As fault-tolerant quantum computing continues to mature, enterprises can already use quantum-inspired computing techniques to increase utilization of existing hardware.
IBM Ventures has announced an investment in quantum-inspired engineering company BQP, formerly BosonQ Psi, to help expand its BQPhy product from engineering design into operational deployments. Venn10 Capital and existing investor Monta Vista Capital also participated, bringing BQP’s total funding to $8 million. IBM Ventures did not disclose the size of its investment.
BQPhy is a software platform designed to accelerate engineering simulations and optimization workloads on existing computing infrastructure.
Emily Fontaine, Global Head of IBM Ventures had this to say: “What stood out with BQP is that users don’t have to change how they work to get quantum-accelerated results. Their proven track record of developing software that helps enterprises build a practical path toward hybrid quantum-classical computing is what gives IBM Ventures confidence in making this investment,”
Why this investment matters
In 2023, BQP joined the IBM Quantum Network startup program in a bid to build quantum-based simulations and proof of concept projects.
Since then, BQP has moved beyond research concepts into production environments for industries like aerospace, transportation and defense.
The most notable development that the company has achieved is the BQPhy solver. A solver is a software tool designed to find a solution to a specific mathematical, logical, or engineering problem.
BQPhy is built specifically for this purpose to tackle optimization and simulation scenarios for both commercial and research purposes.
BQPhy offers three types of solvers: the Optimization Solver for engineering and logistics, the Physics-based Solver for multi-physics simulations, and finally the Data-Driven Solver for predictive analytics.
Of these three, only the Optimization Solver is already in commercial use, while the others are still in research and development.
Notice that these are areas with computationally intensive loads that would require high-performance computing (HPC) environments or GPUs in order to feasibly run them.
BQP says BQPhy can deliver results up to 10 times faster on current HPC infrastructure and make better use of GPU floating-point capacity that conventional physics solvers can leave idle.
Though BQP claims these performance gains, its commercially available software currently runs on classical CPUs, GPUs and HPC infrastructure rather than relying on a QPU (Quantum Processing Unit). That’s an advantage for near-term deployment, but it also means claims about quantum advantage shouldn’t be interpreted as demonstrated advantage from a physical quantum computer.
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The enterprise takeaway
BQPhy can be accessed through MATLAB and Python, meaning developers don’t have to learn completely new tools or domain-specific languages to use it.
For MATLAB, users can access it through the add-ons library, activate the license and then use the BQPhy solver in their existing workflow.
For the Python integration, the team offers an SDK which developers can install, authenticate and then start using.
IBM says that building these tools which developers can use without overhauling their current infrastructure helps “customers improve performance without asking users to abandon the tools they already trust.”
For enterprises, BQPhy’s near-term appeal is less about accessing a quantum computer and more about getting greater performance from infrastructure they already operate. BQP is betting that organizations can adopt quantum-inspired techniques now while preparing engineering workloads for future hybrid quantum-classical systems.
Read more: RIKEN ROQUO Supercomputer Shows What Hybrid Quantum Computing Actually Needs