An artificial neuron implemented on an actual quantum processor

Tacchino, Francesco; Macchiavello, Chiara; Gerace, Dario; Bajoni, Daniele

doi:10.1038/s41534-019-0140-4

Cited by 231 publications

(207 citation statements)

References 35 publications

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“…The first step in creating a usable quantum neural network is to model a single quantum neuron (Figure ). In 2018, researchers from the university of Pavia in Italy implemented the first single‐layer neural network on a quantum computer . In a classical neural network with a single neuron, the output is a weighted sum that maps the input vector to the binary output through the activation function.…”

Section: Application Algorithms For Quantum Machine Learningmentioning

confidence: 99%

“…In 2018, researchers from the university of Pavia in Italy implemented the first single-layer neural network on a quantum computer. 43 In a classical neural network with a single neuron, the output is a weighted sum that maps the input vector to the binary output through the activation function. In quantum neural networks, the first layer encodes input vectors as quantum states.…”

Section: Quantum Neural Networkmentioning

confidence: 99%

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Recent advances in quantum machine learning

Zhang

2020

Quantum Engineering

106

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Summary Machine learning is a branch of artificial intelligence, and it has been widely used in many science and engineering areas, such as data mining, natural language processing, computer vision, biological analysis, and so on. Quantum computer is considered as one of the most promising technologies of human beings in the near future. With the development of machine learning and quantum computing, researchers consider to combine these two aspects to gain more benefits. As a result, a novel interdisciplinary subject has emerged—quantum machine learning. This article reviews the state‐of‐the‐art research of algorithms of quantum machine learning and shows a path of the research from the basic quantum information to quantum machine learning algorithms from the perspective of people in the field of computer science.

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Section: Application Algorithms For Quantum Machine Learningmentioning

confidence: 99%

Section: Quantum Neural Networkmentioning

confidence: 99%

Recent advances in quantum machine learning

Zhang

2020

Quantum Engineering

106

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“…[81] The first attempts in this direction have been reported. [229][230][231] Universal quantum simulators might also help solving problems in open quantum system dynamics, for which novel numerical approaches have already been developed. [232,233] Another potentially interesting direction to look at is the digital quantum simulation of bosonic systems, for which limited literature still exists, and where it might turn out to be useful considering the quantum circuit model for the algorithmic solution of a single harmonic oscillator.…”

Section: Outlook and Perspectivesmentioning

confidence: 99%

Quantum Computers as Universal Quantum Simulators: State‐of‐the‐Art and Perspectives

et al. 2019

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The past few years have witnessed the concrete and fast spreading of quantum technologies for practical computation and simulation. In particular, quantum computing platforms based on either trapped ions or superconducting qubits have become available for simulations and benchmarking, with up to few tens of qubits that can be reliably initialized, controlled, and measured. The present Review aims at giving a comprehensive outlook on the state‐of‐the‐art capabilities offered from these near‐term noisy devices as universal quantum simulators, that is, programmable quantum computers potentially able to calculate the time evolution of many physical models. First, a pedagogic overview on the basic theoretical background pertaining digital quantum simulations is given, with a focus on hardware‐dependent mapping of spin‐type Hamiltonians into the corresponding quantum circuit as a key initial step toward simulating more complex models. Then, the main experimental achievements obtained in the last decade are reviewed, focusing on the digital quantum simulation of such spin models by employing two leading quantum architectures. Their performances are compared, and future challenges are outlined, also in view of prospective hybrid technologies, towards the ultimate goal of reaching the long‐sought quantum advantage for the simulation of complex many‐body models in the physical sciences.

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“…If using this proposed neuron we need to consider the state preparation problem, which requires controlling the amplitude of the desired quantum state to realize effectiveness [19,20]. A method making state preparation simple is to limit the structure of the data [21], in which they limit data to the vectors with binary value components.…”

Section: Introductionmentioning

confidence: 99%

Building quantum neural networks based on a swap test

et al. 2019

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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 and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically.

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An artificial neuron implemented on an actual quantum processor

Cited by 231 publications

References 35 publications

Recent advances in quantum machine learning

Recent advances in quantum machine learning

Quantum Computers as Universal Quantum Simulators: State‐of‐the‐Art and Perspectives

Building quantum neural networks based on a swap test

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