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IEEE International Conference on High Performance Computing, Data, and Analytics

Unitary qubit lattice simulations of multiscale phenomena in quantum turbulence

Vahala, Soe, Zhang, Yepez, Carter, Ziegeler

Published Jan 1, 2011• 0 citations
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A unitary qubit lattice algorithm, which scales almost perfectly to the full number of cores available (e.g., 216000 cores on a CRAY XT5), is used to examine quantum turbulence and its interrelationship to classical turbulence with production runs on grids up to 57603. The maximal grids achievable by conventional CFD for quantum turbulence is just 20483, and artificial dissipation had to be introduced. Our unitary algorithms preserve the Hamiltonian structure of the Gross-Pitaevskii equation which describes quantum turbulence in a zero-temperature Bose-Einstein condensate (BEC). As a result, parameter regimes have been uncovered which exhibit very short Poincare recurrence time, as well as a strong triple cascade structure in the kinetic energy spectrum. Moreover, a detailed examination of the incompressible kinetic energy spectrum has revealed for the first time within a turbulence simulation the κ−17/5 quantum Kelvin wave cascade. By generalizing the unitary entanglement operators on the 2 qubits, a finite temperature BEC system is examined. These unitary qubit lattice algorithms are directly applicable to quantum computers as they become available.
Numerical simulation of turbulent fluid dynamics needs to either parametrize turbulence—which introduces large uncertainties—or explicitly resolve the smallest scales—which is prohibitively expensive. Here, we provide evidence through analytic bounds and numerical studies that a potential quantum speedup can be achieved to simulate fluid dynamics using quantum computing. Specifically, we provide a lattice Boltzmann formulation of fluid dynamics for which we give evidence that low-order Carleman linearization is much more accurate than previously believed for these systems. This is achieved via a combination of reformulating the Navier-Stokes nonlinearity ( u · ∇ u ) to lattice-Boltzmann nonlinearity ( u 2 ) and accurately linearizing the dynamical equations, which effectively trades nonlinearity for additional degrees of freedom that add negligible expense in the quantum solver. Based on this, we apply a quantum algorithm for simulating the Carleman-linearized lattice Boltzmann equation and provide evidence that its cost scales logarithmically with system size compared with polynomial scaling in the best known classical algorithms. In this paper, we suggest that a quantum advantage may exist for simulating fluid dynamics, paving the way for simulating nonlinear multiscale transport phenomena in a wide range of disciplines using quantum computing.
The nonlinear Schrödinger equation (NLSE) governs a broad class of wave phenomena, including deep-water waves, quantum turbulence, and solitons. The multiscale spatiotemporal coupling inherent in these systems imposes severe computational bottlenecks on classical high-fidelity numerical simulations. While quantum computing offers the potential for exponential speedup, its unitary dynamics pose a fundamental challenge to solve the NLSE. We propose a quantum framework based on the Lax-pair scattering for solving the 1D NLSE. Specifically, the physical field is first mapped into the spectral space via a quantum direct scattering circuit. Following a decoupled linear time evolution, the physical solution is reconstructed through an inverse scattering transform utilizing the quantum singular value transformation. Since the temporal evolution is performed analytically in the scattering domain, the framework bypasses iterative time stepping, rendering it highly advantageous for long-time simulations. To demonstrate the accuracy and noise resilience of this approach, we simulate a Gaussian wave packet under quantum noise, two-soliton collisions, breather dynamics, and modulational instability on a quantum emulator.