35 citations · 36 across the 5 of their papers we have counts for
5 papers
A Hybrid Quantum Neural Network to Analyse Big Experimental Powder X-ray Diffraction Data
H. Dong, S. D. M. Jacques, M. Q. Hlatshwayo +4
Quantitative analysis of experimental powder X-ray diffraction data remains challenging when evaluating complex multiphase materials and noisy measurements. We introduce a hybrid q…
Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
Vincent Graves, Manqoba Q. Hlatshwayo, Theodoros Kapourniotis +1
Quantum algorithms for selecting a subspace of Hamiltonians to diagonalize have emerged as a promising alternative to variational algorithms in the NISQ era. So far, such algorithm…
Response of strongly coupled fermions on classical and quantum computers
John Novak, Manqoba Q. Hlatshwayo, Elena Litvinova
Studying the response of quantum systems is essential for gaining deeper insights into the fundamental nature of matter and its behavior in diverse physical contexts. Computation o…
Quantum benefit of the quantum equation of motion for the strongly coupled many-body problem
Manqoba Q. Hlatshwayo, John Novak, Elena Litvinova
We investigate the quantum equation of motion (qEOM), a hybrid quantum-classical algorithm for computing excitation properties of a fermionic many-body system, with a particular em…
Simulating excited states of the Lipkin model on a quantum computer
Manqoba Q. Hlatshwayo, Yinu Zhang, Herlik Wibowo +3
We simulate the excited states of the Lipkin model using the recently proposed Quantum Equation of Motion (qEOM) method. The qEOM generalizes the EOM on classical computers and giv…