Created by quantumz.io

mkdir build && cd build
cmake .. 
cmake --build .

Binaries are one-solver-anneal and one-solver-exhaustive created in the app folder. Binary for tests is located in tests.

The binary named one-solver-anneal allows to solve a QUBO problem using the simulated annealing algorithm whereas one-solver-exhaustive uses the brute force search method.

In the tests folder the application called main-tests is build.

In order to obtain help about the applications arguments use the --help switch. For example, the command ./one-solver-anneal --help prints:

Allowed options:
  --help                                produce help message
  --input arg                           input file
  --output arg                          output file
  --num-iter arg (=100)                 number of iterations of the algorithm
  --num-tries arg (=100)                number of trajectories to try
  --schedule-type arg (=geometric)      type of beta schedule tu use, either 
                                        linear or geometric
  --beta-min arg (=0.10000000000000001) minimum value of beta in the annealing 
                                        schedule (default 0.1)
  --beta-max arg (=1)                   maximum value of beta in the annealing 
                                        schedule (default 1.0)
  --device-type arg (=host)             device type to use (cpu, gpu or host)

The command ./one-solver-exhaustive --help prints:

Allowed options:
  --help                    produce help message
  --input arg               input file
  --output arg              output file
  --device-type arg (=host) device type to use (cpu, gpu or host)

The following applications call

./one-solver-exhaustive --input ../../examples/test1.qubo --output result1.qubo

executes the program for the model described in test1.qubo file and the obtained results are written to result1.qubo file.

The project's goal is to develop a spin-glass (i.e., Ising model) solver based on a family of simulated annealing algorithms for large system sizes and an exhaustive search for small Ising instances. This solver will be implemented using an oneAPI development stack to utilize massive parallel capabilities of present-day computing architectures.

It is well established that NP-hard problems can be solved by cleverly mapping them to the classical Ising Hamiltonians (i.e., spin-glasses). With the rapid development of both quantum and classical annealers, the scientific and computer engineering communities have intertwined once again in their efforts to reformulate many real-life combinatorial optimization problems using the ideal of interacting spin variables (i.e., spins). Various dedicated devices and computer chips are being developed currently to tackle the very problem of finding the low-energy configurations (i.e., spectrum) of the Ising models. For instance, Fujitsu and Hitachi have produced their digital and analog classical annealers. In stark contrast, D-Wave Inc. has manufactured its quantum annealers, which promises to solve the Ising instances using the law of quantum physics; the famous adiabatic theorem to be more specific.

Although there are many sophisticated methods (e.g., tensor networks, simulated quantum annealing, path integrals, etc.) to approach the aforementioned problems, not that many of them can benefit from the High-Performance Computing (HPC) available today. On the other hand, fundamental techniques such as the Brute-Force (that simply executes an exhaustive search) or simulated annealing (that tries to mimic a famous physical process of annealing known in the material science) can take advantage of highly parallel architectures (graphics card, many-cores). This project is devoted precisely for that purpose; i.e., we aim to provide conceptually simple solvers that can be executed concurrently on present-day, massively parallel, classical architectures in an agnostic manner.

Literature:

1. Brute-forcing spin-glass problems with CUDA
2. Demonstration of a scaling advantage for a quantum annealer over simulated annealing
3. Parallel in time dynamics with quantum annealers