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Industry Revolution 5.0
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Keywords- Computer vision, Industrial revolution (5.0), NLP, PISA, (QNNM) Quantum computer, (BQMatNet).

1.Introduction
Professional & industrial software automation (PISA) technology basically idea developed by Zumosun soft invention Pvt. Ltd. company engineers and scientist with special (BQMatNet) algorithms develop to interaction the machine with data, content, photos and video contents with human intelligence (HI). PISA is an integrated designed technology to assist industry revolution (5.0) professionals, such as lawyers, auditors, financiers, economist, and IT consultants, as well as assist to automatic operation and manufacturing control systems with project management and resource management for client projects and utilization rate management for billable staff. This is accomplished by developing biological quantum metrics neural networks ((BQMatNet)) to quantify and qualify basic business processes that can then be used to streamline and improve those processes. Typical PISA functions include the operation of the industry with renewable energy, eliminate resources wastes, production management, project management and documentation, time recording, billing, reporting, labor utilization many fore features.

These features are ingrate with human intelligence (HI) and artificial intelligence (AI) like computer vision, quantum computing, natural language processing (NPL). These features are often integrated with, renewable energy; eliminate the resources wastes accounting, Customer Relationship Management (CRM) systems, and payroll systems in order to improve the efficiency of overall operations, inventory management, production cycle management, and many more. As a result, in addition to better managing industry operation, independent contractors can prevent lost revenue and slow business cycles.
Ultimately PISA Technology suites allow users to integrate industry-appropriate biological quantum metrics neural networks (BOMatNet) with human intelligence (HI) in order to better understanding of manufacturing & services operations cycles and, in turn, improve quality, efficiency and profitability. As businesses grow, the size and complexity of their projects tend to increase as well. PISA technology will use to provide visibility into mid and large project profitability. 
Those familiar with industry automation may like to think of PISA technology as an industry revolution (5.0) system for manufacturing & service organizations.

2. Biological Quantum  Metrics Neural Networks (BQMatNet)
Traditional neural networks assume vectorial inputs as the network is arranged like layers of a single line of computing units called neurons. This special structure requires non-vectorial inputs such as matrices to be converted into vectors. This process can be problematic. Firstly, the spatial information among elements of the data may be lost during vectorization. Secondly, the solution space becomes very large which demands very special treatments to the network parameters and high computational cost. To address these issues, we propose biological quantum metrics neural networks (BQMatNet), which take matrices directly as inputs and superposition and entanglement principle of quantum-mechanical phenomena. Each neuron senses summarized under the sigma evolution process and information through bilinear mapping from lower layer units in exactly the same way as the classic feed-forward quantum neural networks. Under this structure, back prorogation and gradient descent combination can be utilized to obtain network parameters efficiently. Furthermore, it can be conveniently extended for multimodal inputs. We apply (BQMatNet) to integrated database handwritten digits classification and image and video super-resolution tasks to show its effectiveness. Without too much tweaking (BQMatNet) achieves comparable performance as the state-of-the-art methods in both tasks with considerably reduced complexity.

3. Artificial Quantum Neural Networks
In the computational approach to quantum neural network research, our scientists try to combine artificial neural network models which are widely used in machine learning for the important task of pattern classification and recognition) with the advantages of quantum information in order to develop higher-order efficient algorithms. One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism, superposition or the effects of interference and entanglement can be used as resources. Since the technological implementation of a quantum computer neural network is still in a premature stage in our organization, such quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments and commercial utilization.
Quantum neural network research and innovation is still in its infancy, and a conglomeration of proposals and ideas of varying scope and mathematical, quantum mechanics rigor has been put forward. Most of them are based on the idea of replacing classical binary resulting in quantum neural units that can be in a superposition of the state ‘firing’ and ‘resting’.

