Ab initio quantum simulation of strongly correlated materials with quantum embedding

Quantum computing has shown great potential in various quantum chemical applications such as drug discovery,material design,and catalyst optimization.Although significant progress has been made in the quantum simulation of simple molecules,ab initio simulation of solid-state materials on quantum computers is still in its early stage,mostly owing to the fact that the system size quickly becomes prohibitively large when approaching the thermodynamic limit.In this work,we introduce an orbital-based multifragment approach on top of the periodic density matrix embedding theory,resulting in a significantly smaller problem size for the current near-term quantum computer.We demonstrate the accuracy and efficiency of our method compared with the conventional methodologies and experiments on solid-state systems with complex electronic structures.These include spin-polarized states of a hydrogen chain(1D-H),the equation of state of a boron nitride layer(h-BN)as well as the magnetic ordering in nickel oxide(NiO),a prototypical strongly correlated solid.Our results suggest that quantum embedding combined with a chemically intuitive fragmentation can greatly advance quantum simulation of realistic materials,thereby paving the way for solving important yet classically hard industrial problems on near-term quantum devices.

Protonation induced high-T_c phases in iron-based superconductors evidenced by NMR and magnetization measurements

Chemical substitution during growth is a well-established method to manipulate electronic states of quantum materials, and leads to rich spectra of phase diagrams in cuprate and iron-based superconductors. Here we report a novel and generic strategy to achieve nonvolatile electron doping in series of(i.e.11 and 122 structures) Fe-based superconductors by ionic liquid gating induced protonation at room temperature. Accumulation of protons in bulk compounds induces superconductivity in the parent compounds, and enhances the Tclargely in some superconducting ones. Furthermore, the existence of proton in the lattice enables the first proton nuclear magnetic resonance(NMR) study to probe directly superconductivity. Using Fe S as a model system, our NMR study reveals an emergent high-Tcphase with no coherence peak which is hard to measure by NMR with other isotopes. This novel electric-fieldinduced proton evolution opens up an avenue for manipulation of competing electronic states(e.g.Mott insulators), and may provide an innovative way for a broad perspective of NMR measurements with greatly enhanced detecting resolution.

Variational algorithms for linear algebra

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 ans?tz 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%.