Mathematical analysis

Linear algebra

Geometry

Linear system

Quantum

Physics

Pure mathematics

Mathematics

Mathematical optimization

Quantum algorithm

Matrix multiplication

Quantum mechanics

Algebra over a field

Computer science

Quantum computer

Algorithm

Hamiltonian (control theory)

Numerical linear algebra

Applied mathematics

Science Bulletin

Quantum algorithms have been developed for efficiently solving linear algebra tasks. However, they generally require deep circuits and hence universal fault-tolerant quantum computers. In this work, we propose variational algorithms for linear algebra tasks that are compatible with noisy intermediate-scale quantum devices. We show that the solutions of linear systems of equations and matrix-vector multiplications can be translated as the ground states of the constructed Hamiltonians. Based on the variational quantum algorithms, we introduce Hamiltonian morphing together with an adaptive ansätz for efficiently finding the ground state, and show the solution verification. Our algorithms are especially suitable for linear algebra problems with sparse matrices, and have wide applications in machine learning and optimisation problems. The algorithm for matrix multiplications can be also used for Hamiltonian simulation and open system simulation. We evaluate the cost and effectiveness of our algorithm through numerical simulations for solving linear systems of equations. We implement the algorithm on the IBM quantum cloud device with a high solution fidelity of 99.95%.

Variational algorithms for linear algebra