Our dedicated algorithms team focuses on the development of algorithms and protocols that fully exploit the informatics potential of photons to bring ground-breaking applications within reach.
From prototyping with our Perceval simulator, to implementation on our general-purpose chips, or eventually to the design of purpose-built and optimised photonic circuits. Our quantum algorithms team designs and develops industrial solutions for wide-ranging applications: from advanced simulations of physical systems useful for pharmaceutical and material design, to quantum machine learning approaches to networks and optimisation.
Challenge: Global energy demand is increasing rapidly. An exponential increase in the quantity of data from this sector can be seen year after year.
Solution: Quantum computing can have an impact on every stage of the supply chain of energy, from generation to transmission.
Method: Structural mechanics problems for the safe storage of energy and differential equations solvers for energy flow are among Quandela’s existing solutions.
AUTOMOTIVE & AEROSPACE
Challenge: Mobility is heading for a Connected, Autonomous, Shared, Electric network for cars and drones. This presents a substantial new list of challenges.
Solution: Designing new batteries, sensors and traffic routes have been noted as areas of interest for quantum computers.
Method: Quantum computers will allow the simulation of advanced materials and handling of vast amounts of data to greatly speed up the development of these technologies.
Challenge: Predicting the properties of even simple molecules with total accuracy is beyond the capabilities of the most powerful classical computers.
Solution: To develop new solutions in these fields we need to be able to accurately simulate complex molecules.
Method: Quandela has developed cutting-edge solutions in this field with advanced simulation and problem-decomposition methods.
Challenge: Computer-aided drug design techniques have hit the limits of classical computation, interactions on the atomic level are critical.
Solution: Simulations of known and new molecules have the potential to be much faster and more accurate with Quantum Computing.
Method: Quantum computers use their unique properties to efficiently simulate the behaviour of new medicines.
Challenge: From breaking our best encryption methods, to making those which are completely uncrackable; Quantum Computers are set to completely transform cryptography.
Solution: Quantum-safe cryptography relies on being able to generate truly random numbers, a task that is impossible for traditional computers.
Method: The Entropy protocol on our full-stack quantum computer allows the creation of quantum-certifiable random numbers.
Challenge: Fast and dynamic optimisation of routes troubles the logistics industry; the travelling salesman problem is notoriously hard to simulate.
Solution: Quantum Computers have the potential to optimise supply-chain processes with real-time dynamics, out of reach of today’s supercomputers.
Method: Many quantum algorithms have been proposed for combinatorial optimisation problems, from quantum neural networks to graph optimisation.
Challenge: Financial services are intensive users of computing power. The systems needed for simulation are highly complex with a huge number of parameters.
Solution: Experimental quantum systems are already being used to test and develop financial services use cases in such applications as targeting and prediction, asset trading optimization, and risk profiling.
Method: Quantum Monte-Carlo simulations and optimised neural networks are proposed to overhaul computational finance methods.