4. Quantum Knowledge Basics Systems (QKBS)
Top learning algorithms follow the classical model of training an artificial neural network to learn the input-output function of a given training set and use classical feedback loops to update parameters of the quantum system until they converge to an optimal configuration. Adiabatic models of quantum computing have also approached learning as a parameter optimization problem. Recently there has been proposed a new post-learning strategy to allow the search for an improved set of weights based on an analogy with quantum effects occurring in nature. The technique, proposed in is based on the analogy of modeling a biological neuron as a semiconductor heterostructure consisting of one energetic barrier sandwiched between two energetically lower areas. The activation function of the neuron is therefore considered as a particle entering the heterostructure and interacting with the barrier. In this way, auxiliary reinforcement to the classical learning process of neural networks is achieved with minimal additional computational costs.

4.1 Biological Quantum Generalizations of Neural Networks 
Our researcher, scientists, and visionary leaders are working on the analysis of constructing a biological quantum neuron network is to first generalize classical human intelligence (HI) and artificial intelligence (AI) neurons (by the padding of ancillary bits) to reversible permutation gates and then generalizing them further to make unitary gates. Due to the no-cloning theorem in quantum mechanics, the copying of the output before sending it to several neurons in the next layer is non-trivial. This can be replaced with a general quantum unitary acting on the output plus a dummy bit in state |0⟩. That has the classical copying gate (CNOT) as a special case, and in that sense generalizes the classical copying operation. It can be demonstrated that in this scheme, the quantum neural networks can: (i) compress quantum states onto a minimal number of quantum bits (Qbits), creating a quantum auto-encoder, and (ii) discover quantum communication protocols such as teleportation. The general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.

4.2 Biological quantum neural networks
Our quantum neural network researchers explicitly limit their scope to a computational perspective; the field is closely connected to investigations of potential quantum effects in biological research automatic integration neural networks (BRAINN). Models of cognitive agents and memory based on quantum collectives integration have been proposed by Prakash Chand Sharma, but he also points to specific problems of limits on observation and control of these memories due to fundamental logical reasons. He has also proposed that a quantum language must be associated with biological integration quantum cloud neural networks (BIQCNN). 
The combination of quantum physics, quantum mechanics and neuroscience also nourishes a vivid debate beyond the borders of science, an illustrative example being journals such as Neuron Quantology or the healing method of Quantum Neurology. However, also in the scientific sphere theories of how the brain might harvest the behavior of particles on a quantum level are controversially debated. The fusion of biology and quantum physics recently gained momentum by the discovery of signs for efficient energy transport in photosynthesis due to quantum effects. Our researcher is working consistently on these principles to integration the professional & industrial software automation for industry revolution (5.0) to develop an ecosystem to run industries by renewable energy and eliminate wastes of resources. This is a visionary part to become an assistant of nature and build a sustainable environment for our next generations.

5. Conclusion
Industrial revolution (4.0) and artificial intelligence (AI) revolution is going on. We design and invent the Professional & Industrial Software Automation (PISA) technology for emerging industrial revolution (0.5) with a combination of machine reconcile (AI) and human intelligence (HI) work together for sustainable clean environment & nature. Our technology revolution the environment problems when it will come in commercial utilization.

6. Acknowledge
This technology concept & idea based upon work done by Zumosun Soft Invention Pvt. Ltd. Engineers and Scientist.  CIN number of the company is U74999RJ2018PTC, located C-4/193, Chitrakoot Scheme, Vaishali Nagar, Jaipur, Rajasthan  (India) -302021.

Jan 5, 2019
Professional & Industry Software Automation (PISA) Emerge Technology for Industrial Revolution (5.0) in era 2030-2050
Prakash Chand Sharma , Devendra Choudhary,
        

 


Professional & Industry Software Automation (PISA) Emerge Technology for Industrial Revolution (5.0) in era 2030-2050

Prakash Chand Sharma*, Devendra Choudhary**


*Zumosun Soft Invention Pvt. Ltd.

