlight the robustness and scalability of hybrid quantum technologies against decoherence."
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