Developed: Machine Learning based Quantum Error Suppression Technology

Leading Hologram Augmented Reality tech provider WiMi Tuesday (December 24, 2024) announced the development of an innovative solution: Machine Learning-based Quantum Error Suppression Technology (MLQES)

Tuesday December 24, 2024 9:40 PM, ummid.com News Network

Beijing: Leading Hologram Augmented Reality tech provider WiMi Tuesday (December 24, 2024) 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", WiMi Hologram Cloud Inc said.

The computational potential of quantum computers stems from the unique properties of their qubits through superposition that 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", WiMi said.

A qubit, or quantum bit, is the fundamental unit of information in quantum computing.

"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", WiMi said.

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", WiMi said.

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.

The Core Idea of WiMi MLQES

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.

"Supervised Learning Model"

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", WiMi said.

Once the machine learning model predicts that the error value in a quantum circuit exceeds a predetermined threshold, WiMi's MLQES system triggers a circuit segmentation mechanism.

"This is one of the innovations of MLQES: to prevent the entire circuit from running under high-error conditions, MLQES can use an error-affected fragmentation strategy to split a large quantum circuit into two or more smaller sub-circuits.

"This segmentation strategy ensures that within each sub-circuit, errors are controlled within an acceptable range. MLQES employs an iterative segmentation process until the error prediction for each sub-circuit is below the set threshold", WiMi said.

"Classical Reconstruction Algorithm"

The segmented sub-circuits can operate independently on the quantum device. Since the sub-circuits are smaller in scale, the entanglement and interaction between qubits become easier to control, thus reducing noise interference in quantum operations. Once each sub-circuit completes its execution, its output is sent to a classical computer for further processing.

"On the classical computer, MLQES uses a classical reconstruction algorithm to combine the results from multiple sub-circuits into the output of the complete quantum circuit.

"This reconstruction process does not rely on additional quantum operations but leverages the powerful processing capabilities of classical computing to compensate for the limitations of quantum computation", WiMi said.

"MLQES not only addresses the quantum error problem but also provides a scalable computational framework for the future of quantum computing. This technology combines the strengths of quantum computers and classical computers, using the powerful processing capabilities of classical computing to control the execution of quantum circuits", the Hologram AR technology provider said.

"This fusion of classical and quantum computing opens up possibilities for further applications of future NISQ devices, especially in scenarios where the number of qubits is limited but high-precision computation is required. MLQES reduces the reliance on quantum error-correcting codes and redundant qubits in quantum computing while significantly enhancing the overall efficiency of quantum computation", the tech provider said.

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