Characterization of Quantum Noise to Improve Quantum Computers

Abstract:

The scalability of quantum computers is fundamentally limited by decoherence, which is the loss of information from a system into the environment. The decoherence is due to random, noisy, uncontrolled environments, and the sources of the noise need to be characterized to implement noise suppression. Two types of noise are responsible for the decoherence: The classical noise, due to some unknown random classical parameter or to commuting quantum operators, and the quantum noise, due to the non-commutativity of quantum operators. The quantum noise contribution is often relatively weak, and hence quantum systems are often analyzed assuming purely classical noise sources. However, there are many cases that are relevant to current cutting-edge quantum technologies where the noise sources have a large quantum component that should not be ignored. 

To understand which quantum algorithms may be highly sensitive to quantum noise, even when they may be insensitive to classical noise, we develop quantum algorithms to amplify (the often weak) quantum-noise contribution while minimizing the classical noise that would otherwise obscure these measurements. Currently, we know of one protocol that can be viewed as a simple one-qubit quantum algorithm, the Carr-Purcell-Meiboom-Gill sequence, which enhances the quantum noise contribution relative to the classical-noise contribution under a special set of conditions. We theoretically modelled it in a spin system, and saw that the quantum noise results in a phase shift which is correctable. Currently, we generalize it to multi-qubit algorithms, and explore the conditions when quantum noise would be significant, detectable and correctable.