System Bits: Sept. 11

Researchers ‘teleport’ a quantum gate
In a key architectural step for building modular quantum computers, Yale University researchers have demonstrated the teleportation of a quantum gate between two qubits, on demand.

A network overview of the modular quantum architecture demonstrated in the new study.
Source: Yale University

According to the team, the key principle behind this new work is quantum teleportation, a unique feature of quantum mechanics that has been previously used to transmit unknown quantum states between two parties without physically sending the state itself. Using a theoretical protocol developed in the 1990s, the researchers said they have experimentally demonstrated a quantum operation, or gate, without relying on any direct interaction.

Gates such as these are necessary for quantum computation that relies on networks of separate quantum systems — an architecture that many researchers say can offset the errors that are inherent in quantum computing processors, the team said.

Through the Yale Quantum Institute, the Yale research team led by principal investigator Robert Schoelkopf and former graduate student Kevin Chou is investigating a modular approach to quantum computing. Modularity is found in everything from the organization of a biological cell to the network of engines in the latest SpaceX rocket, and has proved to be a powerful strategy for building large, complex systems, the researchers explained. For example, a quantum modular architecture consists of a collection of modules that function as small quantum processors connected into a larger network.

Modules in this architecture have a natural isolation from each other, which reduces unwanted interactions through the larger system but this isolation also makes performing operations between modules a distinct challenge, they said. Interestingly, teleported gates are a way to implement inter-module operations, and Chou pointed out that this work is the first time that this protocol has been demonstrated where the classical communication occurs in real-time, allowing for the implementation of a ‘deterministic’ operation that performs the desired operation every time.

The findings appear online Sept. 5 in the journal Nature.

Theoretical quantum computing optical device
According to MIT researchers, nearly 150 years ago, the physicist James Maxwell proposed that a circular lens that is thickest at its center, and that gradually thins out at its edges, should exhibit some fascinating optical behavior. Namely, when light is shone through such a lens, it should travel around in perfect circles, creating highly unusual, curved paths of light. The MIT researchers also reminded that Maxwell said such a lens, at least broadly speaking, resembles the eye of a fish. This lens configuration he devised has since been known in physics as Maxwell’s fish-eye lens — a theoretical construct that is only slightly similar to commercially available fish-eye lenses for cameras and telescopes. MIT and Harvard University scientists have now studied this lens from a quantum mechanical perspective to see how individual atoms and photons may behave within the lens. Interestingly, they report that the unique configuration of the fish-eye lens enables it to guide single photons through the lens, in such a way as to entangle pairs of atoms, even over relatively long distances.

James Maxwell was the first to realize that light is able to travel in perfect circles within the fish-eye lens because the density of the lens changes, with material being thickest at the middle and gradually thinning out toward the edges.
Source: MIT


First author Janos Perczel, a graduate student in MIT’s Department of Physics said, “We found that the fish-eye lens has something that no other two-dimensional device has, which is maintaining this entangling ability over large distances, not just for two atoms, but for multiple pairs of distant atoms. Entanglement and connecting these various quantum bits can be really the name of the game in making a push forward and trying to find applications of quantum mechanics.”

They also found that the fish-eye lens, contrary to recent claims, does not produce a perfect image. Scientists have thought that Maxwell’s fish-eye may be a candidate for a “perfect lens” — a lens that can go beyond the diffraction limit, meaning that it can focus light to a point that is smaller than the light’s own wavelength. This perfect imaging, scientist predict, should produce an image with essentially unlimited resolution and extreme clarity. However, by modeling the behavior of photons through a simulated fish-eye lens, at the quantum level, Perczel and his colleagues concluded that it cannot produce a perfect image, as originally predicted.

“This tells you that there are these limits in physics that are really difficult to break. Even in this system, which seemed to be a perfect candidate, this limit seems to be obeyed. Perhaps perfect imaging may still be possible with the fish eye in some other, more complicated way, but not as originally proposed,” Perczel said.

Going forward, the team hopes to work with experimentalists to test the quantum behaviors they observed in their modeling. In fact, in their paper, the team also briefly proposes a way to design a fish-eye lens for quantum entanglement experiments.

Simulating the human brain’s function
A research team led by Director Myoung-Jae Lee from the Intelligent Devices and Systems Research Group at Daegu Gyeongbuk Institute of Science and Technology (DGIST) reported that it has succeeded in developing an artificial synaptic device that mimics the function of the nerve cells (neurons) and synapses that are response for memory in human brains.

This chemical synapse information transfer system, which transfers information from the brain, can handle high-level parallel arithmetic with very little energy, so research on artificial synaptic devices, which mimic the biological function of a synapse, is under way worldwide. 

Lee’s research team, through joint research with teams led by Professor Gyeong-Su Park from Seoul National University; Professor Sung Kyu Park from Chung-ang University; and Professor Hyunsang Hwang from POSTEC, developed a high-reliability artificial synaptic device with multiple values by structuring tantalum oxide a trans-metallic material  into two layers of Ta2O5-x and TaO2-x and by controlling its surface.

They said this artificial synaptic device is an electrical synaptic device that simulates the function of synapses in the brain as the resistance of the tantalum oxide layer gradually increases or decreases depending on the strength of the electric signals, and has succeeded in overcoming durability limitations of current devices by allowing current control only on one layer of Ta2O5-x.

In addition, the research team said it has successfully implemented an experiment that realized synapse plasticity, which is the process of creating, storing, and deleting memories, such as long-term strengthening of memory and long-term suppression of memory deleting by adjusting the strength of the synapse connection between neurons.

The non-volatile multiple-value data storage method applied by the research team has the technological advantage of having a small area of an artificial synaptic device system, reducing circuit connection complexity, and reducing power consumption by more than one-thousandth compared to data storage methods based on digital signals using 0 and 1 such as volatile CMOS (Complementary Metal Oxide Semiconductor).

The high-reliability artificial synaptic device developed by the research team can be used in ultra-low-power devices or circuits for processing massive amounts of big data due to its capability of low-power parallel arithmetic. It is expected to be applied to next-generation intelligent semiconductor device technologies such as development of artificial intelligence (AI) including machine learning and deep learning and brain-mimicking semiconductors.

“This research secured the reliability of existing artificial synaptic devices and improved the areas pointed out as disadvantages. We expect to contribute to the development of AI based on the neuromorphic system that mimics the human brain by creating a circuit that imitates the function of neurons,” Lee added. 

The full paper can be found here.