Tunable anisotropic quantum Rabi model via a magnon--spin-qubit ensemble
- URL: http://arxiv.org/abs/2105.07430v2
- Date: Fri, 3 Dec 2021 09:57:21 GMT
- Title: Tunable anisotropic quantum Rabi model via a magnon--spin-qubit ensemble
- Authors: Ida C. Skogvoll, Jonas Lidal, Jeroen Danon, Akashdeep Kamra
- Abstract summary: We study theoretically a spin qubit exchange-coupled to an anisotropic ferromagnet that hosts magnons with a controllable degree of intrinsic squeezing.
We demonstrate that the composite nature of the squeezed magnon enables concurrent excitation of three spin qubits coupled to the same magnet.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The ongoing rapid progress towards quantum technologies relies on new hybrid
platforms optimized for specific quantum computation and communication tasks,
and researchers are striving to achieve such platforms. We study theoretically
a spin qubit exchange-coupled to an anisotropic ferromagnet that hosts magnons
with a controllable degree of intrinsic squeezing. We find this system to
physically realize the quantum Rabi model from the isotropic to the
Jaynes-Cummings limit with coupling strengths that can reach the deep-strong
regime. We demonstrate that the composite nature of the squeezed magnon enables
concurrent excitation of three spin qubits coupled to the same magnet. Thus,
three-qubit Greenberger-Horne-Zeilinger and related states needed for
implementing Shor's quantum error-correction code can be robustly generated.
Our analysis highlights some unique advantages offered by this hybrid platform,
and we hope that it will motivate corresponding experimental efforts.
Related papers
- Toward hybrid quantum simulations with qubits and qumodes on trapped-ion platforms [0.0]
We explore the feasibility of hybrid quantum computing using both discrete (qubit) and continuous (qumode) variables on trapped-ion platforms.
We show that high-fidelity hybrid gates and measurement operations can be achieved for existing trapped-ion quantum platforms.
arXiv Detail & Related papers (2024-10-09T18:01:15Z) - Persisting quantum effects in the anisotropic Rabi model at thermal
equilibrium [0.0]
We study the long-lived quantum correlations and nonclassical states generated in the anisotropic Rabi model.
We demonstrate a stark distinction between virtual excitations produced beyond the strong coupling regime and the quantumness quantifiers once the light-matter interaction has been switched off.
arXiv Detail & Related papers (2023-09-05T10:59:32Z) - Neural-network quantum states for ultra-cold Fermi gases [49.725105678823915]
This work introduces a novel Pfaffian-Jastrow neural-network quantum state that includes backflow transformation based on message-passing architecture.
We observe the emergence of strong pairing correlations through the opposite-spin pair distribution functions.
Our findings suggest that neural-network quantum states provide a promising strategy for studying ultra-cold Fermi gases.
arXiv Detail & Related papers (2023-05-15T17:46:09Z) - Dilute neutron star matter from neural-network quantum states [58.720142291102135]
Low-density neutron matter is characterized by the formation of Cooper pairs and the onset of superfluidity.
We model this density regime by capitalizing on the expressivity of the hidden-nucleon neural-network quantum states combined with variational Monte Carlo and reconfiguration techniques.
arXiv Detail & Related papers (2022-12-08T17:55:25Z) - Quantum emulation of the transient dynamics in the multistate
Landau-Zener model [50.591267188664666]
We study the transient dynamics in the multistate Landau-Zener model as a function of the Landau-Zener velocity.
Our experiments pave the way for more complex simulations with qubits coupled to an engineered bosonic mode spectrum.
arXiv Detail & Related papers (2022-11-26T15:04:11Z) - High-accuracy variational Monte Carlo for frustrated magnets with deep
neural networks [1.2891210250935146]
We show that neural quantum states based on very deep (4--16-layered) neural networks can outperform state-of-the-art variational approaches on highly frustrated quantum magnets.
We focus on group convolutional neural networks (GCNNs) that allow us to impose space-group symmetries on our ans"atze.
arXiv Detail & Related papers (2022-11-14T20:51:19Z) - Enhancing quantum exchanges between two oscillators [0.0]
We show that two quantum oscillators can exchange quantum states efficiently through a three-level system.
High transition probabilities are obtained using Hamiltonian engineering and quantum control techniques.
arXiv Detail & Related papers (2022-07-22T15:53:49Z) - Trapped-Ion Quantum Simulation of Collective Neutrino Oscillations [55.41644538483948]
We study strategies to simulate the coherent collective oscillations of a system of N neutrinos in the two-flavor approximation using quantum computation.
We find that the gate complexity using second order Trotter- Suzuki formulae scales better with system size than with other decomposition methods such as Quantum Signal Processing.
arXiv Detail & Related papers (2022-07-07T09:39:40Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - Quantum simulation of antiferromagnetic Heisenberg chain with
gate-defined quantum dots [0.0]
Magnetic phases naturally arise in the Mott-insulator regime of the Fermi-Hubbard model.
We show the quantum simulation of magnetism in the Mott-insulator regime with a linear quantum-dot array.
arXiv Detail & Related papers (2021-03-15T09:45:02Z) - Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
arXiv Detail & Related papers (2021-01-21T22:18:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.