8 research outputs found
Annealing by simulating the coherent Ising machine
The coherent Ising machine (CIM) enables efficient sampling of low-lying
energy states of the Ising Hamiltonian with all-to-all connectivity by encoding
the spins in the amplitudes of pulsed modes in an optical parametric oscillator
(OPO). The interaction between the pulses is realized by means of
measurement-based optoelectronic feedforward which enhances the gain for
lower-energy spin configurations. We present an efficient method of simulating
the CIM on a classical computer that outperforms the CIM itself as well as the
noisy mean-field annealer in terms of both the quality of the samples and the
computational speed. It is furthermore advantageous with respect to the CIM in
that it can handle Ising Hamiltonians with arbitrary real-valued node coupling
strengths. These results illuminate the nature of the faster performance
exhibited by the CIM and may give rise to a new class of quantum-inspired
algorithms of classical annealing that can successfully compete with existing
methods
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
Fractal states of the Schwinger model
The lattice Schwinger model, the discrete version of QED in
1
+
1
dimensions, is a well-studied test bench for lattice gauge theories. Here, we study the fractal properties of this model. We reveal the self-similarity of the ground state, which allows us to develop a recurrent procedure for finding the ground-state wave functions and predicting ground-state energies. We present the results of recurrently calculating ground-state wave functions using the fractal Ansatz and automized software package for fractal image processing. In certain parameter regimes, just a few terms are enough for our recurrent procedure to predict ground-state energies close to the exact ones for several hundreds of sites. Our findings pave the way to understanding the complexity of calculating many-body wave functions in terms of their fractal properties as well as finding new links between condensed matter and high-energy lattice models
Annealing by simulating the coherent Ising machine
The coherent Ising machine (CIM) enables efficient sampling of low-lying energy states of the Ising Hamiltonian with all-to-all connectivity by encoding the spins in the amplitudes of pulsed modes in an optical parametric oscillator (OPO). The interaction between the pulses is realized by means of measurement-based optoelectronic feedforward which enhances the gain for lower-energy spin configurations. We present an efficient method of simulating the CIM on a classical computer that outperforms the CIM itself as well as the noisy mean-field annealer in terms of both the quality of the samples and the computational speed. It is furthermore advantageous with respect to the CIM in that it can handle Ising Hamiltonians with arbitrary real-valued node coupling strengths. These results illuminate the nature of the faster performance exhibited by the CIM and may give rise to a new class of quantum-inspired algorithms of classical annealing that can successfully compete with existing methods
