20240013080. SIZING FOR QUANTUM SIMULATION simplified abstract (Dell Products L.P.)
Contents
SIZING FOR QUANTUM SIMULATION
Organization Name
Inventor(s)
Rômulo Teixeira de Abreu Pinho of Niteroi (BR)
Benjamin E. Santaus of Somerville MA (US)
Brendan Burns Healy of Whitefish Bay WI (US)
John Richelieu Boisseau of Austin TX (US)
SIZING FOR QUANTUM SIMULATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240013080 titled 'SIZING FOR QUANTUM SIMULATION
Simplified Explanation
The abstract of this patent application describes a method for simulating a quantum algorithm using classical computing resources. The method involves receiving parameter values for the simulation, deriving quantum attributes from these values, generating a prediction of the required classical computing resources based on the quantum attributes, and configuring a classical computing infrastructure accordingly.
- The method receives parameter values for a quantum algorithm simulation.
- Quantum attributes are derived from the parameter values.
- Based on the quantum attributes, a prediction of the required classical computing resources is generated.
- The prediction is then used to size and configure a classical computing infrastructure.
- The classical computing infrastructure is designed to efficiently support the simulation of the quantum algorithm.
Potential applications of this technology:
- Quantum algorithm simulation: This method can be used to efficiently simulate quantum algorithms using classical computing resources, allowing researchers to study and analyze the behavior of quantum algorithms without the need for actual quantum computers.
- Algorithm optimization: By predicting the required classical computing resources for a quantum algorithm, this method can help in optimizing the algorithm's performance and reducing computational costs.
- Quantum algorithm development: The ability to simulate quantum algorithms using classical computing resources can aid in the development and refinement of new quantum algorithms.
Problems solved by this technology:
- Lack of quantum computing resources: Quantum computers are still in their early stages of development and are not widely available. This method allows researchers to simulate quantum algorithms using classical computing resources, overcoming the limitations of access to quantum computers.
- Computational efficiency: By predicting the required classical computing resources, this method ensures that the classical computing infrastructure is appropriately sized and configured, resulting in computationally efficient simulations of quantum algorithms.
Benefits of this technology:
- Cost efficiency: Simulating quantum algorithms using classical computing resources can be more cost-effective than using actual quantum computers, which are expensive and limited in availability.
- Flexibility: The method allows researchers to easily modify and experiment with different parameter values for the simulation, enabling them to explore various scenarios and optimize the algorithm's performance.
- Scalability: The classical computing infrastructure can be scaled up or down based on the predicted resource requirements, allowing for simulations of different sizes and complexities.
Original Abstract Submitted
one example method includes receiving parameter values relating to execution of a simulation of a quantum algorithm, deriving quantum attributes from the parameter values, generating, based on the quantum attributes, a classical computing resource prediction, and translating the classical computing resource prediction into elements of a classical computing infrastructure. the classical computing infrastructure may be sized and configured to support computationally efficient, and cost efficient, execution of the simulation of the quantum algorithm.
