Google's Quantum Advantage Demonstrates Credibility of Quantum Machine Learning

Google's Quantum Advantage in Learning from Experiments

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Quantum Machine Learning (QML) is going to be a game-changer.

Hey Guys,

This topic is a bit more specialized, but gives you some context for my new Newsletter on Substack called "Quantum Foundry". I'm actively looking for guest contributors and student mentors in the field.

Google likes Quantum phrases? First it was “Quantum Supremacy ”. Now it’s?Quantum Advantage. I guess nobody wants to feel left out. Certainly not IBM.

Science Magazine ?just published?Google ?Quantum AI's paper on Quantum advantage in learning from experiments! Read the blog post by?Jarrod McClean ?and?Hsin-Yuan (Robert) Huang .

Posted by Jarrod McClean, Staff Research Scientist, Google Quantum AI, and Hsin-Yuan Huang, Graduate Student, Caltech.

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Read Google's AI Blog June 22nd

In “Quantum Advantage in Learning from Experiments ”, a collaboration with researchers at Caltech, Harvard, Berkeley, and Microsoft?published in Science , Google shows that a quantum learning agent can perform exponentially better than a classical learning agent at many tasks.

Google Admits Superiority of Quantum Machine Learning (QML)

Using Google’s quantum computer,?Sycamore , they demonstrate the tremendous advantage that a?quantum machine learning ?(QML) algorithm has over the best possible classical algorithm.

Unlike previous quantum advantage demonstrations, no advances in classical computing power could overcome this gap. This is the first demonstration of a provable exponential advantage in learning about quantum systems that is robust even on today's?noisy hardware .

Researchers in Canada have conducted a quantum computing experiment that they claim completes a calculation in just a fraction of a second that would take a conventional computer 9,000 years, claiming their own version of “Quantum Advantage”. Published in Published in?Nature ?at the start of June, 2022, there’s a lot of “Quantum Advantage” going around.

Hsin-Yuan is pretty cool though: (check out the?video )

This talk will occur on June 24th, 2022. So Bookmark it! You can get a feel for it?here.

Then you have your Military types who think the U.S. has?some kind of a “Quantum Advantage” ?over China and that we should guard against losing it. Their thesis is that Research centers around the globe are now under pressure, pushing state-of-the-art technologies.

To this end, subsequently, to prevent any technology erosion, President Joe Biden May 4 signed an “Executive Order on Enhancing the National Quantum Initiative Advisory Committee,” which reports directly to the White House. We will see how that actually goes! But let’s double back to the Google paper.

Quantum Speedup

According to Google AI, QML combines the best of both quantum computing and the lesser-known field of?quantum sensing .

Quantum computers will likely offer?exponential improvements over classical systems ?for certain problems, but to realize their potential, researchers first need to scale up the number of qubits and to improve?quantum error correction .

What’s more, the exponential speed-up over classical algorithms promised by quantum computers relies on a big, unproven assumption about so-called “complexity classes ” of problems — namely, that the class of problems that can be solved on a quantum computer is larger than those that can be solved on a classical computer.

Google thinks it seems like a reasonable assumption, and yet, no one has proven it. Until it's proven,?every claim of quantum advantage will come with an asterisk: that it can do better than any?known?classical algorithm.

Quantum Sensors

Quantum sensors, on the other hand, are?already being used for some high-precision measurements ?and offer modest (and proven) advantages over classical sensors.

Some quantum sensors work by exploiting quantum correlations between particles to extract more information about a system than it otherwise could have. For example, scientists can use a collection of?N?atoms to measure aspects of the atoms’ environment like the surrounding magnetic fields.

Typically the sensitivity to the field that the atoms can measure scales with the square root of?N. But if one uses quantum entanglement to create a complex web of correlations between the atoms, then one can improve the scaling to be proportional to?N. But as with most quantum sensing protocols, this quadratic speed-up over classical sensors is the best one can ever do.

Quantum Machine Learning Could Prove Significant

Enter QML, a technology that?straddles the line between quantum computers and quantum sensors. QML algorithms make computations that are aided by quantum data. Instead of measuring the quantum state, a quantum computer can store quantum data and implement a QML algorithm to process the data without collapsing it. And when this data is limited, a QML algorithm can squeeze exponentially more information out of each piece it receives when considering particular tasks.

Read the full Paper

Comparison of a classical machine learning algorithm and a quantum machine learning algorithm. The classical machine learning algorithm measures a quantum system, then performs classical computations on the classical data it acquires to learn about the system. The quantum machine learning algorithm, on the other hand, interacts with the quantum states produced by the system, giving it a quantum advantage over the CML.

Conclusion

This experimental work represents the first demonstrated exponential advantage in quantum machine learning. And, distinct from a computational advantage, when limiting the number of samples from the quantum state, this type of quantum learning advantage cannot be challenged, even by unlimited classical computing resources.

So far, the technique has only been used in a contrived, “proof-of-principle” experiment, where the quantum state is deliberately produced and the researchers pretend not to know what it is. To use these techniques to make quantum-enhanced measurements in a real experiment, Google will first need to work?on current quantum sensor technology?and methods to faithfully transfer quantum states to a quantum computer. But the fact that today’s quantum computers can already process this information to squeeze out an exponential advantage in learning bodes well for the future of quantum machine learning.

The Quantum Problems of the 2020s and our Times

Quantum computers will likely offer?exponential improvements over classical systems ?for certain problems, but to realize their potential, researchers first need to scale up the number of qubits and to improve?quantum error correction .

I like what?Robert , and Quantum-mane are getting at there.

After training the neural network with simulation data,?Google AI found?that the quantum learning agent needed exponentially fewer experiments to reach a prediction accuracy of 70% — equating to 10,000 times fewer measurements when the system size was 20 qubits. The total number of qubits used was 40 since two copies were stored at once.

So guys, what happens when we have quantum computers of 1,000,000 qubits eh?

Check out Robert’s GitHub here:

https://hsinyuan-huang.github.io/

A quantum computer attains computational advantage?when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage.

My opinion: it’s only a matter of time.

Google AI has proved that, in various tasks,?quantum machines can learn from exponentially fewer experiments?than those required in conventional experiments. That my friends, while obvious, is also of considerable importance for the future of A.I. and our future.

Thanks for reading! Credit to Quantum Science Communicator?Katherine McCormick ?for translating their paper into a language I could at least partially understand.

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I've started a Newsletter for quantum computing news, if this field interests you, you can subscribe?here . I cannot continue to write without tips, sponsorship and community support. (follow the link below).

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