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Fast charge noise sensing using a spectator valley state in asinglet-triplet qubit

David W. Kanaar, Yasuo Oda, Mark F. Gyure, J. P. Kestner

Published Mar 19, 2026• 0 citations
Publisher

Abstract

Abstract Semiconductor spin qubits are a promising platform for quantum computing but remain vulnerable tocharge noise. Accurate, in situ measurement of charge noise could enable closed-loop control andimprove qubit performance. Here, we propose a method for real-time detection of charge noise usinga silicon singlet-triplet qubit in which one electron is initialized in an excited valley state. This valleyexcitation acts as a spectator degree of freedom coupled to a high-quality resonator via the exchangeinteraction, which is sensitive to charge-noise-induced voltage fluctuations. Dispersive readout of theresonator enables a continuous, classical measurement of exchange fluctuations during qubitoperation. Signal-to-noise analysis shows that, under realistic device parameters, sub-millisecondmeasurement times are achievable using a quantum-limited amplifier. Even without such an amplifier,similar performance is achievable with appropriately engineered resonator parameters. This approachenables real-time monitoring of slow drifts in exchange, opening the door to feedback and feedforwardstrategies for maintaining high-fidelity quantum operations. Importantly, the protocol preserves spincoherence and can be executed concurrently with qubit logic gates.

