Toshiba launches new SQBM+ quantum-inspired optimization supplier on Azure Quantum

There are numerous optimization issues in finance, logistics, biotechnology, and AI the place it’s essential discover the very best mixture from an unlimited vary of selections. Combinatorial optimization issues similar to these are troublesome to unravel at excessive velocity and at an inexpensive computational value with current computer systems as a result of the variety of combinatorial patterns will increase exponentially as the size of the issue grows.

One option to deal with these combinatorial optimization issues is to map them to a binary illustration referred to as an Ising mannequin, after which use a specialised optimizer to search out the bottom state of this Ising system.

Toshiba’s new Simulated Quantum Bifurcation Machine+ (SQBM+) on Azure Quantum, primarily based on its Simulated Bifurcation Machine (SBM), is an Ising mannequin solver that may resolve advanced and large-scale combinatorial optimization issues with as much as 100,000 variables at excessive velocity. 

Toshiba has adopted a brand new method, impressed by their quantum computing analysis, that considerably improves the velocity, accuracy, and scale of their SBM. There are two algorithms accessible by means of the SQBM+ supplier in Azure Quantum: the high-speed Ballistic Simulated Bifurcation algorithm (bSB) designed to discover a good resolution in a short while; and the high-accuracy Discrete Simulated Bifurcation algorithm (dSB) which finds extra correct options at a calculation velocity that surpasses that of different machines (each classical and quantum). An auto-tune perform has additionally been carried out that may auto-select which algorithm to make use of primarily based on the issue submitted. These algorithms are optimized mechanically to offer the very best efficiency on GPU {hardware} deployed within the Azure cloud.

Customers can choose one in all these algorithms particularly, or just enable the auto choice perform to decide on on their behalf. This selection is made by supplying values for the “algo” and “auto” parameters throughout solver instantiation utilizing the Azure Quantum Python SDK. Extra data is obtainable within the Toshiba SQBM+ supplier documentation, and a pattern exhibiting how to decide on between the completely different algorithm choices might be discovered on the qio-samples repo.

“The core know-how of SQBM+ is SBM, which is software program that makes use of at the moment accessible computer systems and achieves high-accuracy approximate options for advanced and large-scale issues in a brief period of time. The end result is the flexibility to unravel Ising issues of as much as 100,000 variables—at roughly a 10X enchancment over our current PoC service. And that is now all simply accessed by means of the Azure Quantum cloud platform,“—Shunsuke Okada, Company Senior Vice President and Chief Digital Officer of Toshiba.

Azure Quantum prospects can entry SQBM+ by including the supplier to their Quantum Workspace and choosing one of many accessible pricing plans: “Be taught & Develop” (experimentation) and “Efficiency at scale” (business use).  

Since becoming a member of the Azure Quantum Community in September 2020, Toshiba has constantly improved its quantum-inspired optimization solvers know-how. Clients who wish to resolve combinatorial optimization issues together with dynamic portfolio and danger administration, molecular design, and optimizing routing, partitioning, and scheduling in a variety of fields can apply SQBM+ at present,  harnessing the GPU sources within the Azure cloud by means of Azure Quantum.

Be taught extra and get began at present with Toshiba’s SQBM+ on Azure Quantum.