Throughout history, many of the most important chemical discoveries happened by accident: Ninth-Century Chinese alchemists discovered gunpowder when attempting to mix an ‘elixir of life’. In 1928, Sir Alexander Fleming interrupted his experimentation with the influenza virus for a two-week holiday and when he returned, he found that a mould had started to grow which deterred the virus. Penicillin was born!
But in today’s world, the obstacles we face are too great to be left to chance. Problems like energy and climate crises demand a more structured approach to their solutions.
To develop new solutions in these fields, we need to be able to accurately simulate the behaviour of molecules. This, however, is not a straightforward task. Predicting the properties of even simple molecules with total accuracy is beyond the capabilities of the most powerful classical computers. This is where quantum computing offers the possibility of significant advances in the coming years.
For example: simulating a simple 24-atom caffeine molecule on today’s current computers would take longer than the age of the universe.
This kind of approach is obviously not feasible for classical computers, so as a result several approximations must be made to try and simplify the complex quantum attributes of the molecules.
- With clever use of a quantum computer, we can directly model these phenomena that appear at the smallest of scales, such as Superposition, Entanglement and Interference.
- We are entering the era where quantum computers can begin to harness this advantage to simulate useful molecules.
- The team at Quandela has developed a new generation of algorithms and advanced hardware towards this end, including problem decomposition and quantum machine learning techniques.
With a fully scaled quantum device, it will be possible to simulate any valid combination of the 118 elements on the periodic table. The possibilities are truly endless.
Quandela’s Use Cases
Challenge: Simulate the properties of large molecules using a quantum computer.
Application: The efficient simulation of large molecules opens a wide range of applications for many industries. For pharmaceutical companies, accurately predicting the properties of molecules can greatly speed up the process of creating and testing new drugs.
Methodology: Quandela is working with state-of-the-art methods which combine molecule fragmentation techniques such as Density Matrix Embedding Theory (DMET) and Variational Quantum Eigensolvers (VQEs). The marriage of these techniques means that large molecules can be broken into smaller pieces which can be accurately simulated on our quantum processing units.
Clients: Alysophil and MBDA
Challenge (What): Polymer classification using quantum machine learning.
Application (Why): The dimensionality of quantum chemistry applications often grow exponentially. Quandela and Alysophil have developed a hybrid classical-quantum algorithm to cluster polymers given specific features, with the goal of large-scale simulations.
Methodology (How) : We use a pre-trained classical network to extract the essential features which describe the data. These features are analysed by a quantum neural network which classifies the polymers. This is a transfer learning process which uses a hybrid quantum-classical neural network. Preliminary results agree with classical simulations, paving the way for further developments of the quantum algorithm.