DESC:4. DESCRIPTION
Technical Field of the Invention

The present invention relates to an intelligent system for quantum computing and data processing. More particularly offering quantum data encoding templates, hardware recommendations, and error mitigation techniques.

Background of the Invention

In the rapidly evolving technological landscape, quantum computing has emerged as a groundbreaking frontier, heralding a new era of data processing and problem-solving capabilities. This advanced computing paradigm harnesses the principles of quantum mechanics to process and analyze information in ways that are fundamentally different from traditional computing systems. Quantum computers, utilizing the quantum bit or "qubit" as the basic unit of information, can perform complex calculations at speeds unattainable by classical computers, offering promising solutions to problems in optimization, cryptography, machine learning, and beyond.

However, the transition from classical to quantum computing presents significant challenges, particularly in the realm of data processing. Conventional data, whether numerical, textual, or visual, must be encoded into quantum states—a process that is far from straightforward and requires specialized knowledge of quantum mechanics and computing. This encoding challenge is compounded by the diverse nature of data, especially in applications involving multi-modal data processing, which integrates various data types like images, text, and time-series information.

The gap between the capabilities of quantum computing and the practical application of these technologies in real-world scenarios has created a pressing need for innovative solutions. Current approaches to quantum computing often demand a deep understanding of both quantum mechanics and the specific problem domain, creating barriers to entry for researchers, developers, and organizations eager to explore the potential of quantum computing.

Addressing this gap, inventors, proposes an "Intelligent Recommender System for Quantum Computations." This invention aims to democratize access to quantum computing by simplifying the process of leveraging quantum technologies for data processing and analysis. By integrating advanced machine learning techniques, the system classifies data across various modalities and provides tailored quantum data encoding templates, making quantum computing more accessible and applicable across a broad spectrum of industries and research fields.

The inventors behind this system recognize the transformative potential of quantum computing and are driven by the mission to bridge the divide between classical data analysis and quantum computation. Their approach combines sophisticated machine learning algorithms with an intuitive understanding of quantum computing processes, offering a user-friendly platform that empowers users to harness the power of quantum computing without the need for deep expertise in the field.

This intelligent system not only addresses the technical challenges of quantum data encoding but also provides recommendations for quantum hardware, error correction strategies, and noise mitigation techniques, further enhancing the reliability and applicability of quantum computing solutions. By offering a comprehensive suite of tools and resources, the system paves the way for a new era of data processing, where quantum computing becomes a practical and accessible tool for solving complex problems across various domains.

In brief, the "Intelligent Recommender System for Quantum Computations" by inventors represents a significant leap forward in making quantum computing accessible to a wider audience. By addressing the challenges of quantum data encoding, hardware selection, and error mitigation, the inventors are at the forefront of a movement to democratize quantum computing, opening up new avenues for innovation and problem-solving in the digital age.

Brief Summary of the Invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

It is a primary object of the present invention to make quantum computing accessible and comprehensible to a broader audience of researchers, developers, and organizations worldwide.

It is yet another object of the present invention to develop an intelligent system that simplifies the process of utilizing quantum computing aims to democratize its vast potential and remove barriers to entry in the field.
It is yet another object of the present invention to enhance data processing capabilities and problem-solving efficiency by leveraging quantum computing.

These ambitious endeavours seeks not only to unlock the immense potential of quantum computing for a broader audience but also to significantly enhance data processing capabilities and problem-solving efficiency through the power of quantum mechanics.

The system unfolds its capabilities through a meticulously designed architecture comprising several integral modules: the data classification module, the quantum data encoding templates module, the quantum applications mapping module, the quantum hardware services recommendations module, the error correction and noise mitigation plan module, the hybrid quantum-classical computing plan module, the quantum resource estimation module, the quantum algorithm benchmarking module, and the interface and documentation module. Each of these components plays a pivotal role in democratizing quantum computing and bridging the gap between classical data analysis and quantum computation.

