Sophus Technology Inc., a leader in supply chain optimization and decision intelligence, today announced the upcoming beta release of its next-generation optimization engine, the Sophus Quantum Solver, scheduled for beta in January 2026 and general release by the end of Q1 2026.
Designed to overcome the performance and scalability limits of traditional MILP-based solvers, the Quantum Solver delivers 50–100× faster solve times on large-scale, integer-heavy supply chain models while expanding the class of problems that can be solved in practice.
Addressing a Growing Industry Bottleneck
As global supply chains grow more complex, many organizations face critical limitations with existing optimization tools:
- Excessive solve times, often stretching into hours or days and accompanied expensive cloud solving bills.
- Forced simplification of models, requiring teams to split problems into disconnected sub-models
- Entire classes of operational problems deemed “not solvable” due to computational constraints
These limitations force companies to compromise realism, speed, or decision quality, and the Quantum Solver removes that trade-off.
A New Solver Architecture
Rather than relying solely on brute-force mathematical enumeration, the Sophus Quantum Solver takes a different approach, it:
- Interprets the supply chain as a connected system rather than isolated equations
- Learns patterns in how decisions interact across locations, time, and cost
- Uses those patterns to guide the search toward promising solutions earlier
- Continuously improves how it explores complex problems as scale increases
The result is a solver that:
- Reaches high-quality answers much faster
- Remains stable as models grow larger and more detailed
- Avoids the “combinatorial explosion” that slows down traditional approaches
Proven Performance at Enterprise Scale
An example benchmark testing demonstrates the solver’s impact:
- A model with 550,000 integer variables, 53 time periods, 1,678 customers, and 86 DC locations
- Solve time to a 2% optimal gap:
- Traditional optimization tool: ~82 minutes
- Sophus Quantum Solver: ~25 seconds
The performance improvement remains robust as model size increases, enabling practical use for:
- Global network design and restructuring
- Daily-level production optimization
- Daily-level replenishment and inventory optimization
- Complex fixed-cost, changeover, and operational constraint modeling
Unlocking a New Decision Optimization Cadence
By reducing runtime from hours to seconds, the Quantum Solver allows organizations to:
- Run full-network models more frequently
- Test more scenarios without delay
- Move advanced optimization from strategic studies into daily operational decision-making
This shift in runtime unlocks a new cadence for decision-making: more frequent runs, more scenarios, and better decisions with fewer compromises.
As organizations push toward operational agility and cost discipline at the same time, the Sophus Quantum Solver provides the speed and scale needed to optimize end-to-end networks in real time.
Beta launches January 2026, with general availability by end of Q1 2026.
