Variational quantum eigensolver for the Heisenberg antiferromagnet on the kagome lattice
arXiv:2108.02175 · doi:10.1103/PhysRevB.106.214429
Abstract
Establishing the nature of the ground state of the Heisenberg antiferromagnet (HAFM) on the kagome lattice is well known to be a prohibitively difficult problem for classical computers. Here, we give a detailed proposal for a Variational Quantum Eigensolver (VQE) intending to solve this physical problem on a quantum computer. At the same time, this VQE constitutes an explicit experimental proposal for showing a useful quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) devices because of its natural hardware compatibility. We classically emulate noiseless and noisy quantum computers with either 2D-grid or all-to-all connectivity and simulate patches of the kagome HAFM of up to 20 sites. In the noiseless case, the ground-state energy, as found by the VQE, approaches the true ground-state energy exponentially as a function of the circuit depth. Furthermore, VQEs for the HAFM on any graph can inherently perform their quantum computations in a decoherence-free subspace that protects against collective longitudinal and collective transversal noise, adding to the noise-resilience of these algorithms. Nevertheless, the extent of the effects of other noise types suggests the need for error mitigation and performance targets alternative to high-fidelity ground-state preparation, even for essentially hardware-native VQEs.
Added noise effects, system-size scaling, decoherence-free subspace encoding. Published version, 19 pages, 13 figures
References in corpus (24)
- Array Programming with NumPy
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Surface codes: Towards practical large-scale quantum computation
- Quantum computational advantage using photons
- 14-qubit entanglement: creation and coherence
- A four-qubit germanium quantum processor
- Quantum computing with nearest neighbor interactions and error rates over 1%
- The Future of Quantum Computing with Superconducting Qubits
- Ground State of the Kagome Lattice Heisenberg Antiferromagnet
- Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
- Virtual Distillation for Quantum Error Mitigation
- Low-Disorder Microwave Cavity Lattices for Quantum Simulation with Photons
- Efficient estimation of Pauli observables by derandomization
- Exponential Error Suppression for Near-Term Quantum Devices
- Numerical-Diagonalization Study of Spin Gap Issue of the Kagome Lattice Heisenberg Antiferromagnet
- Quantum NP - A Survey
- Triplet and Singlet Excitations in the Valence Bond Crystal Phase of Kagome Lattice Heisenberg Model
- Observation of separated dynamics of charge and spin in the Fermi-Hubbard model
- Evaluating the noise resilience of variational quantum algorithms
- Penalty methods for variational quantum eigensolver
- Quantum simulation of antiferromagnetic Heisenberg chain with gate-defined quantum dots
- Perturbative quantum simulation
- Limitations of optimization algorithms on noisy quantum devices
- Quantum simulation scheme of two-dimensional xy-model Hamiltonian with controllable coupling
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- Ground state energy and magnetization curve of a frustrated magnetic system from real-time evolution on a digital quantum processor
- Role of Riemannian geometry in double-bracket quantum imaginary-time evolution
- Accelerated spin-adapted ground state preparation with non-variational quantum algorithms
- Double-bracket quantum algorithms for high-fidelity ground state preparation
- Preparation of the single-spinon wave function on a quantum computer
- Efficient quantum simulation for translationally invariant systems
- Quantum algorithm for one-quasiparticle excitations in the thermodynamic limit via cluster-additive block diagonalization