A standout feature of the system is its provision of quantum data encoding templates tailored to diverse data modalities, including numerical, image, text, and time series data. This innovation ensures that users can seamlessly transform classical data into quantum-compatible formats, paving the way for the utilization of quantum algorithms across various applications.

Furthermore, the system's intelligent recommendation of suitable quantum hardware services is based on a comprehensive evaluation of factors such as qubit coherence times, error rates, and accessibility. This ensures that users are guided towards the optimal quantum computing resources for their specific needs.

The incorporation of quantum error correction and noise mitigation techniques within the system is a testament to its commitment to enhancing the reliability and accuracy of quantum computations. By addressing the inherent challenges posed by environmental noise and imperfections in quantum hardware, the system ensures that users can rely on the results of their quantum computations.

Another significant aspect of the system is its ability to map quantum algorithms to appropriate data types, thereby enabling effective problem-solving in critical areas such as optimization, cryptography, and machine learning. This alignment of algorithms with data types is crucial for harnessing the full potential of quantum computing in addressing real-world challenges.

The advantages of this intelligent recommender system are manifold. By simplifying the process of engaging with quantum computing, the system removes significant barriers to entry, allowing users to explore and exploit quantum computing's vast potential without the need for deep expertise in quantum mechanics. This democratization of quantum computing is poised to accelerate innovation and problem-solving across a wide range of disciplines.

Moreover, the system's comprehensive approach, from data classification to quantum hardware recommendations and error mitigation, ensures that users are supported throughout their quantum computing journey. The integration of machine learning techniques further enhances the system's ability to provide personalized recommendations and solutions, tailored to the unique requirements of each user.

The applications of the "Intelligent Recommender System for Quantum Computations" are as diverse as the field of quantum computing itself. From optimizing complex logistical operations and cracking cryptographic codes to advancing machine learning algorithms and exploring new frontiers in drug discovery, the potential use cases are vast. By providing an accessible gateway to the power of quantum computing, the system opens up new possibilities for innovation and problem-solving across industries, including finance, healthcare, cybersecurity, and beyond.

In essence, the "Intelligent Recommender System for Quantum Computations" represents a significant leap forward in the democratization of quantum computing. By lowering the barriers to entry and providing a comprehensive suite of tools and resources, the system empowers users to harness the transformative potential of quantum computing, driving forward innovation and problem-solving in the digital age.

Further scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, the detailed description and specific examples, while indicating preferred embodiments of the invention, will be given by way of illustration along with complete specification.

Brief Summary of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1 illustrates a flow diagram having components involved in the system, in accordance with an exemplary embodiment of the present invention;

Fig. 2 illustrates a block diagram illustrating the execution plan, in accordance with an exemplary embodiment of the present invention;

Fig. 3 illustrates a block diagram of sample workflow related to big data processing, in accordance with an exemplary embodiment of the present invention;

Fig. 4 and Fig. 5 illustrates a block diagram of sample workflow related to hybrid processing, in accordance with an exemplary embodiment of the present invention.

Detailed Description of the Invention

The present disclosure emphasises that its application is not restricted to specific details of construction and component arrangement, as illustrated in the drawings. It is adaptable to various embodiments and implementations. The phraseology and terminology used should be regarded for descriptive purposes, not as limitations.

The terms "including," "comprising," or "having" and variations thereof are meant to encompass listed items and their equivalents, as well as additional items. The terms "a" and "an" do not denote quantity limitations but signify the presence of at least one of the referenced items. Terms like "first," "second," and "third" are used to distinguish elements without implying order, quantity, or importance.

According to the exemplary embodiment of the present invention, the system comprises data classification module, quantum data encoding templates module, quantum applications mapping module, quantum hardware services recommendations module, error correction and noise mitigation plan module, hybrid quantum-classical computing plan module, quantum resource estimation module, quantum algorithm benchmarking module, Interface and documentation module.

