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MicroAlgo Inc. Develops Hybrid Classical-Quantum Algorithms to Optimize Multi-Query Problems
Thursday, 2nd January at 9:00 am
SHENZHEN, China, Jan. 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of an innovative hybrid algorithm that combines the advantages of classical and quantum computing to optimize Multi-Query Optimization (MQO) problems.
Quantum computing is a technology that uses the principles of quantum mechanics to process information. Compared to traditional classical computers, quantum computers exhibit the potential to outperform classical computers in handling certain types of problems, such as search, optimization, and simulating quantum systems. However, the realization of quantum computers faces technical challenges, particularly in constructing quantum computers with a sufficient number of qubits and low error rates.
The Multi-Query Optimization (MQO) problem is a class of data-intensive problems that are NP-hard, and it has applications in many fields such as database query optimization, machine learning algorithms, and network routing. The core of the MQO problem lies in how to effectively handle multiple query requests to minimize the overall computational cost or time.
Although quantum computers theoretically have tremendous potential, current quantum computers are far from being fully practical. The limited number of qubits and high error rates restrict their ability to solve large-scale problems. To address these issues, MicroAlgo has proposed a hybrid algorithm that combines the stability of classical computers with the efficiency of quantum computers.
Thursday, 2nd January at 9:00 am
SHENZHEN, China, Jan. 2, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of an innovative hybrid algorithm that combines the advantages of classical and quantum computing to optimize Multi-Query Optimization (MQO) problems.
Quantum computing is a technology that uses the principles of quantum mechanics to process information. Compared to traditional classical computers, quantum computers exhibit the potential to outperform classical computers in handling certain types of problems, such as search, optimization, and simulating quantum systems. However, the realization of quantum computers faces technical challenges, particularly in constructing quantum computers with a sufficient number of qubits and low error rates.
The Multi-Query Optimization (MQO) problem is a class of data-intensive problems that are NP-hard, and it has applications in many fields such as database query optimization, machine learning algorithms, and network routing. The core of the MQO problem lies in how to effectively handle multiple query requests to minimize the overall computational cost or time.
Although quantum computers theoretically have tremendous potential, current quantum computers are far from being fully practical. The limited number of qubits and high error rates restrict their ability to solve large-scale problems. To address these issues, MicroAlgo has proposed a hybrid algorithm that combines the stability of classical computers with the efficiency of quantum computers.
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