Neural Network Quantum Readout Error Mitigation for NISQ Systems
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Solution Overview
Problem
Existing quantum computers face challenges in maintaining quantum superposition states due to external environmental changes, leading to accumulated errors in quantum readout processes, which are difficult to correct without increasing the number of qubits and gates, limiting their practical application.
Innovation Solution
A neural network-based approach is employed to mitigate quantum readout errors by training on actual and ideal measurement results, using a deep learning model to infer ideal measurement outcomes from noisy data, thereby correcting non-linear noise without additional quantum resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum error correction is used to protect quantum superposition state, then reliability is improved, but device complexity increases due to requiring more qubits
Solution Approach 1:
A neural network model is introduced as an intermediary between the quantum circuit and the measurement results. The neural network learns the mapping between ideal measurement results and actual noisy measurement results, enabling error mitigation without adding quantum resources. The neural network acts as a classical mediator that processes quantum output data to remove errors.
Solution Approach 2:
The patent replaces the quantum mechanical approach of using additional qubits for error correction with a classical computational approach using neural networks. Instead of physically adding more qubits to protect the quantum state, the system uses classical machine learning algorithms to correct readout errors in software, substituting quantum hardware solutions with classical software-based correction.
2Measurement precision
If more qubits and gates are added to correct quantum errors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The neural network serves as an intermediary that processes measurement data to improve precision without adding quantum gates. It learns the error characteristics from training data and applies corrections to measurement results, achieving higher precision through classical computation rather than quantum circuit extension.
Solution Approach 2:
The system changes the approach from modifying quantum circuit parameters (adding gates and qubits) to changing classical processing parameters. The neural network adjusts its internal parameters (weights and biases) during training to optimize error correction, achieving improved measurement precision through parameter optimization in the classical domain rather than quantum domain.
3Productivity
If quantum operations are performed for extended duration, then productivity is improved, but reliability deteriorates due to accumulated errors
Solution Approach 1:
The neural network is trained in advance using training data generated from quantum circuits with known error characteristics. This preliminary training allows the system to be prepared with correction knowledge before actual quantum operations are performed, enabling error mitigation to scale with operation duration without degrading reliability.
Data Source
AI summary
The present disclosure relates to a quantum computer technology and a method in which a learning apparatus a neural network for quantum readout acquires a plurality of actual measurement results including noise in quantum readout using a quantum circuit, acquires an ideal measurement result of the quantum circuit in correspondence to each of the plurality of actual measurement results including noise, creates training data from a set of the actual measurement results including noise and the ideal measurement results, and trains a neural network for mitigating errors, which are generated in quantum readout, using the created training data.