The Signed Particle Formulation provides a particle-based interpretation of quantum mechanics in phase space, where quantum dynamics are represented through the creation and evolution of signed particles. A central computational challenge in this framework is the evaluation of the Wigner kernel, which generally involves highly oscillatory integrals and can become computationally demanding in time-dependent simulations. This paper proposes an analytical approximation of the Wigner kernel for one-dimensional single body quantum systems by exploiting a series-based representation of the potential function. The resulting expression provides an efficient way to approximate the Wigner kernel and the associated Gamma function, which governs the particle-generation process in the Signed Particle Formulation framework. The proposed approximation is evaluated for several Gaussian-based potential profiles, including single, double, triple, and quadruple Gaussian potentials. Numerical comparisons between the approximated and directly computed Wigner kernels and Gamma functions show that the proposed method captures the main behavior of the exact quantities while significantly reducing the computational cost. These results indicate that the proposed approximation can serve as an efficient computational component for scalable Signed Particle Formulation based quantum simulations.
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Preprint of the manuscript submitted to the Applied Mathematical Modelling Journal of Elsevier
The Signed Particle Formulation provides a particle-based interpretation of quantum mechanics in phase space, where quantum dynamics are represented through the creation and evolution of signed particles. A central computational challenge in this framework is the evaluation of the Wigner kernel, which generally involves highly oscillatory integrals and can become computationally demanding in time-dependent simulations. This paper proposes an analytical approximation of the Wigner kernel for one-dimensional single body quantum systems by exploiting a series-based representation of the potential function. The resulting expression provides an efficient way to approximate the Wigner kernel and the associated Gamma function, which governs the particle-generation process in the Signed Particle Formulation framework. The proposed approximation is evaluated for several Gaussian-based potential profiles, including single, double, triple, and quadruple Gaussian potentials. Numerical comparisons between the approximated and directly computed Wigner kernels and Gamma functions show that the proposed method captures the main behavior of the exact quantities while significantly reducing the computational cost. These results indicate that the proposed approximation can serve as an efficient computational component for scalable Signed Particle Formulation based quantum simulations.
This work belongs to a series of articles which have been dedicated to the combination of signed particles and neural networks to speed up the time-dependent simulation of quantum systems. More specifically, the suggested networks are utilized to compute the function known as the Wigner kernel. In the first paper, we suggested a network which is completely defined analytically and which does not necessitate any training process. Although very useful, this approach keeps the same complexity as the more standard finite difference methods. In the second work, we presented a different architecture which has generalization capabilities. Although more convenient in terms of computational time, it still uses a similar structure compared to the previous approach, with less neurons in the hidden layer but with the same expensive activation functions (sine functions). In this work, we focus on neural networks without any prior physics based knowledge. In more details, the network now consists of different hidden layers which are based on more common, and less computationally expensive, activation functions such as rectified linear units, and which focus on predicting only one column of the discretized Wigner kernel at a time. This new suggested architecture proves to accurately learn the transform from the space of potential functions to the space of Wigner kernels and performs very well during the simulation of quantum systems (20 times faster). Finally, we simulate a one-dimensional quantum system consisting of a wave packet impinging on a potential barrier for validation purposes. This represents one further step ahead to achieve fast and reliable simulations of time-dependent quantum systems.
Recently the use of neural networks has been introduced in the context of the signed particle formulation of quantum mechanics to rapidly and reliably compute the Wigner kernel of any provided potential. This new technique has introduced two important advantages over the more standard finite difference/element methods: 1) it reduces the amount of memory required for the simulation of a quantum system. As a matter of fact, it does not require storing the kernel in a (expensive) multi-dimensional array, and 2) a consistent speedup is obtained since now one can compute the kernel on the cells of interest only, i.e. the cells occupied by signed particles. Although this certainly represents a step forward into the direction of rapid simulations of quantum systems, it comes at a price: the number of hidden neurons is constrained by design to be equal to the number of cells of the discretized real space. It is easy to see how this limitation can quickly become an issue when very fine meshes are necessary. In this work, we continue to ameliorate this previous approach by reducing the complexity of the neural network and, consequently, by introducing an additional speedup. More specifically, we propose a new network architecture which requires less neurons than the previous approach in its hidden layer. For validation purposes, we apply this novel technique to a well known simple, but very indicative, one-dimensional quantum system consisting of a Gaussian wave packet interacting with a potential barrier. In order to clearly show the validity of our suggested approach, time-dependent comparisons with the previous technique are presented. In spite of its simpler architecture, a good agreement is observed, thus representing one step further towards fast and reliable simulations of time-dependent quantum systems.