C-4/193, Chitrakoot Scheme, Vaishali Nagar, Jaipur

Rajasthan (India)-302021


*prakash69sharma@gmail.com, **devendradedar@gmail.com

http://www.zumosun.com


Dated: - January, 30,2019



Abstract- Professional & industrial software automation (PISA) is emerge technology of industrial revolution (5.0) by the integration of computer vision, natural language processing (NLP speech recognition and human intelligence to assist and increase the degree of accuracy and efficiency environment and eliminate resources wastes. This is accomplished by developing the standards conceptual biological quantum metrics neural network  (BQMatNet) to quality that can then be used to streamline and optimize overall operation of industry revolution.


Keywords- Computer vision, Industrial revolution (5.0), NLP, PISA, (QNNM) Quantum computer, (BQMatNet).


1.Introduction

Professional & industrial software automation (PISA) technology basically idea developed by Zumosun soft invention Pvt. Ltd. company engineers and scientist with special (BQMatNet) algorithms develop to interaction the machine with data, content, photos and video contents with human intelligence (HI). PISA is an integrated designed technology to assist industry revolution (5.0) professionals, such as lawyers, auditors, financers, economist and IT consultants, as well as assist to automatic operation and manufacturing control systems with project management and resource management for client projects and utilization rate management for billable staff. This is accomplished by developing biological quantum metrics neural networks

((BQMatNet)) to quantify and qualify basic business processes that can then be used to streamline and improve those processes. Typical PISA functions include operation of industry with renewable energy, eliminate resources wastes, production management, project management and documentation, time recording, billing, reporting, labor utilization many fore features.


These features are ingrate with the human intelligence (HI) and artificial intelligence (AI) like , computer vision, quantum computing, natural language processing (NPL). These features are often integrated with, renewable energy; eliminate the resources wastes accounting, Customer Relationship Management (CRM) systems, and payroll systems in order to improve efficiency of overall operations, inventory management, production cycle management and many mores. As a result, in addition to better managing industry operation, independent contractors can prevent lost revenue and slow business cycles.

Ultimately PISA Technology suites allow users to integrate industry-appropriate biological quantum metrics neural networks (BOMatNet) with human intelligence (HI) in order to better understanding of manufacturing & services operations cycles and, in turn, improve quality, efficiency and profitability. As businesses grow, the size and complexity of their projects tend to increase as well. PISA technology will use to provide visibility into mid and large project profitability.


Those familiar with industry automation may like to think of PISA technology as an industry revolution (5.0) system for manufacturing & service organizations.


2. Biological Quantum  Metrics Neural Networks (BQMatNet)

Traditional neural networks assume vectorial inputs as the network is arranged as layers of single line of computing units called neurons. This special structure requires the non-vectorial inputs such as matrices to be converted into vectors. This process can be problematic. Firstly, the spatial information among elements of the data may be lost during vectorisation. Secondly, the solution space becomes very large which demands very special treatments to the network parameters and high computational cost. To address these issues, we propose biological quantum metrics neural networks (BQMatNet), which take matrices directly as inputs and superposition and entanglement principle of quantum-mechanical phenomena. Each neuron senses summarized under the sigma evolution process and information through bilinear mapping from lower layer units in exactly the same way as the classic feed forward quantum neural networks. Under this structure, back prorogation and gradient descent combination can be utilized to obtain network parameters efficiently. Furthermore, it can be conveniently extended for multimodal inputs. We apply (BQMatNet) to integrated database handwritten digits classification and image and video super resolution tasks to show its effectiveness. Without too much tweaking (BQMatNet) achieves comparable performance as the state-of-the-art methods in both tasks with considerably reduced complexity.


3. Artificial Quantum neural Networks

In the computational approach to quantum neural network research, our scientists try to combine artificial neural network models which are widely used in machine learning for the important task of pattern classification and recognition) with the advantages of quantum information in order to develop higher order efficient algorithms. One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism, superposition or the effects


of interference and entanglement can be used as resources. Since the technological implementation of a quantum computer neural network is still in a premature stage in our organization, such quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments and commercial utilization.

