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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