Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets.

Area of Science:

• Quantum computing
• Computational chemistry
• Materials science

Background:

• Classical computers struggle with quantum electronic-structure problems due to exponential scaling and the fermionic sign problem.
• Existing quantum implementations are limited to small molecules like hydrogen and helium.
• Solving these problems is crucial for advancements in materials science and condensed matter physics.

Purpose of the Study:

• To demonstrate the experimental optimization of Hamiltonian problems using quantum computation.
• To determine the ground-state energy of molecules with increasing size, up to BeH2.
• To explore the application of quantum algorithms to quantum magnetism.

Main Methods:

• Utilized a variational quantum eigensolver (VQE) with tailored trial states.
• Employed a compact encoding of fermionic Hamiltonians.
• Implemented a robust stochastic optimization routine for Hamiltonian problems up to six qubits and over one hundred Pauli terms.

Main Results:

• Successfully determined the ground-state energy for molecules up to BeH2.
• Applied the quantum approach to an antiferromagnetic Heisenberg model, demonstrating flexibility.
• Experimental results show agreement with numerical simulations, accounting for device noise.

Conclusions:

• The study successfully scaled quantum computation for electronic-structure problems beyond simple molecules.
• The methods developed are applicable to quantum magnetism and other complex quantum systems.
• This work provides insights into scaling quantum algorithms for real-world high-performance computing challenges.