Hundreds of Quantum Applications

Dixit, V.V., Niu, C. Quantum computing for transport network design problems. Sci Rep13, 12267 (2023).

Transport network design problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in a redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP is generally modelled as a bi-level problem, which is known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as a bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved using quantum annealing on a D-Wave quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides significant computational benefit. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.

Satellite image acquisition scheduling is a problem that is omnipresent in the earth observation field; its goal is to find the optimal subset of images to be taken during a given orbit pass under a set of constraints. This problem, which can be modeled via combinatorial optimization, has been dealt with many times by the artificial intelligence and operations research communities. However, despite its inherent interest, it has been scarcely studied through the quantum computing paradigm. Taking this situation as motivation, we present in this paper two QUBO formulations for the problem, using different approaches to handle the non-trivial constraints. We compare the formulations experimentally over 20 problem instances using three quantum annealers currently available from D-Wave, as well as one of its hybrid solvers. Fourteen of the tested instances have been obtained from the well-known SPOT5 benchmark, while the remaining six have been generated ad-hoc for this study. Our results show that the formulation and the ancilla handling technique is crucial to solve the problem successfully. Finally, we also provide practical guidelines on the size limits of problem instances that can be realistically solved on current quantum computers.

Earth observation satellites (EOS) collect vital data for various applications such as weather forecasting, disaster management, environmental monitoring, etc. Maximizing the value of this data requires designing optimal EOS missions to capture targets with high business value or priority while satisfying complex constraints such as storage capacity, energy limits, weather, etc. However, traditional computing methods often struggle with the complexity of optimizing EOS mission schedules, leading to suboptimal target selection and reduced data collection efficiency.

In this paper, we demonstrate the potential of our quantum algorithm to optimize EOS mission schedules and improve the efficiency of multiple EOS in real-time. The aim is to maximize the acquisition of high-priority targets with significant business value within the constraints of limited resources. We evaluated the performance of our quantum algorithm by comparing it with two classical optimization algorithms: simulated annealing and Gurobi optimizer. Our quantum algorithm outperformed the Gurobi optimizer by 23.46%.

Accenture is working with telecom companies on the application of quantum computers to complex business problems. Watch this talk and find out more.

Addressing the world’s climate emergency is an uphill battle, requiring a multifaceted approach including optimal deployment of green-energy alternatives. Such undertakings often involve computationally-expensive optimisation of black-box models in a continuous parameter space. We observe good performance on the DW-2000Q QPU - even using default settings - and higher sensitivity of performance to number of samples and annealing time for the Advantage QPU. These results are promising and potentially justify further investment in hardware, software, and optimisation research, in particular, in the area of solvers for dense QUBOs

The possibility of using quantum computers for electronic structure calculations has opened up a promising avenue for computational chemistry. Towards this direction, numerous algorithmic advances have been made in the last five years. The potential of quantum annealers, which are the prototypes of adiabatic quantum computers, is yet to be fully explored. In this work, we demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience.

This paper discusses how to enhance often time-consuming RBM training processes based on the commonly used Gibbs sampling using an improved version of quantum sampling. To prevent overfitting, they propose some solutions that help acquire less probable samples from the distribution by adjusting D-wave control and embedding parameters.