WiMi Hologram Cloud Inc, a leading global Hologram Augmented Reality (“AR”) Technology provider, announced the development of an innovative solution: Machine Learning-based Quantum Error Suppression Technology (MLQES). This technology not only breaks through the error bottleneck in quantum computing but also demonstrates the potential to enhance the accuracy of quantum circuits through classical control and hybrid computing methods, without requiring additional quantum resources.
The computational potential of quantum computers stems from the unique properties of their qubits: through superposition, a quantum computer with a system of n qubits can provide a computational space of 2^n. This gives it a significant advantage in solving large-scale problems, particularly in fields such as factorization, molecular simulation, and artificial intelligence.
However, current quantum devices are still at the noisy intermediate-scale quantum (NISQ) stage, and the noise, thermodynamic disturbances, and other external environmental interferences during quantum circuit operations often lead to errors in qubits. Compared to errors in classical computing, quantum computing errors are more complex and harder to correct, with the risk of errors propagating throughout the quantum circuit. Therefore, effectively reducing these quantum computing errors is crucial for advancing quantum computing technology.
Also Read: InvestCloud Introduces Private Markets Account™: A Wealth Management First
Traditional quantum error correction methods typically require additional qubits to store redundant information or use complex quantum error-correcting codes to fix errors. However, these methods not only consume significant quantum resources but also impose higher demands on the physical implementation of current NISQ devices. Against this backdrop, WiMi’s MLQES (Machine-Learning-Based Quantum Error Suppression) technology offers a new direction—by relying solely on the combination of classical computers and quantum devices, it can effectively reduce quantum errors without the need for additional quantum resources.
The core idea of WiMi’s Machine Learning-Based Quantum Error Suppression Technology (MLQES) is to predict potential errors in quantum circuits using machine learning models and dynamically adjust the circuit structure to minimize the impact of errors on the final computational results.
In MLQES, the quantum circuit is first analyzed using a supervised learning model. This supervised learning model is trained on a large dataset of historical quantum circuits and error distributions, enabling it to accurately predict common errors in different quantum circuits. When a new quantum circuit is input, MLQES can predict in real-time the potential error magnitude associated with various operations in the circuit, such as quantum gates, entanglement between qubits, and so on.
SOURCE: PRNewswire