20240013081. METHOD AND SYSTEM FOR RECOMMENDING OPTIMUM COMBINATION OF QUANTUM CIRCUITS simplified abstract (Tata Consultancy Services Limited)

METHOD AND SYSTEM FOR RECOMMENDING OPTIMUM COMBINATION OF QUANTUM CIRCUITS

Organization Name

Tata Consultancy Services Limited

Inventor(s)

Aniket Nandkishor Kulkarni of Pune (IN)

Sukesh Kumar Ranjan of Pune (IN)

Pathai Viswanathan Venkateswaran of Bangalore (IN)

Mariswamy Girish Chandra of Banglore (IN)

Pranav Champaklal Shah of Thane West (IN)

Sayantan Pramanik of Bangalore (IN)

Chundi Venkata Sridhar of Hyderabad (IN)

Vishnu Vaidya of Bangalore (IN)

Vidyut Vaman Navelkar of Mumbai (IN)

Sudhakara Deva Poojary of Thane West (IN)

Mayank Baranwal of Thane West (IN)

METHOD AND SYSTEM FOR RECOMMENDING OPTIMUM COMBINATION OF QUANTUM CIRCUITS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240013081 titled 'METHOD AND SYSTEM FOR RECOMMENDING OPTIMUM COMBINATION OF QUANTUM CIRCUITS

Simplified Explanation

The disclosed method and system aim to recommend the optimum combination of quantum circuits. Traditional approaches for this task are experimentation-based and require manual efforts, making the process cumbersome, effort-intensive, and iterative.

The approach presented in this patent application involves the following steps:

1. High-level combinations of experiments are initially generated. 2. These combinations are prioritized using a graph-based approach. 3. The prioritized combinations form the training data. 4. A graph neural network (GNN) data model is generated using the training data. 5. The GNN data model is utilized to recommend the optimum combination of quantum circuits.

Potential applications of this technology:

• Quantum computing research: The recommended optimum combination of quantum circuits can aid researchers in optimizing their experiments and achieving better results.
• Quantum algorithm development: By suggesting the best combination of quantum circuits, this technology can assist in the development of efficient quantum algorithms.
• Quantum hardware design: The recommended combinations can be used to optimize the design and configuration of quantum hardware systems.

Problems solved by this technology:

• Manual and cumbersome process: The traditional approaches for recommending optimum combinations of quantum circuits require manual efforts and are time-consuming. This technology automates the process, saving time and effort.
• Iterative experimentation: Experimentation-based approaches often involve multiple iterations to find the best combination. The disclosed method reduces the need for iterative experimentation by providing optimized recommendations.
• Lack of prioritization: Prioritizing different combinations of experiments can be challenging. The graph-based approach used in this technology helps in prioritizing the combinations effectively.

Benefits of this technology:

• Time and resource savings: By automating the process and providing optimized recommendations, this technology saves time and reduces the need for manual efforts and iterative experimentation.
• Improved results: The recommended optimum combination of quantum circuits can lead to improved experimental outcomes, algorithm efficiency, and hardware design.
• Enhanced research capabilities: Researchers can leverage this technology to explore and optimize the potential of quantum computing, leading to advancements in various fields.

Original Abstract Submitted

traditional approaches for recommending optimum combination of quantum circuits are experimentation based approaches, and require manual efforts or are cumbersome, effort intensive and iterative processes. method and system disclosed herein generally relates to quantum experimentation, and, more particularly, for recommending optimum combination of quantum circuits. in this approach, a high-level combination of experiments are initially generated, which are further prioritized using a graph based approach, which then forms a training data. the training data is then used for generating a gnn data model, which is further used for recommending optimum combination of quantum circuits.