Parameterized Two-Qubit Gates for Enhanced Variational Quantum Eigensolver

Abstract

The variational quantum eigensolver is a prominent hybrid quantum-classical algorithm expected to impact near-term quantum devices. They are usually based on a circuit ansatz consisting of parameterized single-qubit gates and fixed two-qubit gates. We study the effect of parameterized twoqubit gates in the variational quantum eigensolver. We simulate a variational quantum eigensolver algorithm using fixed and parameterized two-qubit gates in the circuit ansatz and show that the parameterized versions outperform the fixed versions for a range of Hamiltonians with applications in quantum chemistry and materials science.

Applications and resource reductions in measurement-based variational quantum eigensolvers

“…As the entangling gate we chose the diagonal R Z ⊗n since it naturally implements as an MBQC, reducing the resources. Also, there is evidence that parametrising the entangling step of a VQE reduces required depths further reducing the resources [27]. The bottom half of figure 4 shows the reduced MBQC that implements the single layer circuit.…”

Multiqubit State Learning with Entangling Quantum Generative Adversarial Networks

“…1, which only employs nearest-neighbor interactions, it is expected that the fidelity will decrease as the number of qubits increases. Instead of just using cnot gates as the entangling operation we also simulate the EQ-GAN using iswap and cz gates and their parameterized equivalents [37].…”

“…This indicates the advantages of using parameterized two-qubit gates instead of just increasing the number of layers. This could be advantageous in near-term devices [37].…”