If you're not a physicist, the concept of "quantum" will likely confuse you, or simply put you off. But even for experts, the quantum world can be complex. Luckily, in a world where for nearly every challenge there's a bespoke robot ready to help, an AI now makes it easier to navigate quantum systems too.
For the first time, physicists have demonstrated that machine learning can reconstruct a quantum system based on relatively few experimental measurements. This method will allow scientists to thoroughly probe systems of particles exponentially faster than conventional, brute-force techniques. Complex systems that would require thousands of years to reconstruct with previous methods could be wholly analyzed in a matter of hours. The research will benefit the development of quantum computers and other applications of quantum mechanics, the researchers report February 26 in Nature Physics. "We have shown that machine intelligence can capture the essence of a quantum system in a compact way," says study co-author Giuseppe Carleo, an associate research scientist at the Center for Computational Quantum Physics at the Flatiron Institute in New York City.
A quantum walk is the quantum mechanical analog of a classical random walk, describing the propagation of quantum walkers (photons) through an optical circuit. Because quantum walks generate large-scale quantum superposed states, they can be used for simulating many-body quantum systems and the development of algorithms for quantum computation. Nejadsattari et al. describe the photonic simulation with cyclic quantum systems. With the ability to simulate a variety of different quantum operations and gates, they claim that the versatility of the approach should allow the study of more complex many-body systems.
In 1994, MIT professor of applied mathematics, Peter Shor, developed a groundbreaking quantum computing algorithm capable of factoring numbers (that is, finding the prime numbers for any integer N) using quantum computer technology. For the next decade, this algorithm provided a tantalizing glimpse at the potential prowess of quantum computing versus classical systems. However researchers could never definitively prove that quantum would always be faster in this application or whether classical systems could overtake quantum if given a sufficiently robust algorithm of its own. In a paper published Thursday in the journal Science, Dr. Sergey Bravyi and his team reveal that they've developed a mathematical proof which, in specific cases, illustrates the quantum algorithm's inherent computational advantages over classical. "It's good to know, because results like this become parts of algorithms," Bob Sutor, vice president of IBM Q Strategy and Ecosystem, told Engadget.