Related papers: Artificial Neural Network based on SQUIDs: demonst…
Platforms based on transmission lines are nowadays employed for the simulation of standard phenomena in quantum electrodynamics and quantum field theory. In this work, we propose a hybrid platform, in which a right-handed transmission line…
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build…
We report on the fabrication and electrical transport properties of gate-tunable superconducting quantum interference devices (SQUIDs), made of semiconducting PbS nanowire contacted with PbIn superconducting electrodes. Applied with a…
Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor to hone these techniques, quantum machine learning is budding to serve as an important tool. Using the techniques of deep…
Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is inner product…
We propose quantum neural networks that include multi-qubit interactions in the neural potential leading to a reduction of the network depth without losing approximative power. We show that the presence of multi-qubit potentials in the…
Engineering artificial systems by twisting and stacking van der Waals materials has proven to be an excellent platform for exploring emergent quantum phenomena that can be significantly different from the constituents. Recent advances in…
We present a way to realize a $n$-qubit controlled phase gate with superconducting quantum interference devices (SQUIDs) by coupling them to a superconducting resonator. In this proposal, the two logical states of a qubit are represented by…
Neuromorphic computing aims to reproduce the energy efficiency and adaptability of biological intelligence in hardware. Superconducting devices are an attractive platform due to their ultra-low dissipation and fast switching dynamics. Here…
Superconducting electronic circuits have much to offer with regard to neuromorphic hardware. Superconducting quantum interference devices (SQUIDs) can serve as an active element to perform the thresholding operation of a neuron's soma.…
It is suggested that a quantum neural network (QNN), a type of artificial neural network, can be built using the principles of quantum information processing. The input and output qubits in the QNN can be implemented by optical modes with…
We present a fabrication method of superconducting quantum interference devices (SQUIDs) based on direct write lithography with an Atomic Force Microscope (AFM). This technique involves maskless local anodization of Nb or NbN ultrathin…
This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory and the possibility to train artificial neural…
We present the fabrication of nanoscale superconducting quantum interference devices (SQUIDs) at the apex of wireframe tips on self-aligned superconducting cantilever probes. The probes are made on silicon wafers using molding techniques in…
We propose an approach to realize an $n$-qubit controlled-$U$ gate with superconducting quantum interference devices (SQUIDs) in cavity QED. In this approach, the two lowest levels of a SQUID represent the two logical states of a qubit…
Artificial neural networks have been proposed as potential algorithms that could benefit from being implemented and run on quantum computers. In particular, they hold promise to greatly enhance Artificial Intelligence tasks, such as image…
We propose a superconducting circuit platform for simulating spin-1 models. To this purpose we consider a chain of N ultrastrongly coupled qubit-resonator systems interacting through a grounded SQUID. The anharmonic spectrum of the…
Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of…
Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum…
This paper presents a very simple architecture for a large-scale superconducting quantum computer. All of the SQUID qubits are fixed-coupled to a single large superconducting loop.