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Researchers from the Los Alamos National Laboratory have developed new open-source software that could evaluate quantum annealers down to the individual qubit level aside from also characterizing noise.

Usually, when institutions and organizations purchase a new piece of equipment, such as a classical supercomputer, this new item is first verified and validated. It involves running the equipment against a set of benchmarks. This drove the team of Carleton Coffrin, a computer scientist and artificial intelligence expert, to develop open-source software that could do a similar job, but this time for quantum annealers.

"We didn't have good analogs for that on the quantum annealing computers. For quantum annealing, our new a Quantum Annealing Single-qubit Assessment, or QASA, protocol gives us one tool for acceptance testing," Coffrin said of their new project. The Los Alamos National Laboratory team published the report "Single-Qubit Fidelity Assessment of Quantum Annealing Hardware" on the latest IEEE Transactions on Quantum Engineering.

Latest D-Wave 2000 Qubit Processor
(Photo: Steve Jurvetson from Menlo Park, the USA via Wikimedia Commons)

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A Readily-Available Tool for Researchers, Engineers

The QASA open-source software is a part of the ongoing project "Accelerating Combinatorial Optimization with Noisy Analog Hardware. It is currently available on the online software repository GitHub, on the LANL-ANSI/ QASA webpage. The new quantum annealer assessment tool runs in parallel for all qubits in the machine. This execution allows QASA to generate a detailed evaluation of the annealer through critical metrics about each of the qubits or the quantum computer analog for bits in a classical computer. Parameters include effective temperature, noise, and biases. The QASA single-qubit approach, in the project's breakthrough, can be executed in parallel for all individual qubits in a quantum annealer.

"The QASA protocol could eventually find a wide range of uses, such as tracking improved performance in quantum annealing computers and helping hardware developers spot inconsistencies in their own devices," Coffrin added in a Los Alamos National Laboratory news article. He adds that perhaps the most impactful thing in the breakthrough project is the open-source software being capable of characterizing noise in the system. If the noise is measurable, its distribution in a quantum system could be better understood.

Additionally, QASA's analysis of each qubit would allow users and developers of quantum annealers to easily verify consistency across the hardware and compensate for, if not totally avoid non-ideal qubits.

A Machine-Learning Driven Development Process

To develop QASA, the Los Alamos National Laboratory used machine learning protocols and data derived from a D-Wave 2000Q quantum computer, which, according to its developer D-Wave Systems, computes through the use of a quantum processing unit (QPU) to implement quantum annealing to search for solutions for a given problem.

Usually, quantum annealers use their namesake metaheuristic for finding the global minimum for a given objective function over a set of candidate states. It uses a process called quantum fluctuation, defined as a temporary and random change in energy values, which allows it to find the smallest possible values for a function over its candidate states.

 

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