Reimagining Computation: How Coherent Ising Machines Are Solving the Unsolvable
In a world where data is growing exponentially and complexity is the norm, some of the most valuable problems in business and science are also the hardest to solve. From optimizing delivery routes across congested cities to designing molecules for new medicines, we frequently run into computational walls that even the fastest classical computers struggle to break through.
What Are Ising Machines and Why Do They Matter?
Ising machines are a new type of computer designed to solve very tough problems that traditional computers cannot handle. These problems often involve finding the best combination of choices, like planning delivery routes, managing energy grids, or scheduling flights, where trying every possible option would take far too long.
The idea behind Ising machines comes from physics. They are based on the Ising model, which was initially created to understand how tiny magnetic particles called spins interact with each other. Scientists could explain patterns seen in magnetic materials by studying how large groups of these particles behave together. Scientists realised that this same model could represent a wide range of real-world challenges. Instead of solving these problems one step at a time like regular computers, Ising machines use physical processes like light, electricity, or even quantum effects to explore many possible answers simultaneously, making them very powerful for optimization tasks, where you need to find the best outcome from a vast number of possibilities.
Researchers worldwide, including those at the University of Tokyo and Stanford, have built versions of Ising machines using lasers, electronic circuits and even quantum light [Inagaki et al., 2016; Haribara et al., 2017]. These machines are already being tested in areas like finance, supply chain management, and drug discovery.
In short, Ising machines are a new tool for solving some of the world’s most complex and significant problems and they do it by letting physics do the heavy lifting.
The Limits of Traditional Computing
Classical computers have served us well, but were never designed for some of today’s most complex challenges. Many real-world problems fall into a category called combinatorial optimization, which involves finding the best solution from an enormous number of possibilities.
While GPUs excel at parallel processing, they struggle with combinatorial optimization problems due to their fundamentally sequential decision logic. Traditional optimization algorithms also involve sequential decision logic, leading to limited speed-ups from using GPUs.
As the problem size grows, GPUs require massive computational overhead, often failing to scale efficiently, leading to significantly higher energy consumption than physics-inspired approaches like Coherent Ising Machines.
Let’s consider a simplified example of a 10-minute food delivery platform that needs to assign delivery executives to orders. This scenario illustrates a classic combinatorial optimization problem, which aims to find the best assignment among many possible combinations.
- • With 3 agents and 3 deliveries, there are 27 possible combinations.
- • With 3 agents and 10 deliveries, the number of combinations skyrockets to over 59,000.
- • And with 10 agents and 10 deliveries, the possibilities explode to 10 billion.
This exponential growth in complexity with the number of variables underscores the need for specialised hardware like Ising machines, which are designed to solve such large-scale optimization problems efficiently.
Enter the Coherent Ising Machine
Coherent Ising Machines (CIMs) are specialised physical systems that solve combinatorial optimization problems by mapping them to the Ising Hamiltonian. Traditionally, CIMs use networks of optical parametric oscillators (OPOs), where the phase state of each oscillator represents an Ising spin [Yang et. al., 2013; Yamamura et. al., 2017; Bohm et. al., 2019; Prabhakar et. al., 2021]. The system evolves to minimize the overall energy of the encoded problem.
At Quanfluence, we extend this concept with a hybrid CIM architecture that separates the linear and nonlinear components of the computation across distinct hardware domains for improved flexibility and scalability. In our implementation, matrix multiplication and spin-spin coupling operations typically handled optically in conventional CIMs are offloaded to high-speed FPGAs, enabling precise, programmable control of large and dense interaction matrices. Meanwhile, the nonlinear dynamics and bifurcation behaviour crucial for analog annealing are retained in the photonic subsystem, where coherent light evolution preserves the energy-minimizing dynamics.
This separation of responsibilities enables our CIM to scale beyond all-optical implementations’ size and sparsity constraints while maintaining the inherent advantages of photonic coherence and analog state evolution.
We have introduced architectural, algorithmic, and control-level enhancements that significantly extend their computational capabilities. Our research has focused on optimizing computational precision, convergence stability, and problem scalability.
