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Akiva Feintuch

Publications and source records attributed to Akiva Feintuch.

2 recordsLinked to original sources

Optimizing Pump Conditions of Parametric Amplifiers for Fast Multiplexed Readout of Superconducting Qubits

Low-noise parametric amplifiers are widely used as the first-stage amplifier in qubit readout chains. The performance of parametric amplifiers depends sensitively on the choice of the pump condition. We propose a strategy for determining the pump condition that is tailored for fast multiplexed readout. Choosing the amplifier pump to maximize the signal-to-noise ratio (SNR) improvement at the readout frequency of the limiting qubit--the qubit that requires the longest readout time to reach a target SNR--minimizes the total multiplexed readout time. We demonstrate our pump calibration strategy experimentally on a five-qubit multiplexed readout chain with a traveling-wave parametric amplifier. Using our strategy, we reduce the multiplexed readout time by 320 ns compared to optimizing the average SNR improvement on all qubits, without degrading the target SNR for any qubit.

quant-ph

Neural networks in pulsed dipolar spectroscopy: a practical guide

This is a methodological guide to the use of deep neural networks in the processing of pulsed dipolar spectroscopy (PDS) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived radical pairs and paramagnetic metal ions. PDS uses distance dependence of magnetic dipolar interactions; measuring a single well-defined distance is straightforward, but extracting distance distributions is a hard and mathematically ill-posed problem requiring careful regularisation and background fitting. Neural networks do this exceptionally well, but their "robust black box" reputation hides the complexity of their design and training - particularly when the training dataset is effectively infinite. The objective of this paper is to give insight into training against simulated databases, to discuss network architecture choices, to describe options for handling DEER (double electron-electron resonance) and RIDME (relaxation-induced dipolar modulation enhancement) experiments, and to provide a practical data processing flowchart.

physics.chem-ph