Complex Deep Learning with Quantum Optics

Manzalini, Antonio

doi:10.3390/quantum1010011

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…Since 2015, the new impetus was given to computer vision design, which was caused by advances in the development of neural network deep learning methods [36][37][38][39].…”

Section: Achievements In the Realization Of Ai Methods For Cnmentioning

confidence: 99%

“…MAS are primarily applied for the modeling of software and hardware agents in the tasks of interaction with the environment and other agents, as well as for space positioning [28][29][30][31][32][33][34][35]. Modern versions of deep learning neural networks are concentrated on computer vision and speech procession [36][37][38][39]. Fuzzy logic has proved to be the effective method to work with uncertain and approximate data, substantially reducing the volume of calculations for autonomous vehicles, industrial systems, and medicine images processing [40][41][42][43].…”

Section: Actual Problems For Communication Networkmentioning

confidence: 99%

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Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Bykovsky

2020

Quantum Reports

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Quantum optics is regarded as the acknowledged method to provide network quantum keys distribution and in the future secure distributed quantum computing, but it should also provide cryptography protection for mobile robots and the Internet of Things (IoT). This task requires the design of new secret coding schemes, which can be also based on multiple-valued logic (MVL). However, this very specific logic model reveals new possibilities for the hierarchical data clustering of arbitrary data sets. The minimization of multiple-valued logic functions is proposed for the analysis of aggregated objects, which is possible for an arbitrary number of variables. In order to use all the useful properties of the multiple-valued logic, the heterogeneous network architecture is proposed, which includes three allocated levels of artificial intelligence (AI) logic modeling for discrete multiple-valued logic, Boolean logic, and fuzzy logic. Multiple-valued logic is regarded as the possible platform for additional secret coding, data aggregation, and communications, which are provided by the united high dimensional space for network addressing and the targeted control of robotic devices. Models of Boolean and fuzzy logic are regarded as separate logic levels in order to simplify the integration of various algorithms and provide control of additional data protection means for robotic agents.

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“…Since 2015, the new impetus was given to computer vision design, which was caused by advances in the development of neural network deep learning methods [36][37][38][39].…”

Section: Achievements In the Realization Of Ai Methods For Cnmentioning

confidence: 99%

Section: Actual Problems For Communication Networkmentioning

confidence: 99%

Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Bykovsky

2020

Quantum Reports

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“…[48,[367][368][369]. Inverse designs and optimization on the photonic crystals, plasmonic nano-structures, programmable meta-materials, and meta-surfaces have been actively explored for high-speed optical communication and computing, ultrasensitive biochemical detection, efficient solar energy harvesting, and super-resolution imaging [370]. By utilizing adaptive linear optics, quantum machine learning is proposed for performing quantum variational classification and quantum kernel estimation [371].…”

Section: Photonic Quantum Computingmentioning

confidence: 99%

Quantum Machine Learning: from physics to software engineering

Melnikov¹,

Kordzanganeh²,

Alodjants³

et al. 2023

Preprint

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Quantum machine learning (QML) is a new, rapidly growing, and fascinating area of research where quantum information science and quantum technologies meet novel machine learning and artificial intelligent facilities. A comprehensive analysis of the main directions of current QML methods and approaches is performed in this review. The aim of our work is twofold. First, we show how classical machine learning approach can help improve the facilities of quantum computers and simulators available today. It is most important due to the modern noisy intermediate-scale quantum (NISQ) era of quantum technologies. In particular, the classical machine learning approach allows optimizing quantum hardware for achieving desired quantum states by implementing quantum devices. Second, we discuss how quantum algorithms and quantum computers may be useful for solving keystone classical machine learning tasks. Currently, quantum-inspired algorithms, which use a quantum approach to classical information processing, represent a powerful tool in software engineering for improving classical computation capacities. In this work, we discuss various quantum neural network capabilities that can be implemented in quantum-classical training algorithms for variational circuits. It is expected that quantum computers will be involved in routine machine learning procedures. In this sense, we are showing how it is essential to elucidate the speedup problem for random walks on arbitrary graphs, which are used in both classical and quantum algorithms. Quantum technologies enhanced by machine learning in fundamental and applied quantum physics, as well as quantum tomography and photonic quantum computing, are also covered.

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“…The QC can provide the solutions for the 6G networks in the domain of increased channel capacity, e.g., new multiple access technologies, such as NOMA, RSMA demand very high power on run time for computation of SIC. Similarly, QC and QML can have a considerable role in 6G in the field of channel estimation, channel coding (quantum turbo codes), localization, load balancing, routing, and multiuser transmissions [ 75 ]. In the communication network core side, QC and QML can solve complex problems such as multi-object exhaustive search by providing fast and optimum path selection to the data-packets in ad hoc sensor networks and Cloud IoT [ 76 ].…”

Section: Potential Technologiesmentioning

confidence: 99%

The shift to 6G communications: vision and requirements

Akhtar

Hassan

Ghaffar

et al. 2020

Hum. Cent. Comput. Inf. Sci.

188

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The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.

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Complex Deep Learning with Quantum Optics

Cited by 10 publications

References 40 publications

Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Quantum Machine Learning: from physics to software engineering

The shift to 6G communications: vision and requirements

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