Towards a feasible implementation of quantum neural networks using quantum dots

Abstract

We propose an implementation of quantum neural networks using an array of quantum dots with dipole-dipole interactions. We demonstrate that this implementation is both feasible and versatile by studying it within the framework of GaAs based quantum dot qubits coupled to a reservoir of acoustic phonons. Using numerically exact Feynman integral calculations, we have found that the quantum coherence in our neural networks survive for over a hundred ps even at liquid nitrogen temperatures (77 K), which is three orders of magnitude higher than current implementations which are based on SQUID-based systems operating at temperatures in the mK range.

Recent Progress of Quantum Dot‐Based Photonic Devices and Systems: A Comprehensive Review of Materials, Devices, and Applications

“…The promising properties of QDs seem to be well suited for the development of high‐performance memory and neuromorphic computing devices. [ 214–220 ] Solution‐processable QD semiconductors enable to design area‐scalable novel neuromorphic devices due to excellent optoelectronic performance, stability, and size‐tunable properties as well as their low cost and large area fabrication possibility. There have been various promising studies about QD‐based neuromorphic devices, which can be integrated as flash memory, resistive random access memory (RRAM), [ 221,222 ] and photonic synaptic devices.…”

Quantum error-correcting output codes

“…Quantum Machine Learning (QML) is a emerging field of research within quantum computing that can be said to have commenced with the implementation of the quantum Support Vector Machine by Rebentrost, Mohseni & Lloyd [1], and the quantum k-means algorithm by Aïmeur, Brassard & Gambs [2]. In the last few years many quantum versions of well known machine learning methods have been proposed; examples include quantum neural networks [3], quantum principal component analysis [4], quantum nearest neighbours [5], partially observable Markov decision processes [6], Bayesian networks [7], quantum decision trees [8] and quantum annealing [9,10] 1 .…”

“…It will be the endeavour of this paper to demonstrate that decision errors with respect to the output of quantum classifier ensembles are also amenable to error correction. In particular, this work will demonstrate that the existing up-to exponential advantages of quantizing machine learning algorithms demonstrated in [1][2][3][4][5]8] can be further applied to the problem of multi-class ensemble decisionerror correction. This will lead to a cumulative performance boost i.e.…”

Generation of Werner-like states via a two-qubit system plunged in a thermal reservoir and their application in solving binary classification problems

“…Also, the dissipative quantum computation as a reasonable platform has successfully been introduced for stable quantum neural network states 37 . In fact, the dissipative systems reach steady states during the interaction with quantum reservoirs and provide another new direction for quantum computation 2,39 . Mathematically speaking, a data classifier is the simplest neural network unit which connects input nodes to an output node 40 .…”