Time-resolved tomography of a driven adiabatic quantum simulation
arXiv:2001.05243 · doi:10.1103/PhysRevA.102.062611
Abstract
A typical goal of a quantum simulation is to find the energy levels and eigenstates of a given Hamiltonian. This can be realized by adiabatically varying the system control parameters to steer an initial eigenstate into the eigenstate of the target Hamiltonian. Such an adiabatic quantum simulation is demonstrated by directly implementing a controllable and smoothly varying Hamiltonian in the rotating frame of two superconducting qubits, including longitudinal and transverse fields and iSWAP-type two-qubit interactions. The evolution of each eigenstate is tracked using time-resolved state tomography. The energy gaps between instantaneous eigenstates are chosen such that depending on the energy transition rate either diabatic or adiabatic passages are observed in the measured energies and correlators. Errors in the obtained energy values induced by finite and times of the qubits are mitigated by extrapolation to short protocol times.
arXiv comment5 pages, 4 figures
References in corpus (23)
- QuTiP: An open-source Python framework for the dynamics of open quantum systems
- A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
- Error mitigation for short-depth quantum circuits
- Fermionic quantum computation
- Extending the computational reach of a noisy superconducting quantum processor
- Efficient variational quantum simulator incorporating active error minimisation
- Quantum optimization using variational algorithms on near-term quantum devices
- Efficient Z-Gates for Quantum Computing
- Chiral groundstate currents of interacting photons in a synthetic magnetic field
- Digitized adiabatic quantum computing with a superconducting circuit
- Observation of topological phenomena in a programmable lattice of 1,800 qubits
- A universal gate for fixed-frequency qubits via a tunable bus
- Adiabatic Quantum Simulation of Quantum Chemistry
- Experimental implementation of an adiabatic quantum optimization algorithm
- Gate-efficient simulation of molecular eigenstates on a quantum computer
- Adiabatic quantum dynamics of the Lipkin-Meshkov-Glick model
- Simulation of Many-Body Hamiltonians using Perturbation Theory with Bounded-Strength Interactions
- Analysis of parametrically driven exchange-type (iSWAP) and two-photon (bSWAP) interactions between superconducting qubits
- Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stochastic Hamiltonians
- Benchmarking the noise sensitivity of different parametric two-qubit gates in a single superconducting quantum computing platform
- Temperature scaling law for quantum annealing optimizers
- Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor
- Adiabatic quantum simulations with driven superconducting qubits