Complex optimization problems exist across every industry, such as vehicle routing, supply chain management, risk assessment, portfolio optimization, power grid operations, and many others.
While a number of sophisticated algorithms have been developed that can solve certain optimization problems very efficiently, many real-world optimization problems remain hard to optimize despite the remarkable advancements in both algorithms and computing power over the past decades. These scenarios usually involve many variables and are computationally difficult to solve using traditional methods.
Many optimization algorithms, such as simulated annealing, parallel tempering Monte Carlo, or genetic algorithms, mimic natural processes. New optimizers have been developed that make use of quantum mechanics to accelerate optimization and escape local minima in the cost function landscape through emulating quantum tunneling.
Simulating these quantum effects on classical computers has led to the development of new types of quantum solutions that run on classical hardware, also called quantum-inspired optimization (QIO) algorithms. These algorithms allow to exploit some of the advantages of quantum computing approaches today on classical CMOS-based hardware, providing a speedup over traditional approaches.
Expanding the portfolio of QIO algorithms and solvers, Microsoft is pleased to announce that Toshiba is joining the Microsoft Quantum Network and will be offering Toshiba’s Simulated Bifurcation Machine (SBM) in Azure Quantum. Toshiba joins existing partners 1Qbit, Honeywell, IonQ, and QCI in providing services to the growing quantum ecosystem.