In accordance with the exemplary embodiment of the present invention, wherein the data classification module employing advanced machine learning techniques, notably deep neural networks, to classify diverse data types such as numerical, image, text, and time series data. This initial step is pivotal in setting the stage for quantum data processing.

In accordance with the exemplary embodiment of the present invention, wherein the quantum data encoding templates module takes center stage by generating customized templates tailored to each specific data modality. Leveraging quantum techniques like amplitude encoding and the quantum Fourier transform, this module transforms classical data into quantum states represented by qubits. This transformative process unlocks the ability to employ quantum algorithms across diverse applications, ranging from event detection to sentiment analysis.

In accordance with the exemplary embodiment of the present invention, the quantum applications mapping module plays a strategic role by mapping quantum algorithms to appropriate data types. This ensures effective problem-solving in areas such as optimization, cryptography, and machine learning. This module's intelligence aligns specific quantum algorithms, such as Grover’s search or Shor's factoring, with data types, optimizing the use of quantum computing for real-world challenges.

In accordance with the exemplary embodiment of the present invention, ensuring the seamless execution of quantum algorithms, the quantum hardware services recommendations module evaluates and recommends quantum hardware platforms based on factors like qubit coherence times, error rates, and accessibility. This strategic guidance ensures that users can harness the full potential of quantum computing while navigating the nuances of available quantum hardware services.

In accordance with the exemplary embodiment of the present invention, error correction and noise mitigation are addressed through the dedicated module, incorporating essential techniques such as quantum error correction codes and noise mitigation algorithms. This ensures the reliability and accuracy of quantum computations, mitigating the disruptive effects caused by environmental noise and imperfections in quantum hardware.

In accordance with the exemplary embodiment of the present invention, in recognition of the current noisy intermediate-scale quantum (NISQ) era, the hybrid quantum-classical computing plan module strategically suggests combining classical and quantum paradigms. By integrating variational quantum algorithms, quantum-classical optimization, and quantum neural networks, this module maximizes computational efficiency.

In accordance with the exemplary embodiment of the present invention, quantum resource estimation and benchmarking modules provide crucial insights. The former offers estimate of quantum resources required for executing suggested quantum algorithms on available quantum processors. The latter evaluates the performance and scalability of suggested quantum algorithms on different hardware platforms, aiding users in selecting the most suitable algorithm-hardware combination.

In accordance with the exemplary embodiment of the present invention, the user interface and documentation module designed with user-friendliness in mind, ensures easy interaction and comprehensive guidance. From data submission to algorithm selection and hardware execution, the interface simplifies the complexities of quantum computing. Detailed documentation, tutorials, and troubleshooting guides further democratize access to the vast potential of quantum computing.

List of the reference numerals and their corresponding descriptions:
102: Data Classification Module - Processes and classifies various data types using machine learning techniques;
104: Quantum Data Encoding Templates Module - Generates quantum data encoding templates employing techniques like amplitude encoding and quantum Fourier transform;
106: Quantum Applications Mapping Module - Aligns quantum algorithms with appropriate data types for problem-solving in optimization, cryptography, and machine learning;
108: Quantum Hardware Services Recommendations Module - Evaluates and recommends quantum hardware platforms considering factors like qubit coherence times and error rates;
110: Error Correction and Noise Mitigation Plan Module - Implements techniques for quantum error correction and noise mitigation to enhance computational accuracy and reliability;
112: Hybrid Quantum-Classical Computing Plan Module - Suggests an optimized combination of classical and quantum computing paradigms;
114: Quantum Resource Estimation Module - Offers insights into the quantum resources needed for executing algorithms on quantum processors;
116: Quantum Algorithm Benchmarking Module - Assesses the performance and scalability of quantum algorithms on different hardware;
118: Interface and Documentation Module - Provides an accessible interface and comprehensive documentation for system users.