Abstract Recently neural networks have been applied in the context of the signed particle formulation of quantum mechanics to rapidly and reliably compute the Wigner kernel of any provided potential. Important advantages were introduced, such as the reduction of the amount of memory required for the simulation of a quantum system by avoiding the storage of the kernel in a multi‐dimensional array, as well as attainment of consistent speedup by the ability to realize the computation only on the cells occupied by signed particles. An inherent limitation was the number of hidden neurons to be equal to the number of cells of the discretized real space. In this work, anew network architecture is presented, decreasing the number of neurons in its hidden layer, thereby reducing the complexity of the network and achieving an additional speedup. The approach is validated on a onedimensional quantum system consisting of a Gaussian wave packet interacting with a potential barrier.
A branching process treatment for the nonlocal Wigner pseudo-differential operator and its numerical applications in quantum dynamics is proposed and analyzed. We start from the discussion on two typical truncations of the nonlocal term, i.e., the k-truncated and y-truncated models. After introducing an auxiliary function γ(x), the (truncated) Wigner equation is reformulated into the integral formulation as well as its adjoint correspondence, both of which can be regarded as the renewal-type equations and have transparent stochastic interpretation. We prove that the moment of a branching process happens to be the solution for the adjoint equation, which connects rigorously the Wigner quantum dynamics to the stochastic branching process, and thus a sound mathematical framework for the Wigner Monte Carlo methods is established. Within the framework, the branching process for the y-truncated model recovers the popular signed particle Monte Carlo method which needs a discretization of the momentum space beforehand, whereas the k-truncated branching method allows a continuous momentum sampling. More interestingly, with the help of the rigorous connection, we find that a constant auxiliary function γ(x) ≡ γ0 performs better from the point of view of both theoretical and numerical aspects. In theory, we obtain a simple but analytical calculation formula for the growth rate of particle number, i.e., e2γ0t with t being the time. In numerics, the constant auxiliary function may facilitate the random sampling from the exponential distribution and improve the accuracy. Typical numerical experiments on the Gaussian barrier scattering validate our theoretical findings, as well as demonstrate the accuracy, the efficiency and thus the computability of the Wigner branching process.
A branching random walk algorithm for many-body Wigner equations and its numerical applications for quantum dynamics in phase space are proposed and analyzed in this paper. Using an auxiliary function, the truncated Wigner equation and its adjoint form are cast into integral formulations, which can be then reformulated into renewal-type equations with probabilistic interpretations. We prove that the first moment of a branching random walk is the solution for the adjoint equation. With the help of the additional degree of freedom offered by the auxiliary function, we are able to produce a weighted-particle implementation of the branching random walk. In contrast to existing signed-particle implementations, this weighted-particle one shows a key capacity of variance reduction by increasing the constant auxiliary function and has no time discretization errors. Several canonical numerical experiments on the 2D Gaussian barrier scattering and a 4D Helium-like system validate our theoretical findings, and demonstrate the accuracy, the efficiency, and thus the computability of the proposed weighted-particle Wigner branching random walk algorithm.
Many-body quantum-optical systems, where a collection of emitters interacts through a common electromagnetic reservoir, exhibit rich out-of-equilibrium behavior and hold promise for applications in quantum technologies. However, exact numerical simulations of their dynamics quickly become unfeasible due to the exponential growth of the Hilbert space with system size. Semiclassical, phase-space approaches -- such as the Truncated Wigner approximation (TWA) -- provide computationally efficient alternatives by capturing leading-order quantum fluctuations. In this paper, we present a comprehensive overview of how to tackle problems in many-body quantum optics using phase-space methods. We derive the exact partial differential equation governing many-body dissipative evolution in any phase-space representation and discuss the approximations that yield the dissipative TWA proposed by Mink and Fleischhauer [SciPost Phys. 15, 233 (2023)]. We find that P and Q distributions are generally suboptimal for many-body quantum optics. Additionally, we extend the formalism to calculate multi-time correlation functions, thereby broadening the scope of phase-space simulations of open spin systems to include coherence and spectral properties, as well as directional correlations of collectively radiating emitters. These developments provide valuable tools for investigating exotic light sources driven by collective dissipation, driven-dissipative phase transitions, and a wealth of many-body phenomena arising in state-of-the-art experimental platforms.