Diffusion Model for Quantum Signal Estimation
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Solution Overview
Problem
Quantum computing systems face challenges in separating signal from noise due to the probabilistic and non-deterministic nature of quantum computations, requiring a large number of circuit executions to obtain reliable results, with the unknown number of executions being costly and inefficient.
Innovation Solution
A machine learning model, specifically a diffusion model, is used to estimate the probability distribution of quantum circuit outputs after fewer executions by training on characteristics of the quantum circuit and the computing system, reducing the need for extensive shot executions and refining noisy outputs into clean signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum circuits are executed many times to mitigate noise effects, then measurement reliability is improved, but execution cost and time increase
Solution Approach 1:
The patent applies preliminary action by training a machine learning model in advance to learn the noise characteristics of the quantum computing system. This pre-trained model can then quickly estimate clean probability distributions from limited measurement data without requiring extensive circuit executions at runtime, thus resolving the contradiction between reliability and execution time.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the noisy quantum measurements and the final clean probability distribution. This model acts as a mediator that processes the noisy data and outputs reliable results, eliminating the need for numerous circuit executions while maintaining measurement reliability.
2Measurement precision
If quantum circuits are executed many times to separate signal from noise, then signal accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent replaces the mechanical approach of repeatedly executing quantum circuits with a computational approach using a machine learning model. Instead of physically running the quantum circuit many times to average out noise, the system uses the pre-trained model to computationally infer the clean signal from limited measurements, significantly reducing resource consumption while maintaining signal accuracy.
Solution Approach 2:
The patent creates a computational copy or representation of the noise characteristics through the machine learning model during training. This learned representation allows the system to simulate and correct for noise effects without physically executing the quantum circuit numerous times, thereby improving signal accuracy while reducing resource consumption.
3Reliability
If the number of executions is increased to obtain reliable results, then result quality is improved, but cost increases
Solution Approach 1:
The patent performs preliminary training of the machine learning model to capture noise patterns in advance. This upfront investment allows the system to subsequently generate high-quality results with minimal circuit executions, directly addressing the contradiction between result quality and cost by shifting computational effort from runtime executions to offline training.
Solution Approach 2:
The machine learning model serves as an intermediary that decouples the relationship between execution count and result quality. By inserting this model between the quantum measurements and final results, the system can achieve high-quality outputs with fewer executions, thereby reducing the cost associated with numerous circuit runs.
Data Source
AI summary
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.