Simulating the driven-dissipative Bose-Hubbard model

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In summary, the driven-dissipative Bose-Hubbard model is a theoretical model used in quantum mechanics to study the behavior of interacting bosons in the presence of driving forces and dissipation. It is typically simulated using numerical methods and can be applied to a variety of systems, including ultracold atomic gases and quantum simulators. Simulations of this model have applications in understanding complex quantum phenomena and designing new materials and devices, but may have limitations in accurately predicting real systems due to its simplified nature and computational intensity.
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matteo137
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I'm trying to simulate with Mathematica the driven-dissipative Bose-Hubbard model, and I would like to know what is the most convenient way of doing it.

The Hamiltonian of the system is the following:
[tex] H= \Delta \sum_j a^\dagger_j a_j + U \sum_j a^\dagger_j a^\dagger_j a_j a_j + J \sum_{\langle j,j^\prime\rangle} a^\dagger_j a_{j^\prime} + F_p \sum_j \left(a^\dagger_j + a_j \right)
[/tex]
where ##\Delta, U, J, F_p ## are real constants, and the index ##j## goes from 1 to M.
##F_p ## is the amplitude of the driving field, and the dissipations are introduced through a master equation

[tex] \dfrac{d\rho}{\text{dt}} = - i \left[ H,\rho\right] + \dfrac{\kappa}{2} \sum_j \left(2 a_j \rho a^\dagger_j -a^\dagger_j a_j \rho - \rho a^\dagger_j a_j \right)
[/tex]
where ##\kappa## is the real constant.

Case 1: for the case of ##M=3## sites (trimer), I described each site in the Fock basis. i.e. with the vectors ##(1,0,0,...)=\vert 0\rangle##, ##(0,1,0,...)=\vert 1\rangle##, ... up to a cutoff ##\vert n_{\text{max}}\rangle##. Starting from this basis, I expressed the operators ##a##, ##a^\dagger## as matrices, and I built the Hamiltonian matrix, of size ##(n_{\text{max}})^M##, using Kronecker products of the kind ##a_1 = a\otimes 1\otimes 1##.
The matrix ##\rho(t)## is then obtained propagating a starting ##\rho(0)## with the Runge-Kutta algorithm.

This method does not seems very efficient and stable...
would it be better to work in the "occupation" basis? i.e. writing the operators in the basis built from the vectors ##(0,0,0)=\vert 0,0,0\rangle##, ##(1,0,0)=\vert 1,0,0\rangle##, ...
Or, for example, in the momentum basis, mean-field, ...?

Case 2: what about in two dimensions? ...is it better to work with a mean-field description?
 
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  • #2

Thank you for reaching out and sharing your question with us. As a scientist familiar with using Mathematica for simulations, I can offer some suggestions for simulating the driven-dissipative Bose-Hubbard model.

Case 1: Working in a specific basis, such as the Fock or occupation basis, can be a good starting point for simulations. However, as you have observed, it may not always be the most efficient or stable approach. My suggestion would be to try using different bases and see which one yields the best results for your specific system. For example, the momentum basis may be more suited for studying certain properties of the system, while a mean-field approach may be better for others. It may also be helpful to consult literature or speak with colleagues who have worked on similar systems to get a better idea of which basis to use.

In addition, you may also want to experiment with different numerical methods for propagating the density matrix, such as the Runge-Kutta algorithm you mentioned or other techniques like the time-dependent variational principle. Each method has its own advantages and limitations, so it would be beneficial to explore and compare their performances for your specific system.

Case 2: For simulations in two dimensions, it may be necessary to adopt a mean-field description due to the increased complexity of the system. However, as with the single dimension case, it would be beneficial to try out different approaches and see which one works best for your specific system.

In summary, there is no one "most convenient" way of simulating the driven-dissipative Bose-Hubbard model in Mathematica. It ultimately depends on the specific properties and dynamics of your system. I hope these suggestions can help guide you in finding the most suitable approach for your simulations. Best of luck with your research!
 

Related to Simulating the driven-dissipative Bose-Hubbard model

1. What is the driven-dissipative Bose-Hubbard model?

The driven-dissipative Bose-Hubbard model is a theoretical model used in quantum mechanics to study the behavior of a system of interacting bosons (particles with integer spin) in the presence of both external driving forces and dissipation (loss of energy). It is often used to understand the dynamics of condensed matter systems, such as superfluidity and superconductivity.

2. How is the driven-dissipative Bose-Hubbard model simulated?

The driven-dissipative Bose-Hubbard model is typically simulated using numerical methods, such as Monte Carlo simulations or mean-field approximations, on a computer. These simulations involve solving complex equations that describe the interactions and dynamics of the bosons in the system.

3. What types of systems can be described using the driven-dissipative Bose-Hubbard model?

The driven-dissipative Bose-Hubbard model can be applied to a wide range of systems, including ultracold atomic gases, exciton-polariton condensates, and photon or phonon systems. It is also used in the study of quantum simulators and quantum annealers.

4. What are some applications of simulating the driven-dissipative Bose-Hubbard model?

Simulating the driven-dissipative Bose-Hubbard model can help scientists understand the behavior of complex quantum systems and phenomena, such as phase transitions and quantum coherence. It can also be used to design and optimize new materials and devices, such as quantum sensors and quantum computers.

5. What are some limitations of simulating the driven-dissipative Bose-Hubbard model?

The driven-dissipative Bose-Hubbard model is a simplified theoretical model and therefore may not fully capture the complexity of real systems. Additionally, simulations of this model can be computationally intensive and may require significant computational resources. As a result, there may be limitations in accurately predicting the behavior of certain types of systems or in scaling up simulations to larger and more complex systems.

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