Quanfluence Achieves Breakthrough Supply Chain Optimization with Coherent Ising Machine
Harnessing Quantum-Inspired Optimization for Supply Chain Allocation
Quanfluence’s Coherent Ising Machine has delivered the best optimization results while evaluating profit, cost and SKU selection-showing a net profit of USD 12.75M, the highest value obtained when evaluating against competing models, in a newly published research study, highlighting the real-world power of quantum-inspired photonic computing.
The paper titled “Quantum Similarity-Driven QUBO Framework for Multi-Period Supply Chain Allocation using Time-Multiplexed Coherent Ising Machines and Simulated Quantum Annealing” is published on arXiv.
The study demonstrates the power of photonics-based computation in solving large-scale, real-world problems. This work showcases how quantum-inspired hardware is already delivering tangible impact today, while quantum computers get better and more usable.
The Problem: Complex, Dynamic Supply Chains
Modern supply chains must manage thousands of stock-keeping units (SKUs) across multiple time periods, balancing profitability, capacity constraints, and product diversity. These allocation problems belong to the class of NP-hard combinatorial optimizations, where traditional methods often struggle to find efficient, high-quality solutions.
The research focuses on modelling this challenge as a Quadratic Unconstrained Binary Optimization (QUBO) problem — a formulation that maps naturally to both classical and quantum optimization hardware.
The Approach: Quantum-Inspired Optimization
The authors propose a hybrid optimization framework combining quantum concepts with classical methods, anchored around three innovations:
- Quantum-Derived Similarity Kernel – A quantum embedding method creates a similarity metric between SKUs, encouraging diversity in selection and reducing redundancy.
- Slack-Bit Encoding for Constraints – This encoding technique maintains strict adherence to per-period capacity limits, ensuring feasible allocations across time horizons.
- Execution on Coherent Ising Machines (CIMs) – The model is tested on time-multiplexed CIM hardware and compared with simulated quantum annealing to evaluate efficiency, scalability, and robustness.
Findings and Insights
The study demonstrates that
- quantum-inspired solvers, particularly the Quanfluence Coherent Ising Machine, can efficiently handle high-dimensional, constraint-driven optimization problems.
- The hybrid approach scales to thousands of binary variables and millions of interactions — showing clear potential for industrial-grade deployment.
- Rather than replacing classical systems, this research highlights how quantum-inspired methods can complement and enhance existing optimization workflows.
Readers interested in the technical and numerical results can explore them in the full paper.
Why It Matters
At Quanfluence, our mission is to translate breakthroughs in photonics and quantum computing into real-world solutions. This research underscores the promise of Coherent Ising Machines — one of our focus areas — in delivering tangible performance gains in optimization tasks that underpin use cases in industries such as logistics, telecom networks, automobile industry, finance- to name just a few.
It reaffirms our commitment to advancing the state of quantum-inspired computing as contributors to the global research community.
Looking Forward
Quantum-inspired optimization represents a powerful bridge between today’s computing capabilities and tomorrow’s quantum future. As industries face increasingly complex decision problems, such approaches can deliver measurable efficiency, sustainability, and cost benefits.
At Quanfluence, we invite enterprises, researchers, and technology partners to explore how our quantum-inspired solution- available both on cloud and on premise- can enhance their optimization workflows.
Reach out to us at info@quanfluence.com to collaborate or learn more about our Coherent Ising Machine-based solutions.