Correlation-Enhanced Algorithmic Cooling

The field of quantum information has inspired new methods for cooling physical systems at the quantum scale. In particular, heat-bath algorithmic cooling (HBAC) methods operate by manipulating entropy at the quantum level and making contact with a bath. These techniques are at the core of practical applications of quantum information science: in quantum computing, they provide a controlled way to prepare highly pure quantum states (which are required from the state initialization of most quantum algorithms to a reliable supply of ancilla qubits that satisfy the fault-tolerance threshold for quantum error correction). In this talk, we generalize HBAC methods to allow the presence of correlations due to internal interaction-- and we show that these correlations provide a resource that can be used to improve cooling beyond previously established limits. Furthermore, we show that correlated relaxation processes between the system and environment during rethermalization can be also exploited to enhance purification.