Machine learning blazes path to reliable Quantum Computers

Noise-aware circuit learning uses machine learning to formulate a circuit, or algorithm, with the best strategy to run a specific task in the most reliable way on a given quantum computer.

Scientists at Los Alamos National Laboratory has proposed 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.

The method is called Noise-Aware Circuit Learning (NACL) and will play an important role in the quest for Quantum Advantage.

This machine-learning approach is a kind of a vaccine that strengthens a person’s resistance to a virus by training their immune system in the presence of a piece of that pathogen. Similarly, the machine learning trains quantum circuits in the presence of a specific quantum computer’s noise processes. The resulting circuit, or algorithm, is resistant to that noise, which is the biggest problem facing today’s noisy intermediate-scale quantum computers.

NACL starts with two things: a description of a computational task and a model of the noise on the quantum computer that will perform the task. Then the machine learning program formulates a circuit with the best strategy to run the task in the most reliable way on that particular computer, based on its unique noise profile.

The team tested sample problems in each of these areas and 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.

The paper has been published in Physical Review X Quantum.

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