Simulations of reality would require less memory on a quantum computer than on a classical computer has shown.
The study by Karoline Wiesner from the School of Mathematics and Centre for Complexity Sciences , together with researchers from the Centre for Quantum Technologies in Singapore, demonstrates a new way in which computers based on quantum physics could beat the performance of classical computers.†
When confronted with a complicated system, scientists typically strive to identify underlying simplicity which is then articulated as natural laws and fundamental principles.† However, complex systems often seem immune to this approach, making it difficult to extract underlying principles.
Researchers have discovered that complex systems can be less complex than originally thought if they allow quantum physics to help: quantum models of complex systems are simpler and predict their behaviour more efficiently than classical models.
A good measure of the complexity of a particular system or process is how predictable it is.† For example, the outcome of a fair coin toss is inherently unpredictable and any resources (beyond a random guess) spent on predicting it would be wasted.† Therefore, the complexity of such a process is zero.
Other systems are quite different, for example neural spike sequences (which indicate how sensory and other information is represented in the brain) or protein conformational dynamics (how proteins - the molecules that facilitate biological functions - undergo structural rearrangement).† These systems have memory and are predictable to some extent; they are more complex than a coin toss.