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Register a new userLight-stimulable molecules/nanoparticles networks for switchable logical functions and reservoir computing
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Added by
Dominique Vuillaume
Publication date
2018
fields
Physics
and research's language is
English
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We report the fabrication and electron transport properties of nanoparticles self-assembled networks (NPSAN) of molecular switches (azobenzene derivatives) interconnected by Au nanoparticles, and we demonstrate optically-driven switchable logical operations associated to the light controlled switching of the molecules. The switching yield is up to 74%. We also demonstrate that these NPSANs are prone for light-stimulable reservoir computing. The complex non-linearity of electron transport and dynamics in these highly connected and recurrent networks of molecular junctions exhibit rich high harmonics generation (HHG) required for reservoir computing (RC) approaches. Logical functions and HHG are controlled by the isomerization of the molecules upon light illumination. These results, without direct analogs in semiconductor devices, open new perspectives to molecular electronics in unconventional computing.
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We study single-electron tunneling (SET) characteristics in crystalline PbS/InP junctions, that exhibit single-electron Coulomb-blockade staircases along with memory and memory-fading behaviors. This gives rise to both short-term and long-term plasticities as well as a convenient non-linear response, making this structure attractive for neuromorphic computing applications. For further insights into this prospect, we predict typical behaviors relevant to the field, obtained by an extrapolation of experimental data in the SET framework. The estimated minimum energy required for a synaptic operation is in the order of 1 fJ, while the maximum frequency of operation can reach the MHz range.
Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can be leveraged to achieve waveform classification for an input signal encoded in the amplitude of the input voltage. Here we show that the frequency and the phase of the oscillator can also be used to recognize waveforms. For this purpose, we phase-lock the oscillator to the input waveform, which carries information in its modulated frequency. In this way we considerably decrease amplitude, phase and frequency noise. We show that this method allows classifying sine and square waveforms with an accuracy above 99% when decoding the output from the oscillator amplitude, phase or frequency. We find that recognition rates are directly related to the noise and non-linearity of each variable. These results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features.
Optical wave-based computing has enabled the realization of real-time information processing in both space and time domains. In the past few years, analog computing has experienced rapid development but mostly for a single function. Motivated by parallel space-time computing and miniaturization, we show that reconfigurable graphene-based metasurfaces offer a promising path towards spatiotemporal computing with integrated functionalities by properly engineering both spatial- and temporal-frequency responses. This paper employs a tunable graphene-based metasurface to enable analog signal and image processing in both space and time by tuning the electrostatic bias. In the first part of the paper, we propose a switchable analog computing paradigm in which the proposed metasurface can switch among defined performances by selecting a proper external voltage for graphene monolayers. Spatial isotropic differentiation and edge detection in the spatial channel and first-order temporal differentiation and metasurface-based phaser with linear group-delay response in the temporal channel are demonstrated. In the second section of the paper, simultaneous and parallel spatiotemporal analog computing is demonstrated. The proposed metasurface processor has almost no static power consumption due to its floating-gate configuration. The spatial- and temporal-frequency transfer functions (TFs) are engineered by using a transmission line (TL) model, and the obtained results are validated with full-wave simulations. Our proposal will enable real-time parallel spatiotemporal analog signal and image processing.
Current, near-term quantum devices have shown great progress in recent years culminating with a demonstration of quantum supremacy. In the medium-term, however, quantum machines will need to transition to greater reliability through error correction, likely through promising techniques such as surface codes which are well suited for near-term devices with limited qubit connectivity. We discover quantum memory, particularly resonant cavities with transmon qubits arranged in a 2.5D architecture, can efficiently implement surface codes with substantial hardware savings and performance/fidelity gains. Specifically, we *virtualize logical qubits* by storing them in layers distributed across qubit memories connected to each transmon. Surprisingly, distributing each logical qubit across many memories has a minimal impact on fault tolerance and results in substantially more efficient operations. Our design permits fast transversal CNOT operations between logical qubits sharing the same physical address which are 6x faster than lattice surgery CNOTs. We develop a novel embedding which saves ~10x in transmons with another 2x from an additional optimization for compactness. Although Virtualized Logical Qubits (VLQ) pays a 10x penalty in serialization, advantages in the transversal CNOT and area efficiency result in performance comparable to 2D transmon-only architectures. Our simulations show fault tolerance comparable to 2D architectures while saving substantial hardware. Furthermore, VLQ can produce magic states 1.22x faster for a fixed number of transmon qubits. This is a critical benchmark for future fault-tolerant quantum computers. VLQ substantially reduces the hardware requirements for fault tolerance and puts within reach a proof-of-concept experimental demonstration of around 10 logical qubits, requiring only 11 transmons and 9 attached cavities in total.
Ferroelectric tunnel junctions (FTJ) based on hafnium zirconium oxide (Hf1-xZrxO2; HZO) are a promising candidate for future applications, such as low-power memories and neuromorphic computing. The tunneling electroresistance (TER) is tunable through the polarization state of the HZO film. To circumvent the challenge of fabricating thin ferroelectric HZO layers in the tunneling range of 1-3 nm range, ferroelectric/dielectric double layer sandwiched between two symmetric metal electrodes are used. Due to the decoupling of the ferroelectric polarization storage layer and a dielectric tunneling layer with a higher bandgap, a significant TER ratio between the two polarization states is obtained. By exploiting previously reported switching behaviour and the gradual tunability of the resistance, FTJs can be used as potential candidates for the emulation of synapses for neuromorphic computing in spiking neural networks. The implementation of two major components of a synapse are shown: long term depression/potentiation by varying the amplitude/width/number of voltage pulses applied to the artificial FTJ synapse, and spike-timing-dependent-plasticity curves by applying time-delayed voltages at each electrode. These experimental findings show the potential of spiking neural networks and neuromorphic computing that can be implemented with hafnia-based FTJs.
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