Real-Time FPGA-Based Multi-Parameter Feedback Stabilization of a Silicon Double Quantum Dot

summary

The gist

The gist The system achieves a-6 dB noise suppression up to 5 kHz by implementing an active stabilization scheme using an FPGA for digital signal processing and continuous multi-parameter gradient

In short

The study addresses long-term drift in silicon double quantum dot spin qubits caused by low-frequency 1/f charge noise. The researchers developed a high-speed active stabilization scheme using an FPGA and rf reflectometry readout to continuously calculate gradients. This method successfully suppressed noise by -6 dB up to 5 kHz, significantly enhancing the device's stability compared to older transport measurement techniques.

Key concepts

1/f Charge Noise: This is a specific type of low-frequency noise common in semiconductor devices, caused by fluctuating charge traps in the surrounding material. This noise slowly changes the local electric potential landscape around the quantum dots over time, which causes the qubit's operating point to drift and degrades coherence.
Gradient Descent Feedback (GDFB): This is an active stabilization technique where a digital processor calculates how much each gate voltage needs to be adjusted to counteract noise. It works by measuring the current noise, calculating a 'gradient' of error, and then making small, corrective adjustments to the gate voltages in real-time.
FPGA-based Digital Signal Processing: The FPGA (Field-Programmable Gate Array) is a programmable chip used here for high-speed digital signal processing. It allows the system to perform continuous multi-parameter gradient calculations and execute quick gate voltage updates rapidly, which is crucial for achieving a high feedback bandwidth.

Terminology used across episodes

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This episode discusses

The paper

Real-Time FPGA-Based Multi-Parameter Feedback Stabilization of a Silicon Double Quantum Dot · Read on arXiv

Johnathan Bryan, Tim J. Wilson, Hong-Wen Jiang

University of California, Los Angeles

Long term operation of semiconductor spin qubits requires active stabilization of quantum dot potentials against low-frequency charge noise. To address this, we demonstrate a gradient-descent feedback approach in a silicon double quantum dot device using high-bandwidth radio-frequency (rf) reflectometry readout and a field-programmable gate array (FPGA) for digital signal processing. This architecture enables continuous multi-parameter gradient calculation and quick gate voltage updates, significantly increasing the effective feedback bandwidth compared to previous methods relying on slow direct-current transport measurements. By operating with 8 steps per feedback cycle and an integration time of 25.6 μ s, this high-speed stabilization scheme maintains better than-6 dB of noise suppression up to 5 kHz, achieving an attenuation of-7.4 plus or minus 0.2 dB at the maximum measured frequency. This effectively suppresses low-frequency 1/f noise, maintaining device stability over longer time periods, and potentially enables real-time control in large-scale quantum dot arrays.

Transcript

Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Kai: We're looking at the paper: "Real-Time FPGA-Based Multi-Parameter Feedback Stabilization of a Silicon Double Quantum Dot".

Mira: Here's the gist of the paper. The gist The system achieves a-6 dB noise suppression up to 5 kHz by implementing an active stabilization scheme using an FPGA for digital signal processing and continuous multi-parameter gradient calculation in a silicon double quantum dot device.

Kai: Device and Problem Context Semiconductor spin qubits are a promising platform for fault-tolerant quantum computation due to their potential for high-fidelity operation, small footprint, and compatibility with modern semiconductor manufacturing.

Mira: A primary decoherence mechanism in these systems is low-frequency 1/f charge noise, which arises from fluctuating charge traps in the surrounding device materials and limits free-evolution dephasing times.

Kai: Over time, this noise alters the local potential landscape, causing the operating point of quantum dot systems to drift and degrading long-term qubit operation and T2 coherence times.

Paper summary: Kai: To wrap up what we've seen in "Real-Time FPGA-Based Multi-Parameter Feedback Stabilization of a Silicon Double Quantum Dot," the paper proposes extending a gradient descent feedback approach for silicon double quantum dots.

Mira: The main thesis is that by using high bandwidth rf reflectometry readout combined with an FPGA like the OPX, you can achieve continuous multi-parameter gradient calculation and quick gate voltage updates.

Lev: This setup is designed to increase the effective feedback bandwidth substantially compared to earlier methods that relied on slower direct current transport measurements.

Kai: They demonstrate this high speed stabilization scheme operates with eight steps per feedback cycle and an integration time of twenty-five point six µs, which allows them to reach a cutoff frequency of approximately six point one kHz based on the Nyquist Shannon sampling theorem.

Mira: The result is that this process maintains better than-six dB of noise suppression up to a bandwidth of five kHz, effectively tackling low frequency one over f noise in the system <ref:2608.10325#pg1,noise suppression up to a bandwidth of 5 kHz>.

Lev: The numbers show they achieve an attenuation of-seven point four dB at the maximum measured frequency, which suggests it's quite effective at suppressing that specific type of noise.

Kai: So what this means for us is that we can now do stabilization much faster and with higher fidelity than before, without needing those slow current measurements to keep things stable.

Mira: It’s a big step because low frequency charge noise is a major issue for long term operation, and this architecture gives us the tools to actively fight it in real time.

Lev: For someone building real hardware, this means the overhead of running this protocol on actual hardware would be determined by how fast those voltage transition times are and how many steps per cycle they need to complete.

Kai: It’s about getting that feedback loop tight enough to keep the dot potentials where we want them.

Conclusion: Mira: So looking at the paper "Real-Time FPGA-Based Multi-Parameter Feedback Stabilization of a Silicon Double Quantum Dot," it really shows how fast you can make these stabilization protocols.

Kai: The authors are using an FPGA, the OPX, to implement this gradient descent feedback protocol by directly recording the input signal from the OPX and comparing it to the applied output voltage over time.

Lev: The implication is that we can achieve noise suppression up to five kHz with a bandwidth of five kHz, which is a significant increase over previous techniques based on DC transport measurements <ref:2608.10325#pg1,a bandwidth of 5 kHz>.

Mira: This work gives us a concrete method for mitigating low frequency one over f noise in silicon double quantum dots by using fast dispersive readout and FPGA processing.

Kai: It means we can potentially achieve better long term stability for these spin qubits, which is crucial for any kind of fault tolerant quantum computation you're hoping to build.

Lev: The paper flags that the bandwidth is currently limited by the total duration of their feedback cycle, as they need eighty-one point eight µs per cycle to reach that five kHz cutoff frequency.

Mira: That’s a practical limitation for real hardware integration, so we need to think about how many steps per cycle are needed versus how much speed we actually get in practice.

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