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Machine Learning Reduces Impact of Noise on Quantum Circuits

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Noise-aware circuit learning produces shorter depth circuits that minimize the impact of stochastic noise sources.

Using machine learning to develop algorithms that compensate for the crippling noise endemic on today's quantum computers offers a way to maximize their power for reliably performing actual tasks, according to a new paper.

"The method, called noise-aware circuit learning, or NACL, will play an important role in the quest for quantum advantage, when a quantum computer solves a problem that's impossible on a classical computer," says Patrick Coles, a quantum physicist in at Los Alamos National Laboratory and lead author on "Machine Learning of Noise-Resilient Quantum Circuits," published in PRX Quantum.

"Our work automates designing quantum computing algorithms and comes up with the fastest algorithm tailored to the imperfections of a specific hardware platform and a specific task," says Lukasz Cincio, a quantum physicist at Los Alamos. "This will be a crucial tool for using real quantum computers in the near term for work such as simulating a biological molecule or physics simulations relevant to the national security mission."

NACL formulates a circuit with the best strategy to run a task in the most reliable way on a particular computer, based on its unique noise profile. The framework works for all of the common tasks in quantum computing — extracting observables, preparing quantum states, and compiling circuits. Researchers demonstrated that NACL reduces error rates in algorithms run on quantum computers by factors of 2 to 3 compared to textbook circuits for the same tasks. 

From Los Alamos National Laboratory
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