Dell Products L.P. (20240320532). APPROXIMATION OF STATE VECTOR SPARSITY FOR EFFICIENT QUANTUM CIRCUIT KNITTING simplified abstract

APPROXIMATION OF STATE VECTOR SPARSITY FOR EFFICIENT QUANTUM CIRCUIT KNITTING

Organization Name

Dell Products L.P.

Inventor(s)

[[:Category:Miguel Paredes Qui�ones of Campinas (BR)|Miguel Paredes Qui�ones of Campinas (BR)]][[Category:Miguel Paredes Qui�ones of Campinas (BR)]]

Rômulo Teixeira de Abreu Pinho of Niterói (BR)

[[:Category:Micael Veríssimo De Ara�jo of Rio de Janeiro (BR)|Micael Veríssimo De Ara�jo of Rio de Janeiro (BR)]][[Category:Micael Veríssimo De Ara�jo of Rio de Janeiro (BR)]]

João Victor Pinto of Rio de Janeiro (BR)

Alexander Eulalio Robles Robles of Valinhos (BR)

APPROXIMATION OF STATE VECTOR SPARSITY FOR EFFICIENT QUANTUM CIRCUIT KNITTING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240320532 titled 'APPROXIMATION OF STATE VECTOR SPARSITY FOR EFFICIENT QUANTUM CIRCUIT KNITTING

Simplified Explanation

A patent application focuses on approximating the sparsity of state vectors in quantum computing operations. A recurrent model is trained to predict sparsity indexes for quantum circuits, allowing for more efficient estimation of computational requirements and decision-making based on these predictions.

• Quantum computing operations
• Recurrent model trained to predict sparsity indexes
• Efficient estimation of computational requirements
• Decision-making based on predicted sparsity indexes

Key Features and Innovation

The innovation lies in the use of a recurrent model to predict sparsity indexes for quantum circuits, enabling more efficient estimation of computational requirements and informed decision-making based on these predictions.

Potential Applications

1. Quantum computing research and development 2. Optimization of computational resources in quantum operations 3. Enhanced decision-making in quantum circuit design

Problems Solved

1. Inefficient estimation of computational requirements in quantum computing 2. Lack of predictive tools for sparsity in quantum circuits

Benefits

1. Improved efficiency in quantum computing operations 2. Enhanced accuracy in estimating computational needs 3. Better decision-making based on predicted sparsity indexes

Commercial Applications

Title: Predictive Sparsity Indexes for Quantum Computing Operations This technology can be applied in quantum computing research labs, tech companies developing quantum solutions, and academic institutions focusing on quantum information science.

Prior Art

Readers interested in prior art related to this technology can explore research papers on quantum circuit optimization, sparsity prediction models in quantum computing, and advancements in quantum algorithm design.

Frequently Updated Research

Stay updated on the latest developments in quantum computing, recurrent neural networks for quantum applications, and predictive modeling in quantum information science.

Questions about Quantum Computing Operations

How can predictive sparsity indexes benefit quantum computing operations?

Predictive sparsity indexes can streamline computational resource allocation and improve decision-making in quantum circuit design, leading to more efficient quantum computing operations.

What are the potential challenges in implementing predictive sparsity indexes in quantum computing?

Challenges may include the complexity of quantum circuits, the accuracy of sparsity predictions, and the integration of predictive models into existing quantum computing frameworks.

Original Abstract Submitted

approximating state vector sparsity for quantum computing operations. a recurrent model is trained to predict sparsity indexes (sparsity vector) for a quantum circuit and its subcircuits. the computational requirements of a knitting operation can be estimated or predicted more efficiently using the predicted sparsity indexes. cutting operations and decisions can also be based on the predicted sparsity indexes.