Hybrid algorithms to solve linear systems of equations with limited qubit resources
Fang Gao, Guojian Wu, Mingyu Yang, Wei Cui, Feng Shuang

TL;DR
This paper introduces three hybrid iterative phase estimation algorithms that reduce qubit resource requirements for solving linear systems of equations on quantum computers, enabling more practical applications.
Contribution
The paper proposes novel hybrid algorithms that lower qubit demands and store solutions classically, expanding quantum computing applicability for linear systems solving.
Findings
Reduced qubit requirements compared to traditional algorithms
Exact unnormalized solutions stored in classical registers
Flexible ancillary qubit configurations for different scenarios
Abstract
The solution of linear systems of equations is a very frequent operation and thus important in many fields. The complexity using classical methods increases linearly with the size of equations. The HHL algorithm proposed by Harrow et al. achieves exponential acceleration compared with the best classical algorithm. However, it has a relatively high demand for qubit resources and the solution is in a normalized form. Assuming that the eigenvalues of the coefficient matrix of the linear systems of equations can be represented perfectly by finite binary number strings, three hybrid iterative phase estimation algorithms (HIPEA) are designed based on the iterative phase estimation algorithm in this paper. The complexity is transferred to the measurement operation in an iterative way, and thus the demand of qubit resources is reduced in our hybrid algorithms.…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Parallel Computing and Optimization Techniques
