Use Cases

Business challenge
Financial institutions face increasingly complex problems in portfolio construction, risk simulation, asset pricing and security selection. As markets become more interconnected, decision-makers need to evaluate more scenarios, more constraints and more sources of uncertainty.

Limitations of classical approaches
Classical methods remain highly effective for many financial applications, especially standard portfolio optimisation and pricing tasks. However, some problems become difficult when the number of assets, scenarios or constraints grows sharply. Large-scale Monte Carlo simulations, combinatorial security selection and high-dimensional risk models can be costly, slow or difficult to adapt in real time.

Quantum potential
Quantum methods could eventually help accelerate simulation, improve the exploration of complex optimisation spaces and support more efficient approaches to pricing and risk analysis. In investment management, one promising direction is not necessarily replacing classical portfolio optimisation, but helping identify which securities should enter the investable universe before classical methods determine optimal weights.

Timeline
Near term: hybrid quantum-classical experiments on narrowly defined optimisation, simulation and pricing problems.
- Medium term: stronger evidence of quantum advantage may emerge for selected use cases in risk modelling, derivatives pricing and security selection.
- Long term: broader integration will depend on hardware maturity, error correction and the ability to demonstrate business value beyond proof-of-concept experiments.

References
EDHEC Quantum Finance Research Programme; Rebentrost et al. (2018); Egger et al. (2020); World Economic Forum, Embracing the Quantum Economy.

Business challenge
Energy systems involve highly complex optimisation and simulation problems, from grid management and renewable integration to battery design, carbon capture and advanced materials discovery.

Limitations of classical approaches
Classical computing already supports many energy applications, but some molecular simulations and large-scale network optimisation problems remain highly demanding. Approximations are often necessary, which can slow down innovation in areas such as clean energy materials, storage and industrial decarbonisation.

Quantum potential
Quantum simulation could help model molecules and materials with greater precision, supporting the discovery of improved batteries, catalysts, solar materials or carbon capture processes. Quantum optimisation may also support better energy distribution, storage management and grid resilience, particularly as systems become more decentralised and renewables-dependent.

Timeline
- Near term: exploratory work on hybrid quantum-classical methods for materials and optimisation problems.
- Medium term: more robust applications in materials science, battery chemistry and energy system modelling.
- Long term: possible use of quantum-enhanced optimisation for complex, real-time grid management.

References
Cao et al. (2019); McArdle et al. (2020); World Economic Forum, Embracing the Quantum Economy.

Business challenge
Today’s widely used public-key cryptography, including RSA and elliptic-curve cryptography, could become vulnerable to sufficiently powerful quantum computers. This creates a long-term risk for sensitive data, digital infrastructure and critical systems.

Limitations of classical approaches
Post-quantum cryptography standards are emerging, but migration is complex. Organisations must identify exposed systems, assess data with long confidentiality lifetimes and update infrastructure without disrupting operations. Many are not yet prepared for this transition.

Quantum potential
Post-quantum cryptography offers quantum-resistant protection for classical networks and should become a priority for organisations handling sensitive or long-lived data. Quantum key distribution may also play a role in specific high-security environments, especially for critical infrastructure, government and strategic communications. The key issue for most organisations is not immediate deployment of quantum security tools, but readiness planning.

Timeline
Near term: cryptographic inventory, risk assessment and migration planning towards post-quantum cryptography.
Medium term: implementation of post-quantum standards across priority systems and vendors.
Long term: selective deployment of quantum-secure communication infrastructures where the use case justifies the cost and complexity.

References
NIST post-quantum cryptography standards; EDHEC-EURECOM Quantum Information Programme; World Economic Forum, Embracing the Quantum Economy.

Business challenge
Automotive, aerospace and transportation industries face complex challenges in materials design, aerodynamic modelling, route optimisation, fleet management, predictive maintenance and autonomous systems. These sectors must improve performance, safety and sustainability while managing increasingly complex engineering and operational constraints.

Limitations of classical approaches
Classical simulation and optimisation tools are already highly advanced, but some problems remain computationally intensive, especially when they involve complex materials, fluid dynamics, large transport networks or real-time decision-making. As systems become more connected and autonomous, the number of variables and scenarios to evaluate continues to grow.

Quantum potential
Quantum simulation could support the discovery of lighter, stronger and more sustainable materials for aircraft, vehicles and batteries. Quantum optimisation may help improve routing, scheduling, fleet allocation and traffic management. In aerospace, quantum sensing could also contribute to more precise navigation and positioning, particularly in environments where satellite-based systems are limited or unavailable.

Timeline
- Near term: hybrid quantum-classical experiments for route optimisation, scheduling and selected materials problems.
- Medium term: quantum-enhanced simulation for advanced materials, batteries and aerospace components.
- Long term: broader integration into transport networks, autonomous mobility systems and high-precision aerospace navigation.

References
World Economic Forum, Embracing the Quantum Economy; quantum optimisation literature; quantum sensing literature for navigation and positioning; materials simulation research.

Business challenge
Pharmaceutical and healthcare organisations face highly complex challenges in drug discovery, molecular simulation, medical imaging and disease detection. Developing new therapies requires exploring vast chemical spaces, modelling biological interactions and reducing the time and cost of early-stage research.

Limitations of classical approaches
Classical computational methods have made major progress, particularly with AI and high-performance computing. However, accurately simulating molecular structures and interactions remains extremely demanding. In healthcare, improving the sensitivity and precision of diagnostic tools also requires technologies capable of detecting very small biological or physical signals.

Quantum potential
Quantum simulation could eventually support more accurate modelling of molecules and biological systems, helping accelerate early-stage drug discovery and materials design for medical applications. Quantum sensing may also improve diagnostic imaging and enable earlier, more precise disease detection. In the near term, the most realistic opportunities are likely to come from hybrid approaches combining classical computing, AI and emerging quantum methods.

Timeline
- Near term: exploratory work combining AI, high-performance computing and quantum-inspired or hybrid methods for drug discovery and molecular modelling.
- Medium term: quantum simulation for selected molecular systems and improved sensing technologies for diagnostics and imaging.
- Long term: broader use of quantum-enhanced tools in pharmaceutical R&D, precision medicine and advanced healthcare diagnostics.

References
World Economic Forum, Embracing the Quantum Economy; Cao et al. (2019); McArdle et al. (2020); quantum sensing literature for biomedical imaging and diagnostics.

Business challenge
Supply chains, routing, scheduling and resource allocation involve combinatorial optimisation problems whose complexity grows rapidly with the number of variables, constraints and disruptions.

Limitations of classical approaches
Classical heuristics can provide strong approximate solutions, and in many cases they remain the most practical option. However, large-scale logistics problems can become difficult to optimise when decisions must be updated dynamically in response to demand shifts, delays, capacity constraints or geopolitical disruptions.

Quantum potential
Quantum optimisation approaches, including quantum annealing and gate-based algorithms such as QAOA, may eventually help explore large solution spaces more efficiently. The most realistic near-term opportunity is likely to come from hybrid approaches, where quantum methods complement classical optimisation rather than replace it.

Timeline
- Near term: hybrid experiments on specific routing, scheduling and allocation problems.
- Medium term: more advanced use cases in dynamic supply chain reconfiguration and large-scale optimisation.
- Long term: broader deployment will depend on demonstrated advantage over mature classical methods.

References
Farhi et al. (2014); QAOA literature; D-Wave case studies; World Economic Forum, Embracing the Quantum Economy.