We have developed and integrated custom control algorithms that dynamically tune feedback gain, phase stability, and annealing schedules during runtime. These enhancements lead to more accurate convergence on ground-state or near-ground-state solutions, especially for large and highly degenerate energy landscapes. Our implementation includes hybrid classical-quantum heuristics that precondition the problem encoding and adaptively reshape the Ising energy landscape in real-time to guide the machine toward optimal states more reliably.
Beyond hardware tuning, we’ve built a full software stack that allows developers to define problem instances in QUBO or Ising format and deploy them efficiently on our CIM platform using APIs or a Python SDK. This enables seamless integration with practical applications in combinatorial optimization, such as portfolio optimization, constraint satisfaction, scheduling, and circuit mapping.
Quanfluence’s CIM architecture represents a novel computing method and a refined computational framework designed to meet the robustness and repeatability demands of production-scale deployments.
Our goal? To put this transformative technology in the hands of industries that need it most.
Real-World Applications: Where CIMs Shine
CIMs aren’t just lab curiosities. They are already demonstrating promise across multiple sectors.
Supply Chain
Solve dynamic delivery, inventory, and routing problems at scale, leading to faster fulfilment and lower logistics costs.
Finance
Run real-time portfolio rebalancing and scenario planning with tighter risk control and regulatory compliance.
Factory Production optimization
Optimize factory production by solving complex scheduling and resource allocation problems faster than traditional methods, reducing downtime and increasing throughput.
Machine Learning
Perform tasks like feature selection, graph clustering, and neural architecture search, where optimization lies at the core of performance.
Telecommunications
Optimize the configuration of network nodes and links for maximum throughput, minimum latency, or better load balancing. Assigning frequency bands to different users or services in a way that minimizes interference and maximizes efficient use of available spectrum.
Transportation
Schedule and route electric vehicles (EVs) for optimal charging patterns across multiple stations to reduce wait times and manage grid load.
Aerospace,Aviation
Allocate aircraft to routes or tasks efficiently to maximize fleet utilization while minimizing delays and operational costs. Assign pilots and crew members to flights while adhering to regulations around rest periods, shift lengths, and qualifications.
Healthcare
Assign nurses to shifts in a way that balances workloads, meets patient care requirements, and respects work-hour regulations. Organize patient appointments or treatments to optimize resource usage and minimize patient wait times.
These are not a comprehensive list of domains where use cases for Ising machines exist. Stay tuned for more case studies and example problems in future blogs.
The Road Ahead
Coherent Ising Machines are part of a broader wave of quantum-inspired computing, where physical systems offer a new way to solve problems that resist conventional techniques. As technologies mature and demand for efficient problem-solving grows, CIMs are poised to be a foundational element of next-generation computing infrastructure.
For machine access, demos, or to schedule a call or a visit, please get in touch with us at info@quanfluence.com
Additional reading and references
- Wang, Z., Marandi, A., Wen, K., Byer, L., Yamamoto, Y., Coherent Ising machine based on degenerate optical parametric oscillators. Phys. Rev.. A., Vol. 88, No. 6. (2013)
- Yamamura, A., Aihara, K. and Yamamoto, Y., Quantum model for coherent Ising machines: Discrete-time measurement feedback formulation, Phys. Rev.. A, Vol. 96, No. 5. (2017)
- Inagaki, T., et. al., A coherent Ising machine for 2000-node optimization problems, Science, Vol. 354, Issue 6312 (2016)
- Haribara, Y., et. al, Performance evaluation of coherent Ising machines against classical neural networks, Quantum Science and Technology, Vol. 2, No. 4. (2017)
- Böhm, F., Verschaffelt, G. & Van der Sande, G., A poor man’s coherent Ising machine based on opto-electronic feedback systems for solving optimization problems. Nat Comm. 10, 3538 (2019).
- Böhm, F., Vaerenbergh, T.V., Verschaffelt, G. et al., Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity.Comm. Phys 4, 149 (2021).
- Prabhakar, A., Shah, P., Gautham, U., Natarajan, V., Ramesh, V., Chandrachoodan, N., Tayur, S., Optimization with photonic wave based annealers, The royal society publishing, rspa.royalsocietypublishing.org, Proc R Soc A. (2021).