DOI
10.21203/rs.3.rs-8857776/v1

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Abstract Semiconductor spin qubits are a promising platform for quantum computing but remain vulnerable tocharge noise. Accurate, in situ measurement of charge noise could enable closed-loop control andimprove qubit performance. Here, we propose a method for real-time detection of charge noise usinga silicon singlet-triplet qubit in which one electron is initialized in an excited valley state. This valleyexcitation acts as a spectator degree of freedom coupled to a high-quality resonator via the exchangeinteraction, which is sensitive to charge-noise-induced voltage fluctuations. Dispersive readout of theresonator enables a continuous, classical measurement of exchange fluctuations during qubitoperation. Signal-to-noise analysis shows that, under realistic device parameters, sub-millisecondmeasurement times are achievable using a quantum-limited amplifier. Even without such an amplifier,similar performance is achievable with appropriately engineered resonator parameters. This approachenables real-time monitoring of slow drifts in exchange, opening the door to feedback and feedforwardstrategies for maintaining high-fidelity quantum operations. Importantly, the protocol preserves spincoherence and can be executed concurrently with qubit logic gates.
Semiconductor spin qubits are a promising platform for quantum computing but remain vulnerable to charge noise. Accurate, in situ measurement of charge noise could enable closed-loop control and improve qubit performance. Here, we propose a method for real-time detection of charge noise using a silicon singlet-triplet qubit with one electron initialized in an excited valley state. This valley excitation acts as a spectator degree of freedom, coupled to a high-quality resonator via the exchange interaction, which is sensitive to charge-noise-induced voltage fluctuations. Dispersive readout of the resonator enables a continuous, classical measurement of exchange fluctuations during qubit operation. Signal-to-noise analysis shows that, under realistic device parameters, sub-millisecond measurement times are possible using a quantum-limited amplifier. Even without such an amplifier, similar performance is achievable with appropriately engineered resonator parameters. This approach allows the probe to monitor slow drift in exchange in real time, opening the door to feedback and feedforward strategies for maintaining high-fidelity quantum operations. Importantly, the protocol preserves spin coherence and can be run concurrently with qubit logic gates.
Two level systems that can be reliably controlled and measured hold promise as qubits both for metrology and for quantum information science. Since a fluctuating environment limits the performance of qubits in both capacities, understanding environmental coupling and dynamics is key to improving qubit performance. We show measurements of the level splitting and dephasing due to the voltage noise of a GaAs singlet-triplet qubit during exchange oscillations. Unexpectedly, the voltage fluctuations are non-Markovian even at high frequencies and exhibit a strong temperature dependence. This finding has impacts beyond singlet-triplet qubits since nearly all solid state qubits suffer from some kind of charge noise. The magnitude of the fluctuations allows the qubit to be used as a charge sensor with a sensitivity of 2 × 10(-8)e/sqrt[Hz], 2 orders of magnitude better than a quantum-limited rf single electron transistor. Based on these measurements, we provide recommendations for improving qubit coherence, allowing for higher fidelity operations and improved charge sensitivity.
We devise a platform for noise-resistant quantum computing using the valley degree of freedom of Si quantum dots. The qubit is encoded in two polarized (1,1) spin-triplet states with different valley compositions in a double quantum dot, with a Zeeman field enabling unambiguous initialization. A top gate gives a difference in the valley splitting between the dots, allowing controllable interdot tunneling between opposite valley eigenstates, which enables one-qubit rotations. Two-qubit operations rely on a stripline resonator, and readout on charge sensing. Sensitivity to charge and spin fluctuations is determined by intervalley processes and is greatly reduced as compared to conventional spin and charge qubits. We describe a valley echo for further noise suppression.
Quantum computers offer a route to outperform classical methods in tasks such as molecular simulation, motivating hybrid algorithms like the Variational Quantum Eigensolver (VQE) for near-term devices. Silicon spin qubits are a promising platform for scalable quantum computation, but their performance is limited by hardware imperfections -- most notably charge-noise-induced potential fluctuations and static miscalibration of gate-electrode voltages -- which degrade quantum gate fidelities and, ultimately, algorithmic accuracy. Here we develop a hardware-algorithm co-simulation framework for silicon quantum-dot processors that links 3D electrostatics to effective ggg-factors and exchange couplings, and propagates voltage-level noise through realistic control pulses. Using VQE for H2\mathrm{H}_2H2​ ground-state energy estimation as a circuit-level testbed, we study both static scaling/offset errors on the gate-electrode voltages and stochastic fluctuations modeled as random-telegraph noise with tunable amplitudes and switching times. At the gate level, we show that exchange-based two-qubit gates are roughly an order of magnitude more sensitive to these types of noise than ESR-driven single-qubit rotations. Quantum process tomography and Kraus-operator analysis further distinguish coherent and incoherent contributions and quantify the fraction of error that is, in principle, correctable by a compensating unitary. Embedding these noise models into the VQE circuit, we identify regimes of miscalibration strength and noise switching time compatible with chemically accurate energy estimates, and discuss how statistical post-processing based on the full distribution of noisy energy estimates could further improve accuracy.
Spins in silicon quantum devices are promising candidates for large-scale quantum computing. Gate-based sensing of spin qubits offers a compact and scalable readout with high fidelity, however, further improvements in sensitivity are required to meet the fidelity thresholds and measurement timescales needed for the implementation of fast feedback in error correction protocols. Here, we combine radio-frequency gate-based sensing at 622 MHz with a Josephson parametric amplifier, that operates in the 500-800 MHz band, to reduce the integration time required to read the state of a silicon double quantum dot formed in a nanowire transistor. Based on our achieved signal-to-noise ratio, we estimate that singlet-triplet single-shot readout with an average fidelity of 99.7% could be performed in 1 μs, well below the requirements for fault-tolerant readout and 30 times faster than without the Josephson parametric amplifier. Additionally, the Josephson parametric amplifier allows operation at a lower radio-frequency power while maintaining identical signal-to-noise ratio. We determine a noise temperature of 200 mK with a contribution from the Josephson parametric amplifier (25%), cryogenic amplifier (25%) and the resonator (50%), showing routes to further increase the readout speed.
The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit parameters. As systems expand to large one- and two-dimensional arrays, manual recalibration becomes impractical, creating a need for autonomous stabilization frameworks. Here, we introduce a method that uses the full network of charge-transition lines in repeatedly acquired double-quantum-dot charge stability diagrams (CSDs) as a multidimensional probe of the local electrostatic environment. By accurately tracking the motion of selected transitions in time, we detect voltage drifts, identify abrupt charge reconfigurations, and apply compensating updates to maintain stable operating conditions. We demonstrate our approach on a 10-QD device, showing robust stabilization and real-time diagnostic access to dot-specific noise processes. The high acquisition rate of radio-frequency reflectometry CSD measurements also enables time-domain noise spectroscopy, allowing the extraction of noise power spectral densities, the identification of two-level fluctuators, and the analysis of spatial noise correlations across the array. From our analysis, we find that the background noise at 100~μμμ\si{\hertz} is dominated by drift with a power law of 1/f21/f^21/f2, accompanied by a few dominant two-level fluctuators and an average linear correlation length of (188±38)(188 \pm 38)(188±38)~\si{\nano\meter} in the device. These capabilities form the basis of a scalable, autonomous calibration and characterization module for QD-based quantum processors, providing essential feedback for long-duration, high-fidelity qubit operations.