Referring to figures now,
Figure 1 depicts the comprehensive architecture of the System, illustrating a phased approach towards quantum computing implementation. At the forefront of this system is the Data Classification Module (102), leveraging deep neural networks to process and categorize diverse data types. This initial step lays the foundation for subsequent processing stages by accurately classifying data. Following successful classification, the Quantum Data Encoding Templates Module (104) comes into play, utilizing advanced quantum techniques such as amplitude encoding and quantum Fourier transform to generate tailored templates. These templates enable the transformation of classical data into quantum states, a pivotal process for quantum computation.

Continuing the progression, the Quantum Applications Mapping Module (106) strategically aligns quantum algorithms with specific data types, ensuring efficient problem-solving in areas such as optimization, cryptography, and machine learning. Simultaneously, the Quantum Hardware Services Recommendations Module (108) evaluates and recommends quantum hardware platforms, considering critical factors like qubit coherence times and error rates. This ensures that the chosen hardware optimally supports the execution of quantum algorithms, enhancing computational performance.

As the system advances, the Error Correction and Noise Mitigation Plan Module (110) becomes instrumental in enhancing the reliability and accuracy of computations. This module implements techniques for quantum error correction and noise mitigation, addressing inherent challenges associated with quantum computing systems. In acknowledgment of the NISQ era, characterized by Noisy Intermediate-Scale Quantum computers, the Hybrid Quantum-Classical Computing Plan Module (112) suggests an optimized combination of classical and quantum computing paradigms. This approach maximizes computational efficiency while leveraging the strengths of both computing models.

Moreover, the Quantum Resource Estimation Module (114) offers insights into the quantum resources required for executing algorithms on quantum processors. This critical information aids in resource allocation and planning for efficient utilization of quantum resources. Additionally, the Quantum Algorithm Benchmarking Module (116) assesses the performance and scalability of quantum algorithms on different hardware platforms. This evaluation facilitates informed decision-making regarding algorithm selection and hardware optimization.

Completing the architecture is the Interface and Documentation Module (118), which provides an accessible interface and comprehensive documentation for system users. This module ensures user-friendly interaction with the system and facilitates efficient utilization of its functionalities. Together, these components form a robust execution plan that democratizes the potential of quantum computing across various applications.

One significant advantage of the system is its comprehensive approach to quantum computing implementation. By encompassing modules dedicated to data classification, quantum data encoding, hardware recommendations, error correction, and hybrid computing strategies, the system addresses key challenges associated with quantum computing adoption.

Another advantage lies in the strategic alignment of quantum algorithms with specific data types facilitated by the Quantum Applications Mapping Module. This ensures that quantum computing resources are utilized efficiently for problem-solving in diverse fields such as optimization, cryptography, and machine learning.

Furthermore, the incorporation of error correction and noise mitigation techniques enhances the reliability and accuracy of computations, crucial for real-world applications where precision is paramount. The system's ability to recommend suitable quantum hardware platforms based on factors like qubit coherence times and error rates further optimizes computational performance, contributing to overall efficiency.

The Hybrid Quantum-Classical Computing Plan Module offers a flexible approach that acknowledges the limitations of current quantum hardware while leveraging classical computing resources to enhance computational efficiency. This hybrid approach ensures that computational tasks are executed optimally, taking advantage of the strengths of both classical and quantum computing paradigms.

The system finds applications across various domains where quantum computing holds promise for transformative solutions. In the field of optimization, the system can be utilized for tasks such as portfolio optimization, supply chain management, and logistics optimization, where quantum algorithms offer the potential for significant efficiency gains.

In cryptography, the system's ability to align quantum algorithms with specific data types enables the development of robust encryption and decryption techniques resistant to quantum attacks. This has implications for securing sensitive data in fields such as finance, healthcare, and national security.

Moreover, the system's capabilities in machine learning enable advancements in areas such as pattern recognition, natural language processing, and drug discovery. Quantum algorithms optimized for specific data types can accelerate the training and inference processes, leading to more accurate predictions and insights.

