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Quantum Computing

New Method Improves Performance of Quantum Computers While Reducing Environmental Impact

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A team of researchers from the National Institute of Information and Communications Technology, Keio University, Tokyo University of Science, and The University of Tokyo, has successfully developed a method for systematically finding the optimal quantum operation sequence for a quantum computer. This new method is the first to achieve success. 

The research was published in the scientific journal Physical Review A

Developing the New Method

Quantum computers perform tasks by relying on experts writing a sequence of quantum operations, which traditionally has involved computer operators writing their own based on existing methods. The team developed a systematic method that applied optimal control theory (GRAPE algorithm) to identify the theoretically optimal sequence from among all conceivable quantum operation sequences. 

The new method is expected to be useful for medium-scale quantum computers. At the same time, the team says it should help improve the performance of quantum computers while also reducing the environmental impact in the near future. 

Quantum computers have the potential to solve a wide range of complex problems, such as reducing the environmental burden by reducing energy consumption, and discovering new chemical substances for the medical field. 

Challenges of Quantum Computing

However, one of the major challenges of quantum computing is that the quantum state is highly sensitive to noise, meaning it’s difficult to keep it stable for a longer period of time. The operations must be completed within the time that the coherent quantum state is maintained, and this requires a method for systematically identifying the optimal sequences. 

A quantum operation sequence is a computer program that’s written in a human-readable language, and it is converted to be processed by a quantum computer. The quantum operation sequence involves 1-qubit operations and 2-qubit operations, but the best sequence has the fewest operations while demonstrating the best performance. 

The newly developed method analyzes all possible sequences of elementary quantum operations through the use of the GRAPE algorithm, which is a numerical optimal control theory algorithm. The team creates a table of quantum operation sequences and the performance index for each, which can range from thousands to millions. The optimal quantum operation sequence can then be systematically identified based on the accumulated data. 

The team’s method can also analyze the complete list of all quantum operation sequences and evaluate conventional methods, which enables it to help establish benchmarks for past and future research. 

The team also discovered that there are many excellent optimal sequences of quantum operations, meaning a probabilistic approach could extend the applicability of the new method to larger tasks. By integrating machine learning with the method, predictive power can be enhanced even more.

Alex McFarland is a tech writer who covers the latest developments in artificial intelligence. He has worked with AI startups and publications across the globe.