System Bits: Oct. 16

Solving the quantum verification problem
UC Berkeley doctoral candidate Urmila Mahadev spent 8 years in graduate school solving one of the most basic questions in quantum computation, which is how to know whether a quantum computer has done anything quantum at all, according to Quanta Magazine.

In her paper, Mahadev presents the first protocol allowing a classical computer to interactively verify the result of an efficient quantum computation. She wrote this was achieved by constructing a measurement protocol, which enables a classical verifier to use a quantum prover as a trusted measurement device, and that the protocol forces the prover to behave as follows: the prover must construct an n qubit state of his choice, measure each qubit in the Hadamard or standard basis as directed by the verifier, and report the measurement results to the verifier. The soundness of this protocol is enforced based on the assumption that the learning with errors problem is computationally intractable for efficient quantum machines.

According to the article, “Mahadev’s verification protocol — along with the random-number generator and the blind encryption method — depends on the assumption that quantum computers cannot crack LWE. At present, LWE is widely regarded as a leading candidate for post-quantum cryptography, and it may soon be adopted by the National Institute of Standards and Technology as its new cryptographic standard, to replace the ones a quantum computer could break. That doesn’t guarantee that it really is secure against quantum computers,” but so far it’s solid, and evidence has not been found that it’s likely to be breakable.

Mahadev’s protocol is unlikely to be implemented in a real quantum computer in the immediate future because for the time being, the protocol requires too much computing power to be practical but this could change in the coming years, as quantum computers get larger and researchers streamline the protocol.

Further, although, today some say Mahadev’s protocol probably won’t be feasible within, say, the next five years, it’s not completely in fantasyland either, given how quickly the field is now moving, and could be possible sooner rather than later.

Theoretical computer scientists see Mahadev’s unification of quantum computation and cryptography as the initial exploration of what will hopefully prove a rich vein of ideas.

Space-borne quantum source for secure communication
Soon, powerful quantum computers will be able to easily crack conventional mathematically encrypted codes, and entangled photons generated by a space-borne quantum source could enable hack-proof key exchange for ultra high security applications. To this point, a Fraunhofer research team has developed a high-performance quantum source robust enough for deployment in space. They report they aim to launch the first European quantum satellite in some four years’ time.

The quantum source generates entangled photons and transmits them to Earth from a satellite, where they serve to distribute secure keys for encrypting data.
Source: Fraunhofer IOF

No larger than a bread box, this device has been put through its paces, enduring vast leaps in temperatures from minus 40 to plus 60 degrees celsius, exposure to cold and heat in vacuum, and jarring rodeo rides on a triple-axis vibrating platform. Throughout this excruciating campaign, the device had to demonstrate its unwavering robustness and high performance, the researchers report. And, when this quantum source passed the last of a grueling battery of stress tests conducted to the European Space Agency’s stringent standards, it was deemed space-worthy meaning this rugged little box would survive a rocket launch and hold up under harsh off-planet conditions.

Specifically, Fraunhofer Institute for Applied Optics and Precision Engineering IOF researchers have developed a remarkably stable yet powerful quantum source capable of generating 300,000 entangled photon pairs per second when the light from a laser beam hits a non-linear crystal. These twinned light particles enable sensitive messages to be securely encrypted.

To learn how it works, see their explanation here.

Quantum computer algorithms for faster, better data analysis
According to Purdue University researchers, every two seconds, sensors measuring the U.S.’ electrical grid collect 3 petabytes of data – the equivalent of 3 million gigabytes. Data analysis on this scale is a challenge when crucial information is stored in an inaccessible database but the team at Purdue is working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility.

The researchers reported they are using data from the U.S. Department of Energy National Labs’ sensors, called phasor measurement units, that collect information on the electrical power grid about voltages, currents and power generation. Because these values can vary, keeping the power grid stable involves continuously monitoring the sensors.

A Purdue research team led by Sabre Kais, professor of chemical physics, is combining quantum algorithms with classical computing to speed up database accessibility.
Source: Purdue University

Sabre Kais, a professor of chemical physics and principal investigator, is leading the effort to develop new quantum algorithms for computing the extensive data generated by the electrical grid.

Alex Pothen, professor of computer science and co-investigator on the project said, “Non-quantum algorithms that are used to analyze the data can predict the state of the grid, but as more and more phasor measurement units are deployed in the electrical network, we need faster algorithms. Quantum algorithms for data analysis have the potential to speed up the computations substantially in a theoretical sense, but great challenges remain in achieving quantum computers that can process such large amounts of data.”

The research team believes its method has potential for a number of practical applications, such as helping industries optimize their supply-chain and logistics management. It could also lead to new chemical and material discovery using an artificial neural network known as a quantum Boltzmann machine. This kind of neural network is used for machine learning and data analysis.

“We have already developed a hybrid quantum algorithm employing a quantum Boltzmann machine to obtain accurate electronic structure calculations,” Kais said. “We have proof of concept showing results for small molecular systems, which will allow us to screen molecules and accelerate the discovery of new materials.”