Quantum Sequence Alignment for Bioinformatics on Noisy Intermediate-Scale Quantum Devices.

The burgeoning field of quantum bioinformatics seeks to apply the principles of quantum computation to challenges in biological data analysis, offering potential advantages over classical methods for tasks such as genome sequencing and protein folding. Researchers are now demonstrating practical implementations of quantum algorithms on contemporary hardware, despite the limitations of current quantum devices. A team led by Floyd M. Creevey, Mingrui Jing, and Lloyd C. L. Hollenberg, all from the School of Physics at the University of Melbourne, details their work in a new paper titled “Implementation of a quantum sequence alignment algorithm for quantum bioinformatics”. Their study presents an implementation of a sequence alignment algorithm, a fundamental tool in bioinformatics used to identify similarities and differences in biological sequences, adapted for execution on noisy intermediate-scale quantum (NISQ) computers and validated through simulations. The team’s approach utilises a genetic algorithm for state preparation (GASP), a method for efficiently encoding data into quantum states, to load biological sequences into the quantum computer for analysis.

Researchers are actively investigating Quantum Sequence Alignment (QSA) algorithms to accelerate biological sequence alignment, a fundamental task within bioinformatics, and this work concentrates on improving the robustness of QSA against errors inherent in contemporary quantum hardware, a substantial impediment to practical application. The study details an implementation of QSA, adapting a method originally proposed in 2000 to the limitations of Noisy Intermediate-Scale Quantum (NISQ) devices, and employs a Genetic Algorithm for State Preparation (GASP), a technique that constructs encoding circuits to efficiently load both database and target sequences into the quantum computer’s registers, actively addressing the challenges of representing complex biological data within the quantum realm.

Biological sequence alignment involves determining the similarities and differences between DNA, RNA, or protein sequences, crucial for understanding evolutionary relationships, identifying gene functions, and developing new therapies. Quantum computing offers the potential to perform these alignments much faster than classical computers for certain types of problems. NISQ devices represent the current generation of quantum computers, characterised by a limited number of qubits and susceptibility to errors. GASP, in this context, optimises the process of translating biological data into a quantum state, minimising the resources required and improving the algorithm’s resilience to noise.

Testing occurs in both simulated and real NISQ environments, validating the methodology and refining the GASP circuit designs, and results demonstrate the feasibility of deploying QSA on current hardware, showcasing the potential of GASP as a valuable tool for data encoding in complex algorithms, not only within bioinformatics but also in other data-intensive fields. The simulations allow researchers to test the algorithm under ideal conditions and identify potential bottlenecks, while testing on real hardware reveals the impact of noise and imperfections. The success of GASP in these environments suggests its broader applicability to other quantum algorithms where efficient data loading is critical.

This research demonstrates the practical implementation of a QSA algorithm, successfully implementing it on biological data within both simulated environments and utilising actual NISQ hardware, validating the approach and refining data-loading circuit designs. The study establishes a foundation for future research aimed at harnessing the power of quantum computing to address critical challenges in biological data analysis and ultimately advance our understanding of life sciences, and highlights the importance of efficient data loading as a critical component of successful quantum algorithm implementation. Further development will likely focus on scaling the algorithm to handle larger datasets and improving its performance on more complex biological sequences.