Transmon qubit readout fidelity at the threshold for quantum error correction without a quantum-limited amplifier

Chen, Liangyu; Li, Hang-Xi; Lu, Yonghua; Warren, Christopher; Križan, Christian; Kosen, Sandoko; Rommel, Marcus; Ahmed, Shahnawaz; Osman, Amr; Biznárová, Janka; Roudsari, Anita Fadavi; Lienhard, Benjamin; Caputo, Marco; Grigoras, Kestutis; Grönberg, Leif; Govenius, Joonas; Kockum, Anton Frisk; Delsing, Per; Bylander, Jonas; Tancredi, Giovanna

doi:10.1038/s41534-023-00689-6

Cited by 31 publications

(7 citation statements)

References 52 publications

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“…We remark on the fact that the most significant improvement occurs with a combination of hardware and software improvements, as obtained by the authors in Ref. [34]. In this paper, the focus is only on software improvement on present machines.…”

Section: Introductionmentioning

confidence: 66%

Enhancing Qubit Readout with Autoencoders

et al. 2023

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In addition to the need for stable and precisely controllable qubits, quantum computers take advantage of good readout schemes. Superconducting qubit states can be inferred from the readout signal transmitted through a dispersively coupled resonator. This work proposes a readout classification method for superconducting qubits based on a neural network pretrained with an autoencoder approach. A neural network is pretrained with qubit readout signals as autoencoders in order to extract relevant features from the data set. Afterward, the pretrained-network inner-layer values are used to perform a classification of the inputs in a supervised manner. We demonstrate that this method can enhance classification performance, particularly for short-and long-time measurements where more traditional methods present inferior performance.

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Section: Introductionmentioning

confidence: 66%

Enhancing Qubit Readout with Autoencoders

et al. 2023

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“…Nonetheless, the ratio of points inside and outside the ellipses remains close to , characteristic from a 2D Gaussian distribution 35 . Note that this method does not take into account all state-assignment errors, and hence further single-shot-readout optimization may be achieved through more advanced machine learning algorithms 36 – 38 .…”

Section: Methodsmentioning

confidence: 99%

Many-excitation removal of a transmon qubit using a single-junction quantum-circuit refrigerator and a two-tone microwave drive

Teixeira,

Mörstedt,

Viitanen

et al. 2024

Sci Rep

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Achieving fast and precise initialization of qubits is a critical requirement for the successful operation of quantum computers. The combination of engineered environments with all-microwave techniques has recently emerged as a promising approach for the reset of superconducting quantum devices. In this work, we experimentally demonstrate the utilization of a single-junction quantum-circuit refrigerator (QCR) for an expeditious removal of several excitations from a transmon qubit. The QCR is indirectly coupled to the transmon through a resonator in the dispersive regime, constituting a carefully engineered environmental spectrum for the transmon. Using single-shot readout, we observe excitation stabilization times down to roughly 500 ns, a 20-fold speedup with QCR and a simultaneous two-tone drive addressing the e–f and f0–g1 transitions of the system. Our results are obtained at a 48-mK fridge temperature and without postselection, fully capturing the advantage of the protocol for the short-time dynamics and the drive-induced detrimental asymptotic behavior in the presence of relatively hot other baths of the transmon. We validate our results with a detailed Liouvillian model truncated up to the three-excitation subspace, from which we estimate the performance of the protocol in optimized scenarios, such as cold transmon baths and fine-tuned driving frequencies. These results pave the way for optimized reset of quantum-electric devices using engineered environments and for dissipation-engineered state preparation.

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“…[10] The "quantum measurement errors" are among the most relevant. State-of-the-art quantum hardware may experience readout errors ranging from 1% to 30% [44][45][46] which can occur as bit-flips, i. e., where outcomes are mistakenly recorded as |0⟩ when they are |1⟩, and vice versa. Even if the quantum processor ideally performs noiseless operations, the final measurement introduces random perturbations, which makes the final n-bit string different from the ideal one.…”

Section: Measurement Error Mitigation Through Fuzzy Clusteringmentioning

confidence: 99%

Mitigating Errors on Superconducting Quantum Processors Through Fuzzy Clustering

Ahmad,

Schiattarella,

Mastrovito

et al. 2024

Adv Quantum Tech

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Quantum utility is severely limited in superconducting quantum hardware until now by the modest number of qubits and the relatively high level of control and readout errors, due to the intentional coupling with the external environment required for manipulation and readout of the qubit states. Practical applications in the Noisy Intermediate Scale Quantum (NISQ) era rely on Quantum Error Mitigation (QEM) techniques, which are able to improve the accuracy of the expectation values of quantum observables by implementing classical post‐processing analysis from an ensemble of repeated noisy quantum circuit runs. In this work, a recent QEM technique that uses Fuzzy C‐Means (FCM) clustering to specifically identify measurement error patterns is focused. For the first time, a proof‐of‐principle validation of the technique on a two‐qubit register, obtained as a subset of a real NISQ five‐qubit superconducting quantum processor based on transmon qubits is reported. It is demonstrated that the FCM‐based QEM technique allows for reasonable improvement of the expectation values of single‐ and two‐qubit gates‐based quantum circuits, without necessarily invoking state‐of‐the‐art coherence, gate, and readout fidelities.

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Transmon qubit readout fidelity at the threshold for quantum error correction without a quantum-limited amplifier

Cited by 31 publications

References 52 publications

Enhancing Qubit Readout with Autoencoders

Enhancing Qubit Readout with Autoencoders

Many-excitation removal of a transmon qubit using a single-junction quantum-circuit refrigerator and a two-tone microwave drive

Mitigating Errors on Superconducting Quantum Processors Through Fuzzy Clustering

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