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Solving Logistic Problems Using Noisy Intermediate-Scale Quantum (NISQ) Devices

One of the most interesting challenges in modern quantum computing is how to handle the intrinsic problem of noise in quantum devices. This problem is at the basis of what is known as the “noisy intermediate-scale quantum” (NISQ) era. Currently, there are several approaches to reduce noise in quantum hardware devices, such as quantum error correction techniques and classical post-processing methods. Somewhat diverging with the general approach, in this project we will explore the possibility of adaptively using noise in a meaningful way to address hard optimization problems using quantum systems.

It is well-known that some metaheuristic methods applied to logistic problems have benefited from achieving a good balance between deterministic and stochastic processes, sometimes modeled as “noise” or randomly-biased “temperature-dependent” updates. The idea is now to investigate how we can develop metaheuristic approaches using the intrinsic noise of NISQ devices and benefit from its natural existence in these systems. We will first try to characterize a model of the noise in current NISQ devices, and then apply this model to one or more (classical) metaheuristics. The resulting algorithms can be seen as an instance of quantum memetic computing, i.e., a form of hybrid algorithms that combine both classical and quantum computations to perform both local and global optimization. We will then validate the proposed approach on industrially relevant scheduling and vehicle routing problems, on which we will perform a comparison against classical algorithms implemented in commercial MILP solvers.

Our project is supporting a 3-year fully-funded PhD project co-funded by 12thLevel ( and the Q@TN Laboratory (, and will be conducted in collaboration with The University of Newcastle, Australia. The prospective candidate will be primarily based at the University of Trento, Italy, but he or she will also have the opportunity to spend a period in Canberra, Australia, working with renowned experts in the fields of metaheuristics and quantum physics.

The ideal candidate has a Master’s in one of the following areas: computer science, quantum physics, applied mathematics, or related topics, and should have a strong interest in optimization and quantum theory. Previous experience in numerical optimization and quantum computing are considered a plus.

The working language for the PhD is English.

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