Novel quantum computing algorithm enhances single-cell analysis

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A new quantum algorithm developed by University of Georgia statisticians addresses one of the most complex challenges in single-cell analysis, signaling significant impact in both the fields of computational biology and quantum computing.

The study, "Bisection Grover's Search Algorithm and Its Application in Analyzing CITE-seq Data," was published in the Journal of the American Statistical Association on Sept. 20.

While traditional approaches struggle to handle the immense amount of data generated from measuring both RNA and protein expression in individual cells, the new quantum algorithm enables analysis of data from a single-cell technology known as CITE-seq. It allows for selection of the most important markers from billions of possible combinations—a task that would be formidable using classical methods.

"The method is particularly promising for applications in disease research, where understanding the molecular identity of individual cells is crucial," said Ping Ma, UGA Distinguished Research Professor in the Franklin College of Arts and Sciences department of statistics and author of the study detailing the method.

"The power of quantum computing—an emerging and complex technology—provides a faster and more efficient way to analyze biological data that can potentially improve our understanding of good health and disease conditions."

A classical algorithm runs on conventional computers akin to laptops and smartphones, which process information as bits—like on/off switches representing 0s and 1s. These algorithms solve problems by working through a series of steps in a sequential manner, typically one step at a time. This is efficient for many tasks but can be slow when tackling complex problems with many possibilities.

A quantum algorithm, however, runs on a quantum computer, which uses quantum bits (or qubits). Unlike bits, qubits can represent 0 and 1 at the same time, due to a property called superposition.

Quantum algorithms can process many possibilities simultaneously and make use of another quantum property called entanglement to link qubits together, boosting computational power. As a result, quantum algorithms can solve certain problems much faster than classical algorithms by exploring all possible solutions at once instead of step-by-step.

In essence, while classical algorithms are like following a single path through a maze, quantum algorithms are like taking all paths at once, which can lead to a solution much more efficiently for certain problems.

The results of the study were validated by UGA doctoral students Yongkai Chen and Haoran Lu using IBM's quantum computer, a testament to the practical relevance of this work.

"The unique characteristics of quantum algorithms make them especially well-suited to tackle complex genomic and transcriptomic problems, where the combinations and interactions of genetic markers or sequences can be vast and computationally demanding," said Wenxuan Zhong, UGA Athletic Association Professor in the Franklin College of Arts and Sciences department of statistics and author of the study.

"Bringing the power of technology into the life sciences helps advance quantum computing beyond theoretical applications, reaching into impactful, real-world solutions."

More information: Ping Ma et al, Bisection Grover's Search Algorithm and Its Application in Analyzing CITE-seq Data, Journal of the American Statistical Association (2024). DOI: 10.1080/01621459.2024.2404259

Provided by University of Georgia