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arXiv:1506.05135 · Physical Review A

Solving strongly correlated electron models on a quantum computer

Dave Wecker, Matthew B. Hastings, Nathan Wiebe, Bryan K. Clark, Chetan Nayak, Matthias Troyer

Published Dec 10, 2015• 285 citations
arXivHTMLPDF

Abstract

One of the main applications of future quantum computers will be the simulation of quantum models. While the evolution of a quantum state under a Hamiltonian is straightforward (if sometimes expensive), using quantum computers to determine the ground state phase diagram of a quantum model and the properties of its phases is more involved. Using the Hubbard model as a prototypical example, we here show all the steps necessary to determine its phase diagram and ground state properties on a quantum computer. In particular, we discuss strategies for efficiently determining and preparing the ground state of the Hubbard model starting from various mean-field states with broken symmetry. We present an efficient procedure to prepare arbitrary Slater determinants as initial states and present the complete set of quantum circuits needed to evolve from these to the ground state of the Hubbard model. We show that, using efficient nesting of the various terms each time step in the evolution can be performed with just $\mathcal{O}(N)$ gates and $\mathcal{O}(\log N)$ circuit depth. We give explicit circuits to measure arbitrary local observables and static and dynamic correlation functions, both in the time and frequency domain. We further present efficient non-destructive approaches to measurement that avoid the need to re-prepare the ground state after each measurement and that quadratically reduce the measurement error.

DOI
10.1103/physreva.92.062318

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Quantum phase transitions materialize as level crossings in the ground-state energy when the parameters of the Hamiltonian are varied. The resulting ground-state phase diagrams are straightforward to determine by exact diagonalization on classical computers, but are challenging on quantum computers because of the accuracy needed and the near degeneracy of the competing states close to the level crossings. On the other hand, classical computers are limited to small system sizes, which quantum computers may help overcome. In this work, we use a local adiabatic ramp for state preparation to allow us to directly compute ground-state phase diagrams on a quantum computer via time evolution. This methodology is illustrated by examining the ground states of the XY model with a magnetic field in the z-direction in one dimension. We are able to calculate an accurate phase diagram on both two- and three-site systems using IBM quantum machines.
The performance of quantum algorithms for eigenvalue problems, such as computing Hamiltonian spectra, depends strongly on the overlap of the initial wave function and the target eigenvector. In a basis of Slater determinants, the representation of energy eigenstates of systems with N strongly correlated electrons requires a number of determinants that scales exponentially with N . On classical processors, this restricts simulations to systems where N is small. Here, we show that quantum computers can efficiently simulate strongly correlated molecular systems by directly encoding the dominant entanglement structure in the form of spin-coupled initial states. This avoids resorting to expensive classical or quantum state preparation heuristics and instead exploits symmetries in the wave function. We provide quantum circuits for deterministic preparation of a family of spin eigenfunctions with ( N N / 2 ) Slater determinants with depth O ( N ) and O ( N 2 ) local gates. Their use as highly entangled initial states in quantum algorithms reduces the total runtime of quantum phase estimation and related fault-tolerant methods by orders of magnitude. Furthermore, we assess the application of spin-coupled wave functions as initial states for a range of heuristic quantum algorithms, namely, the variational quantum eigensolver, adiabatic state preparation, and different versions of quantum subspace diagonalization (QSD) including QSD based on real-time-evolved states. We also propose a QSD algorithm that exploits states obtained through adaptive quantum eigensolvers. For all algorithms, we demonstrate that using spin-coupled initial states drastically reduces the quantum resources required to simulate strongly correlated ground and excited states. Our work provides a crucial component for enabling scalable quantum simulation of classically challenging electronic systems.
We want to illustrate how to find the ground state of the one-dimensional Hubbard model. We start by introducing the classical algorithms that can do this, like exact diagonalization, Lanczos algorithm and the Bethe-ansatz equations. Subsequently, we show how to solve the problem on a gate-based quantum computer using the quantum annealing algorithm. The procedure involves transforming the fermionic Hamiltonian using the Jordan-Wigner transformation, preparing the initial state for quantum annealing using Givens rotations, and constructing the circuit for the time evolution according to the Schrodinger equation using the second-order product-formula algorithm. We conclude by presenting some results and analysis of performance for the quantum annealing algorithm.
We investigate the performance and accuracy of digital quantum algorithms for the study of static and dynamic properties of the fermionic Hubbard model at half-filling with next-nearest-neighbor hopping terms. We provide quantum circuits to perform ground and excited state calculations, via the variational quantum eigensolver (VQE) and the quantum equation of motion (qEOM) approach, respectively, as well as product formula decompositions for time evolution. We benchmark our approach on a chain with L=6L=6L=6 sites and periodic boundary conditions, computing the charge and spin gaps, the spectral function and spin-spin dynamic correlations. Our results for the ground-state phase diagram are in qualitative agreement with known results in the thermodynamic limit. Finally, we provide concrete scalings for the number of gates needed to implement our protocols on a qubit register with all-to-all connectivities and on a heavy hexagonal coupling map.
Transcorrelated methods provide an efficient way of partially transferring the description of electronic correlations from the ground state wavefunction directly into the underlying Hamiltonian. In particular, Dobrautz et al. [Phys. Rev. B, 99(7), 075119, (2019)] have demonstrated that the use of momentum-space representation, combined with a non-unitary similarity transformation, results in a Hubbard Hamiltonian that possesses a significantly more compact ground state wavefunction, dominated by a single Slater determinant. This compactness/single-reference character greatly facilitates electronic structure calculations. As a consequence, however, the Hamiltonian becomes non-Hermitian, posing problems for quantum algorithms based on the variational principle. We overcome these limitations with the ansatz-based quantum imaginary time evolution algorithm and apply the transcorrelated method in the context of digital quantum computing. We demonstrate that this approach enables up to 4 orders of magnitude more accurate and compact solutions in various instances of the Hubbard model at intermediate interaction strength (U/t=4U/t=4U/t=4), enabling the use of shallower quantum circuits for wavefunction ansatzes. In addition, we propose a more efficient implementation of the quantum imaginary time evolution algorithm in quantum circuits that is tailored to non-Hermitian problems. To validate our approach, we perform hardware experiments on the ibmq_lima quantum computer. Our work paves the way for the use of exact transcorrelated methods for the simulations of ab initio systems on quantum computers.