Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices

AbstractQuantum memories are a fundamental of any global-scale quantum Internet, high-performance quantum networking and near-term quantum computers. A main problem of quantum memories is the low retrieval efficiency of the quantum systems from the quantum registers of the quantum memory. Here, we define a novel quantum memory called high-retrieval-efficiency (HRE) quantum memory for near-term quantum devices. An HRE quantum memory unit integrates local unitary operations on its hardware level for the optimization of the readout procedure and utilizes the advanced techniques of quantum machine learning. We define the integrated unitary operations of an HRE quantum memory, prove the learning procedure, and evaluate the achievable output signal-to-noise ratio values. We prove that the local unitaries of an HRE quantum memory achieve the optimization of the readout procedure in an unsupervised manner without the use of any labeled data or training sequences. We show that the readout procedure of an HRE quantum memory is realized in a completely blind manner without any information about the input quantum system or about the unknown quantum operation of the quantum register. We evaluate the retrieval efficiency of an HRE quantum memory and the output SNR (signal-to-noise ratio). The results are particularly convenient for gate-model quantum computers and the near-term quantum devices of the quantum Internet.

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Quantum Reservoir Computing: A Reservoir Approach Toward Quantum Machine Learning on Near-Term Quantum Devices

Natural Computing Series - Reservoir Computing ◽

10.1007/978-981-13-1687-6_18 ◽

2021 ◽

pp. 423-450

Author(s):

Keisuke Fujii ◽

Kohei Nakajima

Keyword(s):

Machine Learning ◽

Reservoir Computing ◽

Quantum Devices ◽

Quantum Machine Learning ◽

Near Term

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Federated Quantum Machine Learning

Entropy ◽

10.3390/e23040460 ◽

2021 ◽

Vol 23 (4) ◽

pp. 460

Author(s):

Samuel Yen-Chi Chen ◽

Shinjae Yoo

Keyword(s):

Machine Learning ◽

Data Privacy ◽

Research Direction ◽

Future Research ◽

Quantum Computers ◽

Training Time ◽

Quantum Neural Network ◽

Distributed Training ◽

Machine Learning Model ◽

Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

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A quantum convolutional neural network on NISQ devices

AAPPS Bulletin ◽

10.1007/s43673-021-00030-3 ◽

2022 ◽

Vol 32 (1) ◽

Author(s):

ShiJie Wei ◽

YanHu Chen ◽

ZengRong Zhou ◽

GuiLu Long

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Quantum Circuits ◽

Image Smoothing ◽

Quantum Devices ◽

Computing Complexity ◽

Machine Learning Model ◽

Mask Size ◽

Near Term

AbstractQuantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O((log2M)6) basic gates and O(m2+e) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.

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Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures

Entropy ◽

10.3390/e22090936 ◽

2020 ◽

Vol 22 (9) ◽

pp. 936

Author(s):

Milton A. Garcés

Keyword(s):

Machine Learning ◽

Wavelet Transforms ◽

Signal To Noise Ratio ◽

Continuous Wavelet ◽

Signal To Noise ◽

Weighting Functions ◽

Signal Characteristics ◽

Multiple Sensor ◽

New Challenges ◽

Physical Signal

Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.

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GeNets: A unified web platform for network-based analyses of genomic data

10.1101/196303 ◽

2018 ◽

Cited By ~ 1

Author(s):

Taibo Li ◽

April Kim ◽

Joseph Rosenbluh ◽

Heiko Horn ◽

Liraz Greenfeld ◽

...

Keyword(s):

Machine Learning ◽

Functional Genomics ◽

Rna Sequencing ◽

Signal To Noise Ratio ◽

Genomic Data ◽

Learning Model ◽

Signal To Noise ◽

Machine Learning Model ◽

Optimal Network ◽

Web Platform

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

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ODUSSEAS: a machine learning tool to derive effective temperature and metallicity for M dwarf stars

Astronomy and Astrophysics ◽

10.1051/0004-6361/201937194 ◽

2020 ◽

Vol 636 ◽

pp. A9 ◽

Cited By ~ 1

Author(s):

A. Antoniadis-Karnavas ◽

S. G. Sousa ◽

E. Delgado-Mena ◽

N. C. Santos ◽

G. D. C. Teixeira ◽

...

