hqQUBO: A Hybrid-querying Quantum Optimization Model Validated with 16-qubits on an Ion Trap Quantum Computer for Life Science Applications

TL;DR

This paper introduces hyQUBO, a hybrid quantum algorithm for multiple sequence alignment in life sciences, demonstrating its effectiveness on a 16-qubit ion trap quantum computer and highlighting its potential for large-scale biological computations.

Contribution

The work presents a novel hybrid-query encoding method, hyQUBO, reducing quantum resource requirements and successfully implementing large-scale quantum simulations for life science applications.

Findings

Achieved state-of-the-art performance on 16-qubit quantum hardware.

Demonstrated exponential scaling advantages over classical methods.

Validated robustness and convergence of the hybrid quantum algorithm.

Abstract

AlphaFold has achieved groundbreaking advancements in protein structure prediction, exerting profound influence across biology, medicine, and drug discovery. However, its reliance on multiple sequence alignment (MSA) is inherently time-consuming due to the NP-hard nature of constructing MSAs. Quantum computing emerges as a promising alternative, compared to classical computers, offering the potentials for exponential speedup and improved accuracy on such complex optimization challenges. This work bridges the gap between quantum computing and MSA task efficiently and successfully, where we compared classical and quantum computational scaling as the number of qubits increases, and assessed the role of quantum entanglement in model performance. Furthermore, we proposed an innovative hybrid query encoding approach hyQUBO to avoid redundancy, and thereby the quantum resources significantly reduced to a scaling of $\mathcal{O}(NL)$. Additionally, coupling of VQE and the quenched CVaR scheme was utilized to enhance the robustness and convergence. The integration of multiple strategies facilitates the robust deployment of the quantum algorithm from idealized simulators (on CPU and GPU) to real-world, noisy quantum devices (HYQ-A37). To the best of our knowledge, our work represented the largest-scale implementation of digital simulation using up to 16 qubits on a trapped-ion quantum computer for life science problem, which achieved state of the art performance in both simulation and experimental results. Our work paves the way towards large-scale simulations of life science tasks on real quantum processors.