18345505. ESTIMATING SIGNAL FROM NOISE IN QUANTUM MEASUREMENTS simplified abstract (Dell Products L.P.)
Contents
- 1 ESTIMATING SIGNAL FROM NOISE IN QUANTUM MEASUREMENTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ESTIMATING SIGNAL FROM NOISE IN QUANTUM MEASUREMENTS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
ESTIMATING SIGNAL FROM NOISE IN QUANTUM MEASUREMENTS
Organization Name
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)
ESTIMATING SIGNAL FROM NOISE IN QUANTUM MEASUREMENTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18345505 titled 'ESTIMATING SIGNAL FROM NOISE IN QUANTUM MEASUREMENTS
Simplified Explanation
The patent application describes a method for generating clean signals from the execution of quantum circuits by iteratively processing noisy outputs using a machine learning model.
- The method involves executing a quantum circuit a limited number of times (k times) to obtain a noisy output.
- The noisy output is then processed by a machine learning model to gradually separate clean or usable output from the noisy output.
- This allows for a reliable output to be determined from fewer circuit executions.
Potential Applications
This technology could be applied in quantum computing, quantum error correction, and quantum information processing.
Problems Solved
This technology addresses the challenge of obtaining clean signals from noisy quantum circuit executions, which is crucial for the reliability and accuracy of quantum computing systems.
Benefits
The method allows for the extraction of reliable outputs from a limited number of quantum circuit executions, reducing the computational resources required for quantum computing tasks.
Potential Commercial Applications
This technology could be valuable in industries utilizing quantum computing, such as cryptography, drug discovery, and optimization problems.
Possible Prior Art
Prior art in quantum error correction and noise mitigation techniques in quantum computing may exist, but specific examples are not provided in the abstract.
Unanswered Questions
How does the machine learning model differentiate between clean and noisy outputs?
The abstract does not detail the specific mechanisms or algorithms used by the machine learning model to separate clean signals from noisy outputs.
What is the impact of using fewer circuit executions on the overall performance of quantum computing tasks?
The abstract does not discuss the potential trade-offs or limitations of relying on a limited number of circuit executions for generating clean signals.
Original Abstract Submitted
Generating clean signals from the execution of quantum circuits is disclosed. A quantum circuit may be executed a number of times (k times) that is less than a specified number of times. The noisy output after k executions is iteratively processed by a machine learning model that is configured to gradually separate a clean or usable output from the noisy output. This allows a reliable output to be determined from fewer circuit executions.
