18345154. CIRCUIT CUTTING FOR QUANTUM SIMULATION WITH RESOURCE USAGE PREDICTION simplified abstract (Dell Products L.P.)

CIRCUIT CUTTING FOR QUANTUM SIMULATION WITH RESOURCE USAGE PREDICTION

Organization Name

Dell Products L.P.

Inventor(s)

Rômulo Teixeira de Abreu Pinho of Niterói (BR)

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

CIRCUIT CUTTING FOR QUANTUM SIMULATION WITH RESOURCE USAGE PREDICTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18345154 titled 'CIRCUIT CUTTING FOR QUANTUM SIMULATION WITH RESOURCE USAGE PREDICTION

Simplified Explanation

The patent application discloses a method for cutting quantum circuits by representing solutions to a cutting problem in a tree structure and using a machine learning model to predict resource requirements and execution time for selected nodes. If the predictions exceed threshold values, the corresponding solutions are pruned from the tree structure.

• Quantum circuit cutting method:

   - Solutions represented in a tree structure
   - Machine learning model used for prediction
   - Pruning of solutions if predictions exceed thresholds

Potential Applications

The technology can be applied in quantum computing research, optimization of quantum algorithms, and resource management in quantum systems.

Problems Solved

1. Efficient cutting of quantum circuits 2. Predicting resource requirements and execution time accurately

Benefits

1. Improved efficiency in quantum circuit design 2. Better resource allocation in quantum computing systems

Potential Commercial Applications

"Optimizing Quantum Circuit Cutting for Resource Management"

Possible Prior Art

There may be prior art related to quantum circuit optimization techniques, machine learning models in quantum computing, and resource management in quantum systems.

Unanswered Questions

How does the machine learning model handle uncertainty in predictions?

The article does not delve into the specifics of how uncertainty is accounted for in the machine learning model's predictions.

What are the specific threshold values used for pruning solutions?

The exact threshold resource requirements and execution time values that trigger the pruning of solutions are not specified in the article.

Original Abstract Submitted

Cutting quantum circuits is disclosed. Solutions to a cutting problem of cutting a quantum circuit into quantum subcircuits are represented in a tree structure. Selected nodes are queried using a machine learning model to generate predicted resource requirements and/or a predicted execution time. If the prediction associated with a node fails such that the predicted resource requirements and/or execution time in a simulated quantum computing system are greater than threshold resource requirements or a threshold execution time, the corresponding solutions represented by the node and the node's children are pruned from the tree structure.