Sahil Patel

Magnetic-Field and Temperature Limits of a Kinetic-Inductance Traveling-Wave Parametric Amplifier

  1. Lucas M. Janssen,
  2. Farzad Faramarzi,
  3. Henry G. LeDuc,
  4. Sahil Patel,
  5. Gianluigi Catelani,
  6. Peter K. Day,
  7. Yoichi Ando,
  8. and Christian Dickel
Kinetic-inductance traveling-wave parametric amplifiers (KI-TWPAs) offer broadband near-quantum-limited amplification with high saturation power. Due to the high critical magnetic fields
of high-kinetic-inductance materials, KI-TWPAs should be resilient to magnetic fields. In this work, we study how magnetic field and temperature affect the performance of a KI-TWPA based on a thin-NbTiN inverse microstrip with a Nb ground plane. This KI-TWPA can provide substantial signal-to-noise ratio improvement (ΔSNR) up to in-plane magnetic fields of 0.35T and out-of-plane fields of 50mT, considerably higher than what has been demonstrated with TWPAs based on Josephson junctions. The field compatibility can be further improved by incorporating vortex traps and by using materials with higher critical fields. We also find that the gain does not degrade when the temperature is raised to 3K (limited by the Nb ground plane) while ΔSNR decreases with temperature consistently with expectation. This demonstrates that KI-TWPAs can be used in experiments that need to be performed at relatively high temperatures. The operability of KI-TWPAs in high magnetic field opens the door to a wide range of applications in spin qubits, spin ensembles, topological qubits, low-power NMR, and the search for axion dark matter.
arxiv pdf

Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits

  1. Ian Convy,
  2. Haoran Liao,
  3. Song Zhang,
  4. Sahil Patel,
  5. William P. Livingston,
  6. Ho Nam Nguyen,
  7. Irfan Siddiqi,
  8. and K. Birgitta Whaley
We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identity bit-flip errors from continuous noisy
syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.
arxiv pdf