Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem.

/pdf/traffic-flow-optimization-using-a-quantum-annealer-1jd19jw9mo.pdf

Traffic flow optimization using a quantum annealer

Quantum computing could greatly speed up machine learning techniques that rely on sampling complex probability distributions, but limitations prevent demonstrations of feasibility. A new technique implemented on a quantum annealer surpasses these limitations and shows how quantum computing can assist in machine learning tasks.

https://link.aps.org/pdf/10.1103/PhysRevX.7.041052

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of the real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum applications. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem.

We investigate a hybrid quantum-classical solution method to the mean-variance portfolio optimization problems. Starting from real financial data statistics and following the principles of the Modern Portfolio Theory, we generate parametrized samples of portfolio optimization problems that can be related to quadratic binary optimization forms programmable in the analog D-Wave Quantum Annealer 2000Q. The instances are also solvable by an industry-established Genetic Algorithm approach, which we use as a classical benchmark. We investigate several options to run the quantum computation optimally, ultimately discovering that the best results in terms of expected time-to-solution as a function of number of variables for the hardest instances set are obtained by seeding the quantum annealer with a solution candidate found by a greedy local search and then performing a reverse annealing protocol. The optimized reverse annealing protocol is found to be more than 100 times faster than the corresponding forward quantum annealing on average.

Reverse Quantum Annealing Approach to Portfolio Optimization Problems.

We present the mapping of a class of simplified air traffic management problems (strategic conflict resolution) to quadratic unconstrained Boolean optimization problems. The mapping is performed through an original representation of the conflict-resolution problem in terms of a conflict graph, where the nodes of the graph represent flights and the edges represent a potential conflict between flights. The representation allows a natural decomposition of a real-world instance related to wind-optimal trajectories over the Atlantic Ocean into smaller subproblems that can be discretized and are amenable to be programmed in quantum annealers. In this paper, we tested the new programming techniques, and we benchmark the hardness of the instances using both classical solvers and the D-Wave 2X and D-Wave 2000Q quantum chip. The preliminary results show that for reasonable modeling choices, the most challenging subproblems which are programmable in the current devices are solved to optimality with 99% of probability within a second of annealing time.

Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management

A quantum annealing solver for the renowned job-shop scheduling problem (JSP) is presented in detail. After formulating the problem as a time-indexed quadratic unconstrained binary optimization problem, several pre-processing and graph embedding strategies are employed to compile optimally parametrized families of the JSP for scheduling instances of up to six jobs and six machines on the D-Wave Systems Vesuvius processor. Problem simplifications and partitioning algorithms, including variable pruning and running strategies that consider tailored binary searches, are discussed and the results from the processor are compared against state-of-the-art global-optimum solvers.

/pdf/quantum-annealing-implementation-of-job-shop-scheduling-24xq8cejv2.pdf

Galo Rojo

Papers

Quantum Annealing Implementation of Job-Shop Scheduling