8 research outputs found
Complete quantum-inspired framework for computational fluid dynamics
Computational fluid dynamics is both an active research field and a key tool
for industrial applications. The central challenge is to simulate turbulent
flows in complex geometries, a compute-power intensive task due to the large
vector dimensions required by discretized meshes. Here, we propose a full-stack
solver for incompressible fluids with memory and runtime scaling
polylogarithmically in the mesh size. Our framework is based on matrix-product
states, a powerful compressed representation of quantum states. It is complete
in that it solves for flows around immersed objects of diverse geometries, with
non-trivial boundary conditions, and can retrieve the solution directly from
the compressed encoding, i.e. without ever passing through the expensive
dense-vector representation. These developments provide a toolbox with
potential for radically more efficient simulations of real-life fluid problems
Polynomial unconstrained binary optimisation inspired by optical simulation
We propose an algorithm inspired by optical coherent Ising machines to solve
the problem of polynomial unconstrained binary optimisation (PUBO). We
benchmark the proposed algorithm against existing PUBO algorithms on the
extended Sherrington-Kirkpatrick model and random third-degree polynomial
pseudo-Boolean functions, and observe its superior performance. We also address
instances of practically relevant computational problems such as protein
folding and electronic structure calculations with problem sizes not accessible
to existing quantum annealing devices. In particular, we successfully find the
lowest-energy conformation of lattice protein molecules containing up to eleven
amino-acids. The application of our algorithm to quantum chemistry sheds light
on the shortcomings of approximating the electronic structure problem by a PUBO
problem, which, in turn, puts into question the applicability of quantum
annealers in this context.Comment: 10 pages, 6 figure
Continuous-variable quantum tomography of high-amplitude states
Quantum state tomography is an essential component of modern quantum technology. In application to continuous-variable harmonic-oscillator systems, such as the electromagnetic field, existing tomography methods typically reconstruct the state in discrete bases, and are hence limited to states with relatively low amplitudes and energies. Here, we overcome this limitation by utilizing a feed-forward neural network to obtain the density matrix directly in the continuous position basis. An important benefit of our approach is the ability to choose specific regions in the phase space for detailed reconstruction. This results in a relatively slow scaling of the amount of resources required for the reconstruction with the state amplitude, and hence allows us to dramatically increase the range of amplitudes accessible with our method