Tensor Network Quantum Machine Learning for NISQ Error Mitigation
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Solution Overview
Problem
Noisy intermediate-scale quantum (NISQ) computing devices have high error rates, limiting their utility for quantum algorithms that require low noise levels, necessitating improvements in reducing noise and enhancing the reliability of quantum operations.
Innovation Solution
The implementation of quantum-assisted machine learning methods using tensor networks (TNs) to encode classical data into quantum states, train models, and compile them into optimized quantum circuits for operation on NISQ devices, reducing the number of operations and error rates by leveraging tensor network structures and greedy compilation heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum algorithms are executed on NISQ devices, then quantum computing functionality is achieved, but high error rates significantly reduce reliability
Solution Approach 1:
The patent applies error mitigation techniques that convert the harmful effect of noise into beneficial outcomes by using classical post-processing methods to correct quantum measurement results. The system collects multiple measurement shots, applies classical error mitigation algorithms, and reconstructs accurate probability distributions despite noisy quantum hardware, effectively turning noise-induced errors into correctable deviations.
Solution Approach 2:
The patent introduces classical computing as an intermediary between quantum computation and final results. A classical processor receives quantum measurement data, applies error mitigation algorithms, and produces corrected outputs. This intermediary classical system filters out noise effects while preserving genuine quantum computational advantages.
2Productivity
If complex quantum algorithms are implemented, then computational power increases, but the number of quantum operations increases leading to higher error accumulation
Solution Approach 1:
The patent segments quantum algorithms into smaller, manageable circuit components that can be executed independently on NISQ devices. By breaking down complex algorithms into modular quantum circuits with fewer gates each, the system reduces error accumulation in individual executions while maintaining overall computational power through multiple runs and classical post-processing aggregation.
3Reliability
If quantum circuits are optimized for NISQ devices, then error rates are reduced, but the number of quantum operations increases program length
Solution Approach 1:
The patent uses copying by executing the same quantum circuit multiple times (multiple shots) rather than creating a single long circuit. Each copy of the circuit is short and optimized for low error rates, while the collective set of copies provides sufficient statistical data for accurate results through classical aggregation and error mitigation.
Data Source
AI summary
A method for quantum-assisted machine learning includes encoding, by processing circuitry, classical data into a plurality of quantum states by applying the classical data to an encoding map, and training a quantum model based on the plurality of quantum states. The quantum model may have a tensor network structure. The method may also include compiling, by the processing circuitry, the quantum model into a quantum circuit by mapping virtual qubits onto hardware qubits of a quantum hardware device, the quantum circuit including a sequence of operations tailored for operation on the quantum hardware device.