JIJ Launches SQOA Solver in IBM Quantum Functions Catalog to Tackle Combinatorial Optimization Beyond Qubit Counts
quantumcomputingreport.com Nov 14, 2026

JIJ Launches SQOA Solver in IBM Quantum Functions Catalog to Tackle Combinatorial Optimization Beyond Qubit Counts

AI-summarised brief · reviewed before publication

Tokyo‑based JIJ Inc. has added its SQOA Solver to the IBM Quantum Functions Catalog, allowing users of IBM Quantum Premium, Flex, and On‑Prem plans to tackle combinatorial optimization problems with more decision variables than physical qubits. The solver, built on the Sampling‑based Quantum Optimization Algorithm with Quantum Relaxation (SQOA‑QR), compresses variables onto shared qubits and uses classical subspace diagonalization to avoid iterative variational loops. It accepts OMMX instances via JIJ’s JijModeling 2 library and outputs feasible classical solutions after post‑processing.

💡 Why It Matters

  • · By enabling larger problem sizes on current quantum hardware, the SQOA Solver expands practical use cases for quantum optimization, bridging the gap between classical models and noisy intermediate‑scale quantum devices.