Additionally, the system can be applied in scientific research for tasks such as simulating complex quantum systems, protein folding analysis, and molecular dynamics simulations. The system's comprehensive architecture and resource estimation capabilities make it a valuable tool for researchers seeking to leverage quantum computing for scientific discovery.

Overall, the system's versatility and comprehensive approach make it suitable for a wide range of applications, empowering researchers, engineers, and businesses to harness the potential of quantum computing for solving complex problems and driving innovation.

Test results:

Results on comparison of different algorithms using hybrid approach for image classification:

Quantum operations are carried out on a designated device, utilizing a quantum circuit from IBM QX. This circuit, implemented with basic gates of ibm_santiago, processes pixel values [155, 147, 65, 90]. All images undergo grayscale conversion, followed by data augmentation employing both quantum data augmentation (QDA) and classical image augmentation techniques across all synthesized datasets. Classical augmentation methods include CDA1, involving image rotation by 90°, and CDA2, introducing salt and pepper noise by randomly altering pixels to white and black. Table presents a comparative analysis of training and validation accuracies for all models, revealing notable differences between classical and quantum augmentation approaches. For instance, the VCNN's training and validation accuracies for 21 classes display disparities of 19.33% and 15.02%, respectively, using CDA1 and CDA2, whereas QDA shows a smaller difference of 9.27%. Moreover, QDA achieves a higher training accuracy of 95.56% compared to CDA1 and CDA2. Consequently, the proposed data augmentation technique facilitates better model generalization. Additionally, test results demonstrate that the HQCNN model outperforms other models, achieving an overall accuracy of 85.28% in classifying all 21 classes in the UC Merced Land-Use dataset. Furthermore, the proposed augmentation technique exhibits superior accuracy compared to traditional classical image augmentation methods, underscoring its efficacy in improving model performance.
,CLAIMS:5. CLAIMS
We Claim
1. A system for quantum encoding template recommendations, comprising:
a cloud-based service to provide remote accessibility to quantum hardware resources for users;
a data classification module (102), a quantum data encoding templates module (104), a quantum application mapping module (106), a quantum hardware services recommendations module (108), an error correction and noise mitigation plan module (110), a hybrid quantum-classical computing plan module (112), a quantum resource estimation module (114), a quantum algorithm benchmarking module (116), and an interface and documentation module (118);
the data classification module (102) is configured to utilize machine learning techniques to classify diverse data types, including numerical, image, text, and time series data;
the data classification module preprocesses data and extracts important features to facilitate accurate classification;
Characterized in that,
the quantum data encoding templates module (104) is configured to generate tailored quantum data encoding templates for different data modalities, wherein the templates enable the transformation of classical data into quantum states represented by qubits, leveraging techniques such as amplitude encoding and quantum Fourier transform;
the quantum applications mapping module (106) is configured to map quantum algorithms to appropriate data types, facilitating effective problem-solving in areas such as optimization, cryptography, and machine learning;
the quantum hardware services recommendations module (108) is configured to evaluate and recommend quantum hardware platforms based on factors including qubit coherence times, error rates, and accessibility;
the error correction and noise mitigation plan module (110) is configured to enhance the reliability and accuracy of quantum computations;
the hybrid quantum-classical computing plan module (112) is configured to suggest a combination of classical and quantum computing paradigms to optimize computational efficiency;
the quantum resource estimation module (114) is configured to provide insights into the quantum resources required for executing suggested quantum algorithms on available quantum processors;
the quantum algorithm benchmarking module (116) is configured to evaluate the performance and scalability of suggested quantum algorithms on different hardware platforms; and
the interface and documentation module (118) is configured to facilitate easy interaction and comprehensive guidance for utilizing the system capabilities.

2. The system as claimed in claim 1, wherein the quantum applications mapping module (106) ensures precise alignment between the data's requirements and the algorithm's capabilities.

