Quantum Computers Excel in Memory Efficiency, Not Just Speed

Theoretical computer scientists at Sandia National Laboratories and Boston University have made a groundbreaking discovery about quantum computers. Contrary to popular belief, the value of a quantum computer isn’t just that it can solve certain problems faster than a normal one, but also that it uses far less memory. This revelation could help researchers find more real-world applications for the rapidly advancing technology.

The team has found that quantum computers are unrivaled at solving an advanced math problem called maximum directed cut. Interestingly, they proved that quantum computers are not faster than regular computers at this task. Instead, they are exponentially more efficient with their memory usage, especially when data arrives in a stream. This is significant because memory is crucial for any computer. The more memory it has, the bigger problems it can solve. For quantum computers, which store information in qubits, space is particularly important because building quantum computers with lots of qubits is challenging.

This discovery is being hailed as the first exponential quantum advantage for a natural streaming problem. The focus of quantum advantage research has mostly been on achieving time advantage. However, this research points to a different area where quantum advantage is possible, shifting attention to other attributes like efficiency. This could help scientists find more practical uses for quantum computers.

The maximum directed cut problem is significant because it is a natural problem, meaning it’s a problem of independent interest that people were already studying in the classical setting. As this kind of problem grows more complex, regular computers need more memory. But quantum computers don’t. They are exponentially more efficient with their memory usage. This is the first known exponential quantum space advantage for any natural streaming problem. It also constitutes the first unconditional exponential quantum resource advantage for approximating a discrete optimization problem in any setting. This discovery could potentially open up new avenues in the field of quantum computing.

Read more: phys.org


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