Research Bits: Dec. 5

Protonic programmable resistors for AI; optical associative learning; anti-butterfly effect quantum benchmarking.


Protonic programmable resistors for AI

Researchers from the Massachusetts Institute of Technology (MIT) developed an analog deep learning processor based on protonic programmable resistors arranged in an array.

In the processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning. The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte, similar to a battery, that conducts protons but blocks electrons.

“The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” said Bilge Yildiz, professor of nuclear science and engineering and professor of materials science and engineering at MIT.

“The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” said Ju Li, professor of nuclear science and engineering and professor of materials science and engineering at MIT. “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

The researchers used inorganic phosphosilicate glass (PSG) for the electrolyte. PSG is similar to silicon dioxide, used in desiccant bags and in silicon processing, with a small amount of phosphorus added to give it special characteristics for proton conduction. PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion.

Murat Onen, a postdoc at MIT, noted that it can also withstand very strong, pulsed electric fields, which is critical because applying more voltage enables the protons to move faster. “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting.”

“The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” said Li.

Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. Onen added that the insulating properties of PSG means almost no electric current passes through the material as protons move, making the device extremely energy efficient. It is also compatible with silicon fabrication techniques.

The researchers plan to reengineer the programmable resistors for high-volume manufacturing, then study the properties of resistor arrays and scale them up so they can be embedded into systems. They also plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

“Another exciting direction that these ionic devices can enable is energy efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks,” added Yildiz.

Optical associative learning

Researchers from the University of Oxford, university of Exeter, and University of Munster developed an on-chip optical processor capable of detecting similarities in datasets up to 1,000 times faster than conventional machine learning algorithms running on electronic processors.

The work takes inspiration from Pavlov’s famous classical conditioning experiments.

“Pavlovian associative learning is regarded as a basic form of learning that shapes the behavior of humans and animals – but adoption in AI systems is largely unheard of. Our research on Pavlovian learning in tandem with optical parallel processing demonstrates the exciting potential for a variety of AI tasks,” said James Tan You Sian, of the Department of Materials, University of Oxford.

Instead of relying on backpropagation, which neural networks use fine-tune results, the team’s Associative Monadic Learning Element (AMLE) uses a memory material that learns patterns to associate together similar features in datasets, mimicking the conditional reflex observed by Pavlov in the case of a match.

The AMLE inputs are paired with the correct outputs to supervise the learning process, and the memory material can be reset using light signals. In testing, the AMLE could correctly identify cat/non-cat images after being trained with just five pairs of images.

The chip uses wavelength-division multiplexing to send multiple optical signals on different wavelengths on a single channel to increase computational speed.

An associative learning approach could complement neural networks rather than replace them, noted Zengguang Cheng, a professor now at Fudan University. “It is more efficient for problems that don’t need substantial analysis of highly complex features in the datasets. Many learning tasks are volume based and don’t have that level of complexity – in these cases, associative learning can complete the tasks more quickly and at a lower computational cost.”

Anti-butterfly effect quantum benchmarking

Researchers from the Los Alamos National Laboratory propose a new method for benchmarking the performance of quantum computers.

“Using the simple, robust protocol we developed, we can determine the degree to which quantum computers can effectively process information, and it applies to information loss in other complex quantum systems, too,” said Bin Yan, a quantum theorist at Los Alamos National Laboratory. “Our protocol quantifies information scrambling in a quantum system and unambiguously distinguishes it from fake positive signals in the noisy background caused by quantum decoherence.”

Decoherence erases all the quantum information in a quantum computer, while information scrambling through quantum chaos spreads information across the system, protecting it and allowing it to be retrieved.

“Our method, which draws on the quantum anti-butterfly effect we discovered two years ago, evolves a system forward and backward through time in a single loop, so we can apply it to any system with time-reversing the dynamics, including quantum computers and quantum simulators using cold atoms,” Yan said.

The team demonstrated the protocol with simulations on IBM cloud-based quantum computers. The researchers explain that the method prepares a quantum system and subsystem, evolves the full system forward in time, causes a change in a different subsystem, then evolves the system backward for the same amount of time. Measuring the overlap of information between the two subsystems shows how much information has been preserved by scrambling and how much lost to decoherence.

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