Finite-size criticality in fully connected spin models on superconducting quantum hardware
arXiv:2208.02731 · doi:10.1103/PhysRevE.107.024113
Abstract
The emergence of a collective behavior in a many-body system is responsible of the quantum criticality separating different phases of matter. Interacting spin systems in a magnetic field offer a tantalizing opportunity to test different approaches to study quantum phase transitions. In this work, we exploit the new resources offered by quantum algorithms to detect the quantum critical behaviour of fully connected spin models. We define a suitable Hamiltonian depending on an internal anisotropy parameter that allows us to examine three paradigmatic examples of spin models, whose lattice is a fully connected graph. We propose a method based on variational algorithms run on superconducting transmon qubits to detect the critical behavior for systems of finite size. We evaluate the energy gap between the first excited state and the ground state, the magnetization along the easy-axis of the system, and the spin-spin correlations. We finally report a discussion about the feasibility of scaling such approach on a real quantum device for a system having a dimension such that classical simulations start requiring significant resources.
13 pages, 10 figures. Comments are welcome
References in corpus (13)
- Tools for quantum simulation with ultracold atoms in optical lattices
- Finite-Size Scaling Exponents of the Lipkin-Meshkov-Glick Model
- Exact spectrum of the Lipkin-Meshkov-Glick model in the thermodynamic limit and finite-size corrections
- Quantum metrology in Lipkin-Meshkov-Glick critical systems
- Infinite-range Ising ferromagnet in a time-dependent transverse field: quench and ac dynamics near the quantum critical point
- Quantum criticality of the Lipkin-Meshkov-Glick Model in terms of fidelity susceptibility
- Quantum computing of the Li nucleus via ordered unitary coupled clusters
- Variational Quantum Eigensolver for Frustrated Quantum Systems
- Quantum phase detection generalisation from marginal quantum neural network models
- Simulating excited states of the Lipkin model on a quantum computer
- Quantum Phase Transition in Finite-Size Lipkin-Meshkov-Glick Model
- Classical description of the parameter space geometry in the Dicke and Lipkin-Meshkov-Glick models
- Quantum Thermal Amplifiers with Engineered Dissipation
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- Toward scalable quantum computations of atomic nuclei
- Precision magnetometry exploiting excited state quantum phase transitions
- Few-body precursors of topological frustration
- Double-bracket quantum algorithms for high-fidelity ground state preparation