Quantum neural network research and innovation is still in its infancy, and a conglomeration of proposals and ideas of varying scope and mathematical, quantum mechanics rigor has been put forward. Most of them are based on the idea of replacing classical binary resulting in quantum neural units that can be in a superposition of the state ‘firing’ and ‘resting’.

4. Quantum Knowledge Basics Systems (QKBS)

Top learning algorithms follow the classical model of training an artificial neural network to learn the input-output function of a given training set and use classical feedback loops to update parameters of the quantum system until they converge to an optimal configuration. Adiabatic models of quantum computing have also approached learning as a parameter optimization problem. Recently there has been proposed a new post-learning strategy to allow the search for improved set of weights based on analogy with quantum effects occurring in nature. The technique, proposed in is based on the analogy of modeling a biological neuron as a semiconductor heterostructure consisting of one energetic barrier sandwiched between two energetically lower areas. The activation function of the neuron is therefore considered as a particle entering the heterostructure and interacting with the barrier. In this way auxiliary reinforcement to the classical learning process of neural networks is achieved with minimal additional computational costs.

4.1 Biological Quantum Generalizations of Neural Networks 

Our researcher, scientists and visionary leaders are working on analysis of constructing a biological quantum neuron network is to first generalize classical human intelligence (HI) and artificial intelligence (AI) neurons (by padding of ancillary bits) to reversible permutation gates and then generalizing them


further to make unitary gates. Due to the no-cloning theorem in quantum mechanics, the copying of the output before sending it to several neurons in the next layer is non-trivial. This can be replaced with a general quantum unitary acting on the output plus a dummy bit in state |0. That has the classical copying gate (CNOT) as a special case, and in that sense generalizes the classical copying operation. It can be demonstrated that in this scheme, the quantum neural networks can: (i) compress quantum states onto a minimal number of quantum bits (Qbits), creating a quantum auto-encoder, and (ii) discover quantum communication protocols such as teleportation. The general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.


4.2 Biological quantum neural networks

Our quantum neural network researchers explicitly limit their scope to a computational perspective; the field is closely connected to investigations of potential quantum effects in biological research automatic integration neural networks (BRAINN). Models of cognitive agents and memory based on quantum collectives integration have been proposed by Prakash Chand Sharma, but he also points to specific problems of limits on observation and control of these memories due to fundamental logical reasons. He has also proposed that a quantum language must be associated with biological integration quantum cloud neural networks (BIQCNN). 

The combination of quantum physics, quantum mechanics and neuroscience also nourishes a vivid debate beyond the borders of science, an illustrative example being journals such as Neuron Quantology or the healing method of Quantum Neurology. However, also in the scientific sphere theories of how the brain might harvest the behavior of particles on a quantum level are controversially debated. The fusion of biology and quantum physics recently gained momentum by the discovery of signs for efficient energy transport in photosynthesis due to quantum effects. Our researcher are working consistently on this principles to integration the professional & industrial software automation for industry revolution (5.0) to develop ecosystem to run industries by renewable energy and eliminate wastes of resources. This is a visionary part to


become an assistant of nature and build a sustainable environment for our next generations.

5. Conclusion

Industrial revolution (4.0) and artificial intelligence (AI) revolution is going on .We are design and invent the Professional & Industrial Software Automation (PISA) technology for emerge industrial revolution (0.5) with combination of machine reconcile (AI) and human intelligence (HI) work together for sustainable clean environment & nature. Our technology revolution the environment problems when it will come in commercial utilization.

 

6. Acknowledge

This technology concept & idea based upon work done by Zumosun Soft Invention Pvt. Ltd. Engineers and Scientist.  CIN number of company is U74999RJ2018PTC, located C-4/193, Chitrakoot Scheme, Vaishali Nagar, Jaipur, Rajasthan (India) -302021.

 



Reference

[1] Science Direct open research papers.

[2] Quantum Neural Network

[3] Professional & Industrial Software Automation

 [4]Artificial Quantum Neural Network

[5] Industry revolution (5.0)

Jan 5, 2019