Algorithmic cooling for resolving state preparation and measurement errors in quantum computing

While Quantum Computers have the potential to solve problems of great difficulty, they rely on proper usage and error reduction. Often, calculations are not accurate due to errors in state preparations or in measurement among other error sources. The two sources mentioned are the interest of this paper by Raymond Laflamme, Junan Lin, and Tal Mor because they are hard to distinguish and treat. State preparation and measurement errors, abbreviated SPAM, are often confused since one cannot determine if a measured Qubit differs from its original state as a result of the measurement or of the initial state preparation.

The paper thus introduces a method to characterize SPAM errors using a measurements-based algorithmic cooling (MBAC). Traditional algorithmic cooling involves thermal relaxation of the qubits back to their equilibrium. The measurements-based approach used during this research makes more assumptions about the state of a target qubit and reduces the number of measurements made. This addresses the issue of errors due to measurements and the process can be expanded to larger numbers of qubits by repeating the process. Moreover, the contributions from state preparation and measurement errors can be quantified separately to allow for better design of algorithms.

The paper then goes on to determine the number of MBAC(Measurements Based Approach to Cooling) processes required to sufficiently reduce the error attributed to SPAM.This method is generally platform independent which allows different quantum computers to compare their performance to an accessible benchmark.