Quantum neuromorphic computing (QNC) is a novel method that combines quantum computing with brain-inspired neuromorphic computing. Neuromorphic computing performs computations using a complex ensemble of artificial neurons and synapses (i.e., electrical circuits) to emulate the human brain. QNC may lead to a quantum advantage by realizing these components with quantum memory elements, or memelements, which can store and process quantum information within the same device. This research aims to achieve experimental realization of superconducting quantum memelements, which has never been done before. A quantum memcapacitor will be fabricated by depositing and patterning thin aluminum films, and then cooling to cryogenic temperatures to unveil quantum-mechanical properties in highly nonlinear regimes. The success of the device will be demonstrated by measuring a characteristic Lissajous curve with a pinched hysteresis, which is a hallmark of a memelement. A variety of memcapacitor regimes will then be investigated, including two-photon memcapacitive processes, loss and temperature effects. Finally, entanglement between two quantum memcapacitors will be shown theoretically and experimentally, paving the way toward an actual QNC. QNC will lead to new knowledge on quantum technologies by helping develop improved fabrication and quantum machine learning techniques inspired by the brain. Further, investigating the quantum mechanical properties of quantum memelements acting as artificial neurons in dissipative environments may provide further insight into the working principles of the human brain.
Figure 1. Optical images of a typical superconducting quantum device similar to the one investigated in this project.
Harnessing the Promise of Quantum Materials for Future Electronic Devices
Summary Two-dimensional (2D) quantum materials, such as graphene and molybdenum disulfide, have great potential for use in future flexible and wearable electronics applications. With traditional silicon-based electronics nearing their theoretical performance limits, nano-electronics made from 2D quantum materials offer breakthrough opportunities for energy-efficient, wearable ubiquitous computation. In this project, we will study integration of […]
June 14, 2018
Engineering and Characterizing Programmable Interaction Graphs in a Trapped Ion Quantum Simulator
Summary Quantum simulators have the potential to bring unprecedented capabilities in areas such as the discovery of new materials and drugs. Engineering precise and programmable interaction graphs between qubits or spins forms the backbone of simulator applications. The trapped ion system is unique in that the interaction graph between qubits can be programmed, in […]
July 24, 2018
Repurposing potential drug candidates for the treatment of COVID-19
Summary The main protease (Mpro) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus disease (COVID-19), has emerged as a promising drug target. The scientific community has produced a large number of crystallographic structures of the protease, which mediates viral replication and transcription. These structures report several fragments with varied chemotypes […]
May 6, 2020
Zero-Dimensional Quantum Materials for the Next Generation of Highly-Selective Chemical Sensors
Summary Heavy metals are a major public health concern and their on-site detection in water supplies is not well served by existing lab techniques. We develop a new multi-modal platform comprising functionalized quantum dots of two-dimensional materials (2D-QDs) for the sensing of four highly-toxic heavy metal pollutants (arsenic, cadmium, lead and mercury). The zero-dimensional […]
March 11, 2019