Protocol learns single-qubit Z-twirled MCM instrument parameters from three repeated measurements on mixed input, yielding ~100x better Pauli-observable prediction than confusion-matrix models on IBM processors.
Algorithmic Cooling and Scalable NMR Quantum Computers
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
We present here algorithmic cooling (via polarization-heat-bath)- a powerful method for obtaining a large number of highly polarized spins in liquid nuclear-spin systems at finite temperature. Given that spin-half states represent (quantum) bits, algorithmic cooling cleans dirty bits beyond the Shannon's bound on data compression, by employing a set of rapidly thermal-relaxing bits. Such auxiliary bits could be implemented using spins that rapidly get into thermal equilibrium with the environment, e.g., electron spins. Cooling spins to a very low temperature without cooling the environment could lead to a breakthrough in nuclear magnetic resonance experiments, and our ``spin-refrigerating'' method suggests that this is possible. The scaling of NMR ensemble computers is probably the main obstacle to building useful quantum computing devices, and our spin-refrigerating method suggests that this problem can be resolved.
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Learning Mid-circuit Measurement Backaction from Three Repeated Measurements
Protocol learns single-qubit Z-twirled MCM instrument parameters from three repeated measurements on mixed input, yielding ~100x better Pauli-observable prediction than confusion-matrix models on IBM processors.