Optimization of algorithmic cooling for NMR quantum computers
Alexei Kaltchenko · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
To achieve scalability of NMR computers, one needs a large number of highly polarized spins in liquid nuclearspin systems at finite temperature. In quantum computing terminology, such spin-half states are (almost) pure qubit states. Producing highly polarized spins (almost pure qubit states) out of non-polarized spins (non-pure qubit states) is sometimes called "purification". From a thermodynamic point of view, purification can be viewed as cooling spins to a very low temperature. In this work, we study how classical data compression codes can be used to design cooling algorithms for both short and long molecules. We argue that so designed cooling (purification) algorithms potentially outperform other methods in terms of the closeness of the output state to the ideal pure state, which in turn implies lower output temperature. We also analyze how the mismatch of the algorithm's computational basis and the actual eigenbasis of the spins' density matrix will affect the cooling (purification) performance.