Optimization

Unlock better decisions across complex industrial systems with quantum computing

Every major industry depends on optimization.

From balancing energy grids and scheduling manufacturing plants to routing global supply chains and maximizing industrial assets, organizations continuously solve complex decision-making problems where even small improvements can generate significant operational value.

As these systems become larger, more interconnected and more dynamic, classical optimization approaches increasingly rely on approximations and heuristics. Quantum computing introduces a new computational approach designed to explore complex solution spaces and address optimization challenges beyond the practical limits of existing methods.

Who this applies to

This solution is particularly relevant for organizations in:

• Energy and utilities
• Manufacturing
• Automotive
• Aerospace
• Logistics and supply chain
• Transportation
• Telecommunications
• Infrastructure management
• Financial services

 

The challenge

Modern industrial systems involve thousands or millions of interconnected decisions.

Examples include:

• Which power plants should operate at each moment?
• How should production lines be scheduled?
• What is the most efficient route for thousands of deliveries?
• How should limited resources be allocated across competing priorities?
• How can assets be utilized more effectively?

As the number of variables grows, the number of possible combinations increases exponentially.

Organizations therefore rely on classical optimization techniques that simplify the problem to deliver solutions within acceptable timeframes.

These approaches are highly valuable but they often require trade-offs between:

• Solution quality
• Computational cost
• Response time
• Operational flexibility

In high-value industrial environments, even small improvements can translate into millions of dollars in savings.

 

Business impact

Quantum-enhanced optimization has the potential to improve operational performance across critical industries.

Potential outcomes include:

• Reduced operating costs
• Improved asset utilization
• Increased production efficiency
• Lower energy consumption
• More resilient supply chains
• Faster operational decision-making
• Better resource allocation
• Improved sustainability performance

For industries operating complex networks, optimization is not a theoretical challenge: it is a direct driver of competitiveness.

 

Evaluate your optimization problem for quantum advantage

Identify which of your operational challenges could benefit from quantum-enhanced optimization approaches.

→ Speak with our Quantum Engineering Team

 

Why classical optimization reaches its limits

Many industrial optimization problems belong to the class of combinatorial optimization problems, where the number of possible solutions grows exponentially as system complexity increases.

Classical algorithms address this challenge through sophisticated approaches including:

• Heuristics
• Approximation methods
• Branch-and-bound techniques
• Metaheuristics
• Machine learning-based optimization

However, these approaches often require compromises.

Growing solution spaces

A small increase in the number of variables can create an enormous increase in possible configurations, making exhaustive search impossible.

Discrete decision making

Many industrial decisions involve binary or discrete choices:

• On/off decisions
• Scheduling sequences
• Routing options
• Resource assignments

These structures are fundamentally different from continuous optimization problems.

Approximate solutions

To meet operational deadlines, companies frequently accept near-optimal solutions.

While practical, these solutions may leave valuable efficiency gains unrealized.

Dynamic environments

Real-world systems constantly change due to:

• Demand fluctuations
• Supply disruptions
• Equipment availability
• Weather conditions
• Market dynamics

Maintaining optimal decisions becomes increasingly difficult as complexity grows.

 

The quantum approach

Quantum computing introduces new methods for representing and solving complex optimization problems.

Rather than replacing existing optimization systems, quantum processors can be integrated into hybrid workflows combining:

• Classical optimization solvers
• AI models
• High-performance computing
• Quantum algorithms

Quantum algorithms are particularly suited to problems involving large discrete search spaces and complex constraints.

As quantum hardware matures, these approaches may enable organizations to explore optimization landscapes that become increasingly challenging for classical methods.

Representative use cases

Energy grid optimization
Balance increasingly complex energy networks by optimizing:

• Power generation scheduling
• Renewable energy integration
• Energy storage management
• Grid stability

Potential benefits include reduced operational costs and improved utilization of renewable resources.

Manufacturing scheduling
Optimize production planning across complex manufacturing environments.
Applications include:

• Job-shop scheduling
• Production sequencing
• Machine allocation
• Workforce planning

Potential outcomes:

• Increased factory throughput
• Reduced downtime
• Improved resource utilization

Logistics and supply chain optimization
Improve decision-making across global supply networks.

Applications include:

• Vehicle routing
• Warehouse operations
• Inventory optimization
• Distribution planning

Potential benefits:

• Reduced transportation costs
• Faster deliveries
• More resilient supply chains

Aerospace and transportation
Optimize complex operational networks including:

• Fleet management
• Aircraft scheduling
• Maintenance planning
• Crew allocation

Improved optimization can increase asset availability while reducing operational costs.

Infrastructure and network optimization
Manage large-scale interconnected systems such as:

• Telecommunications networks
• Transportation infrastructure
• Industrial assets

Quantum-enhanced approaches may enable better decisions in increasingly complex environments.

Assess your optimization challenges

Our quantum application engineers can analyze your operational problems and identify where quantum optimization could create measurable value.

→ Book a technical assessment

 

Technical deep dive

Industrial optimization problems are often formulated as mathematical models involving discrete variables and complex constraints.

Examples include:

• Quadratic Unconstrained Binary Optimization (QUBO)
• Constraint satisfaction problems
• Graph optimization
• Scheduling problems

These mathematical structures map naturally to quantum formulations.

Current quantum approaches include:

Variational Quantum Algorithms (VQAs)
Hybrid quantum-classical algorithms that use parameterized quantum circuits combined with classical optimization methods to explore complex solution spaces.

Quantum approximate optimization approaches
Algorithms such as QAOA explore combinations of quantum states to identify high-quality solutions for specific optimization problems.

Quantum search methods
Algorithms based on quantum search principles can provide theoretical speedups for specific problem classes.

As fault-tolerant quantum computers mature, more advanced quantum optimization techniques are expected to become available for industrial-scale applications.