There are many optimization problems in finance, logistics, biotechnology and AI where you have to find the best combination from a huge range of choices. Combinatorial optimization problems such as these are difficult to solve at high speed and at reasonable computational cost with existing computers, because the number of combinatorial patterns increases exponentially as the scale of the problem grows.
One way to approach these combinatorial optimization problems is to map them into a binary representation called an Ising model and then use a specialized optimizer to find the ground state of this Ising system.
Toshiba’s new Simulated Quantum Bifurcation Machine+ (SQBM+) on Azure Quantum, based on its Simulated Bifurcation Machine (SBM)is an Ising model solver that can solve complex and large-scale combinatorial optimization problems with up to 100,000 variables at high speed.
Toshiba has taken a new approach, inspired by their research on quantum computers, that significantly improves the speed, accuracy and scale of their SBM. Two algorithms are available through the SQBM+ provider in Azure Quantum: the high-speed Ballistic Simulated Bifurcation (bSB) algorithm designed to find a good solution in a short time; and the highly accurate Discrete Simulated Bifurcation Algorithm (dSB) that finds more precise solutions with a computational speed that exceeds that of other machines (both classical and quantum). An auto-tune feature has also been implemented that automatically selects which algorithm to use based on the submitted issue. These algorithms are automatically optimized to provide the best performance on GPU hardware deployed in the Azure cloud.
Users can select one of these algorithms specifically, or simply allow the automatic selection feature to choose on their behalf. This choice is made by specifying values for the “algo” and “auto” parameters while instantiating the solver using the Azure Quantum Python SDK. More information can be found in the Toshiba SQBM+ Provider Documentationand an example showing how to choose between the different algorithm options can be found at the qio samples repo†
“The core technology of SQBM+ is SBM, which is software that uses currently available computers and achieves highly accurate approximation solutions to complex and large-scale problems in a short time. The result is the ability to solve Ising problems of up to 100,000 variables†with an improvement of approximately 10x over our existing PoC service. And all this is now easily accessible through the Azure Quantum cloud platform,††Shunsuke Okada, Corporate Senior Vice President and Chief Digital Officer of Toshiba.
Azure Quantum customers can access SQBM+ by adding the provider to their Quantum Workspace and selecting one of the available pricing plans: “Learn & Develop” (experiment) and “Performance at scale” (commercial use).
Since joining the Azure Quantum Network in September 2020, Toshiba has been continuously improving its quantum-inspired optimization solvers technology. Customers looking to solve combinatorial optimization problems, including dynamic portfolio and risk management, molecular design, and optimizing routing, partitioning, and scheduling across a range of fields can deploy SQBM+ today, leveraging the GPU resources in the Azure cloud via Azure Quantum.
Learn more and get started with today Toshiba’s SQBM+ on Azure Quantum†