Device Sees All Possible Futures

Credit: NTU, Singapore

“When we think about the future, we are confronted by a vast array of possibilities,” explains Assistant Professor Mile Gu of NTU Singapore, who led the development of the quantum algorithm that underpins the prototype. “These possibilities grow exponentially as we go deeper into the future. For instance, even if we have only two possibilities to choose from each minute, in less than half an hour there are 14 million possible futures. In less than a day, the number exceeds the number of atoms in the universe.” He and his research group realized, however, that a quantum computer can examine all possible futures by placing them in a quantum superposition — similar to Schrödinger’s cat, which is simultaneously alive and dead.

To implement this scheme, they joined forces with the experimental group of Professor Geoff Pryde at Griffith University. Together, they implemented a photonic quantum information processor in which the potential future outcomes of a decision process are represented by the locations of photons (quantum particles of light). They then demonstrated that the state of the quantum device is a superposition of multiple potential futures, weighted by their probability of occurrence.

 

Unlike classical particles, quantum particles can travel in a superposition of different directions. In the experiment, the outcomes of a decision process are encoded using photons — quantum particles of light.

 Photo credit: F. Ghafari et al.

“The functioning of this device is inspired by the Nobel Laureate Richard Feynman,” says Dr. Jayne Thompson, a member of the Singapore team. “When Feynman started studying quantum physics, he realized that when a particle travels from point A to point B, it does not necessarily follow a single path. Instead, it simultaneously transverses all possible paths connecting the points. Our work extends this phenomenon and harnesses it for modelling statistical futures.”

The machine has already demonstrated one application: measuring how much our bias towards a specific choice in the present impacts the future. “We can synthesise differently biased quantum superposition of all possible futures,” explains Farzad Ghafari, a member of the experimental team. “By interfering these superpositions with each other, we can completely avoid looking at each possible future individually. This is reminiscent of how current artificial intelligence (AI) algorithms learn by seeing how small changes in behaviour lead to different future outcomes. In the future, our quantum technique may enable quantum-enhanced AIs to learn the effects of their actions much more efficiently.”

The team notes while their present prototype simulates at most 16 futures simultaneously, the underlying quantum algorithm can in principle scale without bound. “This is what makes the field so exciting,” says Pryde. “It is very much reminiscent of classical computers in the 1960s. Just as few could imagine the many uses of classical computers in the 1960s, we are still very much in the dark about what quantum computers can do. Each discovery of a new application provides further impetus for their technological development.”

Contacts and sources:
Nanyang Technological University, College of Science

Citation: Interfering trajectories in experimental quantum-enhanced stochastic simulation.
Farzad Ghafari, Nora Tischler, Carlo Di Franco, Jayne Thompson, Mile Gu, Geoff J. Pryde. Nature Communications, 2019; 10 (1) DOI: 10.1038/s41467-019-08951-2