Two new techniques are presented that address two challenges in quantum computing – overcoming decoherence, or information loss, due to noise, which is inherent to quantum hardware, and accounting for and removing error in measured quantities from quantum algorithms.
The first technique recovers lost information by repeating the quantum process many times in sequence, with slightly different noise characteristics, and then analyzing the results. After gathering results by running the process many times in sequence or parallel, we plot a hypersurface where one axis represents the results of a measurement and the other two (or more) axes, different noise parameters. This hypersurface yields an estimate of the noise-free observable and gives information about the effect of each noise rate. Applying this technique eliminates quantum noise without the need for additional quantum hardware. The technique is versatile and can be done with separate quantum systems undergoing the same process at the same time. An algorithm could run in parallel on several small quantum computers, and, using this method, one could combine the results on the hypersurface and generate approximate noise-free observables. The results would help extend the usefulness of the quantum computers before decoherence sets in. We successfully performed a simple demonstration of the method on the Rigetti 8Q-Agav quantum computer.
The second technique reduces error on each individual qubit and sums the scaled difference from the results. It focuses on changing the error associated with each individual qubit or error source separately and is well suited to applications where the dominant noise source is environmental interactions. This method is flexible and can reduce environmental error from a measured observable, with potential in quantum sensing and quantum metrology.
Work was performed in part at the Center for Nanoscale Materials.
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