Keyword(s):

Machine Learning ◽

Signal To Noise Ratio ◽

Computational Tool ◽

M Dwarfs ◽

Signal To Noise ◽

Dwarf Stars ◽

M Dwarf Stars ◽

M Dwarf ◽

Python Package ◽

Machine Learning Tool

Aims. The derivation of spectroscopic parameters for M dwarf stars is very important in the fields of stellar and exoplanet characterization. The goal of this work is the creation of an automatic computational tool able to quickly and reliably derive the Teff and [Fe/H] of M dwarfs using optical spectra obtained by different spectrographs with different resolutions. Methods. ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes) is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package “scikit-learn” for predicting the stellar parameters. Results. We show that our tool is able to derive parameters accurately and with high precision, having precision errors of ~30 K for Teff and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions of between 48 000 and 115 000 and a signal-to-noise ratio above 20.

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QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers

Frontiers in Physics ◽

10.3389/fphy.2021.755139 ◽

2022 ◽

Vol 9 ◽

Author(s):

Mahabubul Alam ◽

Swaroop Ghosh

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Network Architecture ◽

Empirical Studies ◽

Quantum Computers ◽

Quantum Neural Network ◽

Small Quantum ◽

Conventional Machine ◽

Near Term ◽

The Impact

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks. Existing QML models that use deep parametric quantum circuits (PQC) suffer from a large accumulation of gate errors and decoherence. To circumvent this issue, we propose a new QML architecture called QNet. QNet consists of several small quantum neural networks (QNN). Each of these smaller QNN’s can be executed on small quantum computers that dominate the NISQ-era machines. By carefully choosing the size of these QNN’s, QNet can exploit arbitrary size quantum computers to solve supervised ML tasks of any scale. It also enables heterogeneous technology integration in a single QML application. Through empirical studies, we show the trainability and generalization of QNet and the impact of various configurable variables on its performance. We compare QNet performance against existing models and discuss potential issues and design considerations. In our study, we show 43% better accuracy on average over the existing models on noisy quantum hardware emulators. More importantly, QNet provides a blueprint to build noise-resilient QML models with a collection of small quantum neural networks with near-term noisy quantum devices.

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Application of machine learning methods to identify englacial seismicity in a Distributed Acoustic Sensing dataset from Store Glacier, West Greenland

10.5194/egusphere-egu21-7448 ◽

2021 ◽

Author(s):

Andrew Pretorius ◽

Emma Smith ◽

Adam Booth ◽

Poul Christofferson ◽

Andy Nowacki ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Crystal Orientation ◽

Time Series Data ◽

Signal To Noise Ratio ◽

Series Data ◽

Seismic Events ◽

Signal To Noise ◽

Controlled Source ◽

Distributed Acoustic Sensing

Seismic surveys are widely used to study the properties of glaciers, basal material and conditions, ice temperature and crystal orientation fabric. The emerging technology of Distributed Acoustic Sensing (DAS) uses fibre optic cables as pseudo-seismic receivers, reconstructing seismic measurements at a higher spatial and temporal resolution than is possible using traditional geophone deployments. DAS generates large volumes of data, especially in passive mode, which can be costly in time and cumbersome to analyse. Machine learning tools provide an effective means of automatically identifying events within these records, avoiding a bottleneck in the data analysis process. Here we present initial trials of machine learning for a borehole-deployed DAS system on Store Glacier, West Greenland. Data were acquired in July 2019, using a Silixa iDAS interrogator and a BRUsens fibre optic cable installed in a 1043 m-deep borehole. The interrogator sampled at 4000 Hz, recording both controlled-source Vertical Seismic Profiles (VSPs), made with hammer-and-plate source, and a 3-day passive record of cryoseismicity.We used a Convolutional Neural Network (CNN) to identify seismic events within the seismic record. A CNN is a deep learning algorithm that uses a series of convolutional filters to extract features from a 2-dimensional matrix of values. These features are then used to train a model that can recognise objects or patterns within the dataset. CNNs are a powerful classification tool, widely applied to the analysis of both images and time series data. Previous research has demonstrated the ability of CNNs to recognise seismic phases in time series data for long-range earthquake detection, even when the phases are masked by a low signal-to-noise ratio. For the Store Glacier data, initial results were obtained using a CNN trained on hand-labelled, uniformly-sized windows. At present, these windows have been targeted around high signal-to-noise ratio seismic events in the controlled-source VSPs only. Once trained, the CNN achieved accuracy of 90% in recognising whether new windows contained coherent seismic energy.The next phase of analysis will be to assess the performance of the CNN when trained and tested on large passive DAS datasets. The method will then be used for the identification and flagging of seismic events within the passive record for interpretation and event location. The identified signals will be used to provide information on the glacier&#8217;s seismic velocity structure, ice temperature and ice crystal orientation fabric and anisotropy. Basal reflections were identified and will be used to provide information on subglacial material properties and conditions of Store Glacier. The efficiency of the CNN allows detailed insight to be made into the origins and style of glacier seismicity, facilitating further advantages of passive DAS instrumentation.

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