At Xanadu we are developing a photonic quantum computer: a device that processes information stored in quantum states of light. We are very excited by the possibilities that this approach brings. Photonic quantum computers naturally use continuous degrees of freedom -- like the amplitude and phase of light -- to encode information. This continuous, or analog, structure makes photonic devices a natural platform for quantum versions of neural networks. How do we mimic a neural network using a photonic system?
Maybe quantum computing is a job for artificial intelligence. To call quantum computing complicated is a gross understatement. Rather than any single complex challenge, quantum computing is a series of obstacles all superimposed (pun intended) onto each other. Even though quantum processors based on superconducting circuits already exist in labs today, they don't compare in speed or processing power to today's typical desktop, laptop, and tablet computers. Even if you can settle on materials, a physical architecture, and a form factor for your quantum device, you're still faced with the very real difficulties of actually measuring quantum signals so you can take advantage of the processing and storage enhancements offered by quantum computing.
Italian researchers recently developed the first functioning quantum neural network by running a special algorithm on an actual quantum computer. The team, lead by Francesco Tacchino of the University of Pavia in Italy, pre-published their research on ArXiv earlier this month in a research paper titled "An Artificial Neuron Implemented on an Actual Quantum Processor." Basically, they developed a single-layer artificial neural network (ANN) that runs on a quantum computer. This kind of rudimentary ANN is called a perceptron, and it's the basic building block of more robust neural networks. Previous attempts at building a perceptron on a quantum system have involved treating individual qubits as neurons in a network.
The same type of artificial intelligence that mastered the ancient game of Go could help wrestle with the amazing complexity of quantum systems containing billions of particles. Google's AlphaGo artificial neural network made headlines last year when it bested a world champion at Go. After marvelling at this feat, Giuseppe Carleo of ETH Zurich in Switzerland thought it might be possible to build a similar machine-learning tool to crack one of the knottiest problems in quantum physics. Now, he has built just such a neural network – which could turn out to be a game changer in understanding quantum systems. Go is far more complex than chess, in that the number of possible positions on a Go board could exceed the number of atoms in the universe.
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.