Speeding up quantum adiabatic processes with dynamical quantum geometric tensor
arXiv:2203.03164 · doi:10.1103/PhysRevResearch.4.023252
Abstract
For adiabatic controls of quantum systems, the non-adiabatic transitions are reduced by increasing the operation time of processes. Perfect quantum adiabaticity usually requires the infinitely slow variation of control parameters. In this paper, we propose the dynamical quantum geometric tensor, as a metric in the control parameter space, to speed up quantum adiabatic processes and reach quantum adiabaticity in relatively short time. The optimal protocol to reach quantum adiabaticity is to vary the control parameter with a constant velocity along the geodesic path according to the metric. For the system initiated from the n-th eigenstate, the transition probability in the optimal protocol is bounded by P_{n}(t)\leq4\mathcal{L}_{n}^{2}/τ^{2} with the operation time τand the quantum adiabatic length \mathcal{L}_{n} induced by the metric. Our optimization strategy is illustrated via two explicit models, the Landau-Zener model and the one-dimensional transverse Ising model.
9 pages, 5 figures, revised version
References in corpus (14)
- Universal Quantum Computation with Continuous-Variable Cluster States
- Quantum Thermodynamic Cycles and quantum heat engines
- Quantum Computation as Geometry
- Quantum critical scaling of the geometric tensors
- The second law, Maxwell's daemon and work derivable from quantum heat engines
- The Statistics of the Work Done on a Quantum Critical System by Quenching a Control Parameter
- Quantum Thermodynamic Cycles and Quantum Heat Engines (II)
- Quantum Adiabatic Brachistochrone
- Irreversible work and inner friction in quantum thermodynamic processes
- Intrinsic geometry of quantum adiabatic evolution and quantum phase transitions
- Finite-Time Quantum Landauer Principle and Quantum Coherence
- All-Optical Production of quantum degeneracy and molecular BEC of Li
- Work statistics across a quantum critical surface
- Robust Coherent Superposition of States using Quasiadiabatic Inverse Engineering