Training a Dragon: Protecting Quantum Algorithms on “Noisy” Computers

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Quantum computing has advanced to the point that both small- and intermediate-scale machines are publicly accessible through organizations such as IBM and Rigetti, enabling researchers to test out theories and perform experiments. But these systems are highly susceptible to decoherence (more commonly called noise in the quantum world), and quantum algorithms perform badly on noisy hardware.
       
APL has applied its engineering and physics expertise to reducing the effect of noise on quantum systems for years, exploring optimal device designs, understanding and reducing noise, and improving supporting infrastructure. Researchers are now testing strategies for combating errors to improve the performance of quantum algorithms on NISQ [noisy intermediate-scale quantum] computers.

We are using two different techniques in our investigation,” Quiroz explained. “We are mitigating errors using dynamical decoupling, which combats the error by effectively averaging out the environmental noise. We are also avoiding the errors altogether by creating a decoherence-free subspace that makes the system itself blind to the noise.

 

The results are encouraging, Quiroz said. “They show us that a lot of these theoretical methods that have been developed may be useful for larger quantum computers and actually near-term quantum computers as methods for improving quality computations,” he said.

Source:  Johns Hopkins Applied Physics Laboratory.  Paulette Campbell,  Training a Dragon: Protecting Quantum Algorithms on “Noisy” Computers…

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