The Incredible Power of Quantum Memory

New Ways to Use Quantum Memory

Reducing the Number of Snapshots Needed

Sitan Chen and his coauthors showed that just two copies of the quantum state could exponentially reduce the number of times you need to take a snapshot of your quantum system.

Quantum memory is almost always worth the investment. Most algorithms require exponentially fewer snapshots when using quantum memory, making it easier for researchers to understand complex quantum systems and potentially achieve quantum advantage.

Researchers are exploring new ways that quantum computers will be able to reveal the secrets of complex quantum systems.

Quantum systems—collections of particles that follow the counterintuitive rules of quantum mechanics—are difficult to study. Heisenberg’s uncertainty principle, a cornerstone of quantum theory, states it’s impossible to simultaneously measure a particle’s exact position and its speed—pretty important information for understanding what’s going on.

In order to study, say, a particular collection of electrons, researchers have to be clever about it.

They might take a box of electrons, poke at it in various ways, then take a snapshot of what it looks like at the end. In doing so, they hope to reconstruct the internal quantum dynamics at work.

This in itself could be a way to prove quantum advantage. “It allows us to, in the more near term, already achieve that kind of quantum advantage,” said Hsin-Yuan Huang, a physicist at Google Quantum AI.

Researchers are excited about the practical benefits too, as the new results make it easier for researchers to understand complex quantum systems.

“We’re edging closer to things people would really want to measure in these physical systems,” said Jarrod McClean, a computer scientist at Google Quantum AI.

A few years ago, a team based at the California Institute of Technology demonstrated that certain algorithms that use quantum memory require exponentially fewer snapshots than algorithms that don’t use it.

Their method was a major advance, but it required a relatively large amount of quantum memory. That’s something of a deal-breaker, because as a practical matter, quantum memory is hard to come by.

A quantum computer is made of interconnected quantum bits called qubits, and qubits can be used for computation or memory but not both.

Now, two independent teams have come up with ways of getting by with far less quantum memory. In the first paper, Sitan Chen, a computer scientist at Harvard University, and his coauthors showed that just two copies of the quantum state could exponentially reduce the number of times you need to take a snapshot of your quantum system.

Understanding Complex Quantum Systems

Researchers are excited about the practical benefits too, as the new results make it easier for researchers to understand complex quantum systems. “We’re edging closer to things people would really want to measure in these physical systems,” said Jarrod McClean, a computer scientist at Google Quantum AI.

Using Quantum Memory to Reconstruct Quantum States

But there’s a catch: They can’t measure all the system’s properties at the same time. So they iterate. They’ll start with their system, poke, then measure. Then they’ll do it again. Every iteration, they’ll measure some new set of properties. Build together enough snapshots, and machine learning algorithms can help reconstruct the full properties of the original system—or at least get really close.

This is a tedious process. But in theory, quantum computers could help. These machines, which work according to quantum rules, have the potential to be much better than ordinary computers at modeling the workings of quantum systems. They can also store information not in classic binary memory, but in a more complex form called quantum memory.

Reducing the Number of Snapshots

A few years ago, a team based at the California Institute of Technology demonstrated that certain algorithms that use quantum memory require exponentially fewer snapshots than algorithms that don’t use it. Their method was a major advance, but it required a relatively large amount of quantum memory.