Noise Robustness of Quantum Relaxation for Combinatorial Optimization
Relaxation is a common way for dealing with combinatorial optimization problems.
Resource Placement for Rate and Fidelity Maximization in Quantum Networks
Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step toward enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement […]
Backtesting Quantum Computing Algorithms for Portfolio Optimization
In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work establishes a methodology for backtesting classical and quantum algorithms […]
Quantum Approximate Bayesian Optimization Algorithms With Two Mixers and Uncertainty Quantification
The searching efficiency of the quantum approximate optimization algorithm is dependent on both the classical and quantum sides of the algorithm. Recently, a quantum approximate Bayesian optimization algorithm (QABOA) that includes two mixers was developed, where surrogate-based Bayesian optimization is applied to improve the sampling efficiency of the classical optimizer. A continuous-time quantum walk mixer […]
Bayesian Optimization for QAOA
The quantum approximate optimization algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems. In fact, it relies on a classical subroutine to optimize the parameters of a quantum circuit. In this article, we present a Bayesian optimization procedure to fulfill this optimization task, and we investigate its performance in […]
Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)
Collateral optimization refers to the systematic allocation of financial assets to satisfy obligations or secure transactions while simultaneously minimizing costs and optimizing the usage of available resources. This involves assessing the number of characteristics, such as the cost of funding and quality of the underlying assets to ascertain the optimal collateral quantity to be posted […]
Improving Urban Traffic Mobility via a Versatile Quantum Annealing Model
The growth of cities and the resulting increase in vehicular traffic pose significant challenges to the environment and citizens’ quality of life. To address these challenges, a new algorithm has been proposed that leverages the quantum annealing paradigm and D-wave’s machines to optimize the control of traffic lights in cities. The algorithm considers traffic information […]
Qubit Reduction and Quantum Speedup for Wireless Channel Assignment Problem
In this article, we propose a novel method of formulating an NP-hard wireless channel assignment problem as a higher-order unconstrained binary optimization (HUBO), where the Grover adaptive search (GAS) is used to provide a quadratic speedup for solving the problem. The conventional method relies on a one-hot encoding of the channel indices, resulting in a […]
Analysis of the Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in the Presence of Noisy Channels
The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry. The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency. Classical tools and methods provide good approximations to reach the optimal global […]
Quantum Topology Optimization via Quantum Annealing
We present a quantum annealing-based solution method for topology optimization (TO). In particular, we consider TO in a more general setting, i.e., applied to structures of continuum domains where designs are represented as distributed functions, referred to as continuum TO problems. According to the problem’s properties and structure, we formulate appropriate subproblems that can be […]