3. The system as claimed in claim 1, wherein the error correction and noise mitigation plan module (108) implement techniques such as quantum error correction codes and noise mitigation algorithms to address disruptive effects caused by environmental noise and imperfections in quantum hardware.

4. The system as claimed in claim 1, wherein the quantum resource estimation module (114) estimates factors such as the number of qubits, quantum circuit depth, and entangling gate count, aiding in efficient resource allocation.

5. The system as claimed in claim 1, wherein the interface and documentation module (118) ensure user-friendly interaction for the system to provide detailed documentation, tutorials, and troubleshooting guides for effective utilization.

6. The system as claimed in claim 1, wherein the hybrid quantum-classical computing plan module (112) maximizes computational power to the system by integrating classical preprocessing with quantum processing, especially in the noisy intermediate-scale quantum (NISQ) era.

7. The system as claimed in claim 1, wherein the quantum algorithm benchmarking module (116) assists in selecting the most suitable algorithm-hardware combination to the system by comparing execution times, error rates, and scalability across platforms.

8. The system as claimed in claim 1, wherein the data classification module (102) utilizes deep neural networks for accurate classification of diverse data modalities.

9. The system as claimed in claim 1, wherein the quantum data encoding templates module (104) employs amplitude encoding, quantum Fourier transform, and quantum embeddings as data encoding techniques for quantum state representation.

10. The system as claimed in claim 1, wherein the quantum applications mapping module (106) aligns Grover’s search, Shor's factoring, variational quantum algorithms, and quantum support vector machines with specific data types for solving optimization, cryptography, natural language processing, and image recognition problems.

11. The system as claimed in claim 1, wherein the quantum hardware services recommendations module (108) evaluates superconducting qubit-based platforms, trapped ion quantum processors, photonic chips, and topological qubit-based systems for seamless execution of quantum algorithms.

12. The system as claimed in claim 1, wherein the quantum error correction and noise mitigation plan module (110) incorporate quantum error correction codes and quantum error mitigation algorithms to enhance quantum computation reliability.

13. The system as claimed in claim 1, wherein the hybrid quantum-classical computing plan module (112) integrates variational quantum algorithms, quantum-classical optimization, and quantum neural networks to maximize computational efficiency in the noisy intermediate-scale quantum (NISQ) era.

14. A method for recommending quantum encoding templates, comprising:
providing a cloud-based service to enable remote accessibility to quantum hardware resources for users;
utilizing a data classification module (102) to classify diverse data types, including numerical, image, text, and time series data, using machine learning techniques, wherein the data is preprocessed, and important features are extracted to facilitate accurate classification;
generating tailored quantum data encoding templates for different data modalities using a quantum data encoding templates module (104), wherein techniques such as amplitude encoding and quantum Fourier transform are employed to transform classical data into quantum states represented by qubits;
mapping quantum algorithms to appropriate data types using a quantum applications mapping module (106), thereby facilitating effective problem solving in areas such as optimization, cryptography, and machine learning;
evaluating and recommending quantum hardware platforms based on factors including qubit coherence times, error rates, and accessibility using a quantum hardware services recommendation module (108);
enhancing the reliability and accuracy of quantum computations through implementation of techniques such as quantum error correction codes and noise mitigation algorithms using an error correction and noise mitigation plan module (110);
suggesting a combination of classical and quantum computing paradigms to optimize computational efficiency using a hybrid quantum-classical computing plan module (112);
providing insights into the quantum resources required for executing suggested quantum algorithms on available quantum processors using a quantum resource estimation module (114);
evaluating the performance and scalability of suggested quantum algorithms on different hardware platforms using a quantum algorithm benchmarking module (116); and
facilitating easy interaction and comprehensive guidance for utilizing the system capabilities using an interface and documentation module (118).

6. DATE AND SIGNATURE

Dated this on 5st April, 2024
Signature

Mr. Srinivas Maddipati
(IN/PA 3124)
Agent for applicant