3 research outputs found
A hardware-efficient leakage-reduction scheme for quantum error correction with superconducting transmon qubits
Leakage outside of the qubit computational subspace poses a threatening
challenge to quantum error correction (QEC). We propose a scheme using two
leakage-reduction units (LRUs) that mitigate these issues for a transmon-based
surface code, without requiring an overhead in terms of hardware or QEC-cycle
time as in previous proposals. For data qubits we consider a microwave drive to
transfer leakage to the readout resonator, where it quickly decays, ensuring
that this negligibly affects the coherence within the computational subspace
for realistic system parameters. For ancilla qubits we apply a
pulse conditioned on the measurement
outcome. Using density-matrix simulations of the distance-3 surface code we
show that the average leakage lifetime is reduced to almost 1 QEC cycle, even
when the LRUs are implemented with limited fidelity. Furthermore, we show that
this leads to a significant reduction of the logical error rate. This LRU
scheme opens the prospect for near-term scalable QEC demonstrations
Neural network decoder for near-term surface-code experiments
Neural-network decoders can achieve a lower logical error rate compared to
conventional decoders, like minimum-weight perfect matching, when decoding the
surface code. Furthermore, these decoders require no prior information about
the physical error rates, making them highly adaptable. In this study, we
investigate the performance of such a decoder using both simulated and
experimental data obtained from a transmon-qubit processor, focusing on
small-distance surface codes. We first show that the neural network typically
outperforms the matching decoder due to better handling errors leading to
multiple correlated syndrome defects, such as errors. When applied to the
experimental data of [Google Quantum AI, Nature 614, 676 (2023)], the neural
network decoder achieves logical error rates approximately lower than
minimum-weight perfect matching, approaching the performance of a
maximum-likelihood decoder. To demonstrate the flexibility of this decoder, we
incorporate the soft information available in the analog readout of transmon
qubits and evaluate the performance of this decoder in simulation using a
symmetric Gaussian-noise model. Considering the soft information leads to an
approximately lower logical error rate, depending on the probability of
a measurement error. The good logical performance, flexibility, and
computational efficiency make neural network decoders well-suited for near-term
demonstrations of quantum memories.Comment: 15 pages, 8 figures, 1 tabl
Microwave-activated gates between a fluxonium and a transmon qubit
We propose and analyze two types of microwave-activated gates between a
fluxonium and a transmon qubit, namely a cross-resonance (CR) and a CPHASE
gate. The large frequency difference between a transmon and a fluxonium makes
the realization of a two-qubit gate challenging. For a medium-frequency
fluxonium qubit, the transmon-fluxonium system allows for a cross-resonance
effect mediated by the higher levels of the fluxonium over a wide range of
transmon frequencies. This allows one to realize the cross-resonance gate by
driving the fluxonium at the transmon frequency, mitigating typical problems of
the cross-resonance gate in transmon-transmon chips related to frequency
targeting and residual ZZ coupling. However, when the fundamental frequency of
the fluxonium enters the low-frequency regime below 100 MHz, the
cross-resonance effect decreases leading to long gate times. For this range of
parameters, a fast microwave CPHASE gate can be implemented using the higher
levels of the fluxonium. In both cases, we perform numerical simulations of the
gate showing that a gate fidelity above 99% can be obtained with gate times
between 100 and 300 ns. Next to a detailed gate analysis, we perform a study of
chip yield for a surface code lattice of fluxonia and transmons interacting via
the proposed cross-resonance gate. We find a much better yield as compared to a
transmon-only architecture with the cross-resonance gate as native two-qubit
gate