Marc Serra-Peralta

Improved error correction with leakage reduction units built into qubit measurement in a superconducting quantum processor

  1. Yuejie Xin,
  2. Sean L. M. van der Meer,
  3. Marc Serra-Peralta,
  4. Tim H. F. Vroomans,
  5. Matvey Finkel,
  6. Hendrik M. Veen,
  7. Mark W. Beekman,
  8. and Leonardo DiCarlo
Leakage to non-computational states is a source of correlated errors in both time and space that limits the effectiveness of quantum error correction (QEC) with superconducting circuits.
We present and experimentally demonstrate a high-fidelity, leakage reduction unit (LRU) operating concurrently with transmon measurement without incurring time overhead. Adapted from double-drive reset of population (DDROP), the protocol utilizes simultaneous drives on the transmon and its readout resonator, leveraging the dispersive shift to create a directional process that returns the transmon to the computational subspace. The LRU achieves a 98.4% leakage removal fraction without compromising the computational-state assignment fidelity (99.2%). We combine LRU-enhanced measurement and neural-network decoding to successfully suppress logical error rates in both memory and stability QEC experiments without any post-selection.
arxiv pdf

Reducing the error rate of a superconducting logical qubit using analog readout information

  1. Hany Ali,
  2. Jorge Marques,
  3. Ophelia Crawford,
  4. Joonas Majaniemi,
  5. Marc Serra-Peralta,
  6. David Byfield,
  7. Boris Varbanov,
  8. Barbara M. Terhal,
  9. Leonardo DiCarlo,
  10. and Earl T. Campbell
Quantum error correction enables the preservation of logical qubits with a lower logical error rate than the physical error rate, with performance depending on the decoding method.
Traditional error decoding approaches, relying on the binarization (`hardening‘) of readout data, often ignore valuable information embedded in the analog (`soft‘) readout signal. We present experimental results showcasing the advantages of incorporating soft information into the decoding process of a distance-three (d=3) bit-flip surface code with transmons. To this end, we use the 3×3 data-qubit array to encode each of the 16 computational states that make up the logical state $\ket{0_{\mathrm{L}}}$, and protect them against bit-flip errors by performing repeated Z-basis stabilizer measurements. To infer the logical fidelity for the $\ket{0_{\mathrm{L}}}$ state, we average across the 16 computational states and employ two decoding strategies: minimum weight perfect matching and a recurrent neural network. Our results show a reduction of up to 6.8% in the extracted logical error rate with the use of soft information. Decoding with soft information is widely applicable, independent of the physical qubit platform, and could reduce the readout duration, further minimizing logical error rates.
arxiv pdf