#twolevelsystems

Quantum system error prevention using qubit and two-level system interactions.

Scientists stabilise noise in superconducting quantum processors to mitigate errors. Researchers have taken a major step towards reliable, large-scale quantum processing by stabilising noise in superconducting quantum processors, boosting error mitigation measures.

Pre-Fault Tolerant Quantum Device Noise Challenge Contemporary quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown the ability to precisely estimate observable values at sizes exceeding brute-force classical simulation. This capacity is enabled by quantum error mitigation (QEM) approaches, which reduce noise from observable estimates without qubit overhead.

However, probabilistic error cancellation (PEC) and zero-noise extrapolation (ZNE) require a precise, representative device noise model. Learning and maintaining this model is difficult due to unpredictable physical device noise fluctuations.

Superconducting quantum processor fluctuations are caused by qubit-defect two-level system (TLS) resonant interaction. The dispersion of Two Level Systems TLS transition frequencies drives qubit relaxation time fluctuations. Dynamic instabilities are shown to reduce error-mitigation performance or produce unphysical estimates, affecting superconducting quantum computers' stability, uniformity, and throughput.

Controlling Qubit–TLS Interaction

Trials were conducted with a one-dimensional chain of six fixed-frequency transmon qubits with tunable couplers. Noise instability was addressed by placing an electrode above each transmon qubit. A bias applied to this electrode can control the Two Level Systems TLS resonance frequency and qubit–TLS interaction by changing the local electric field at defect locations.

Qubit levels could change by nearly 300% in 60 hours. Researchers aimed to manipulate the qubit–TLS interface to reduce noise instabilities and improve QEM performance.

The group studied two noise stabilisation methods:

Optimisation Noise Strategy: Tracking the temporal snapshot of the TLS landscape, this active technique finds a value that maximises the excited state population after a predefined delay time. Avoiding qubit–TLS interactions with high strength is the goal.

Averaged Noise Strategy: This passive strategy reduces oscillations by averaging randomly selected Two Level Systems surroundings per shot. This is done by utilising sinusoidal (or triangular) amplitude modulation that varies slowly at 1 Hz, far lower than the shot repetition rate of 1 kHz. Because it samples a new quasi-static TLS environment for each shot, this approach doesn't need constant monitoring.

Overhead Reduction and Noise Model Stabilisation

These approaches were tested for noise stability connected to layers of concurrently entangling two-qubit gates as well as stabilisation. Accurate noise characterisation is essential for PEC, which uses the Sparse Pauli–Lindblad (SPL) model.

Total sampling overhead, which measures the multiplicative factor that magnifies estimator variation during error mitigation, linked noise severity to experiment cost.

In the control experiment (no modulation), noise model parameters and sampling overhead varied greatly and often correlated with high qubit–TLS interaction.

Optimised sampling overhead was lowest. The model coefficients were steady overall, save for slight transient fluctuations between optimisation cycles.

Importantly, the averaged noise approach outperformed control and optimised tests in stability over time. Smoothing out minor changes and creating more stable device noise models minimises the need for regular active monitoring.

More reliable error mitigation

The approaches were benchmarked using a 6-qubit mirror circuit, which should provide an identity operator. The ideal anticipated value of the weight-6 Pauli-Z observable is 1.

Variations from 0.341 to 0.446 in unmitigated observable values were far from the ideal value of 1. After error mitigation (PEC) with noise models:

The mitigated observable values fluctuated more in the control setting during heavy Two Level Systems interaction. Unlike the control experiment, the optimised and averaged noise solutions stabilised error mitigation findings, reducing observable estimation volatility.

The investigation showed a substantial correlation between the mitigation error and the anticipated deviation (based on noise model fluctuation), showing that time fluctuation considerably spreads the error-mitigated observable. These modulation methods stabilised QEM performance temporal fluctuation, as shown by the optimised and averaged channels' narrower distributions.

In conclusion

Quantum Noise Characterization

Quantum Computing Needs Noise Characterisation.

Fight against mistakes is the core of robust, error-tolerant quantum computing. The high error rates of Noisy Intermediate-Scale Quantum (NISQ) devices limit their calculation complexity. Working with superconducting qubits is complicated due to the quantum logic gate. To improve gate fidelity, one must first identify, understand, and quantify noise sources. The noise characterisation process provides the knowledge needed to build more reliable quantum processors.

Identification of Error Sources

Other reasons of quantum gate failures in superconducting systems include qubit instability and microwave control system problems. Researchers have examined the sources of these errors to understand their effects.

Control System Instability, Qubit

A qubit's transition frequency that differs from the control pulse frequency might generate off-resonance faults. Frequency drifts in the control system and qubit may require periodic calibration.

Long-term stability assessments are done by scientists. Over 20-hour investigations showed that qubit frequencies can change by several kilohertz. The drift is dominated by “pink noise,” caused by tiny qubit manufacturing faults. Compared to the qubit's fluctuations, the control electronics' frequency drift is often less than one hertz. The qubit is the main cause of frequency instability.

In-Control Signal Background Noise

Also essential is the microwave pulse background noise utilised to operate qubits. Signal-to-noise ratio (SNR), which compares the intended signal to background noise, is often used to evaluate these pulses. Additive noise may diminish gate quality by randomly activating the qubit and diverging from its intended state. By intentionally adding noise to control pulses in studies, the gate error rate climbs exponentially as SNR lowers. SNR is crucial for high-fidelity quantum control, as shown in this study.

Issues with Material Defects

TLS—microscopic material defects and qubit resonant interactions—cause most noise fluctuations in superconducting processors. The qubit's relaxation time (T₁) may alter due to unexpected interactions. Due to oscillations, error correction noise models might lose accuracy and the device's performance stability and uniformity. Some interactions can lead a qubit's T₁ value to change by nearly 300% in 60 hours.

Noise Characterisation Techniques

Many models and experiments are used to quantify and understand noise sources.

Ramsey Experiments: Long-term frequency variations of a qubit imitate the noise spectrum and predict its effect on gate fidelity. Varying SNR: Researchers can immediately see how gate fidelity affects SNR by adding controlled noise to control pulses.

Randomised benchmarking: This method determines single-qubit gate error rates. Experimental data and simulations that simply account for qubit decoherence can help scientists distinguish the error contribution from the control system and other fundamental sources.

Noise Modelling: The sparse Pauli-Lindblad (SPL) model provides a scalable framework for estimating gate noise. These models help capture noise features, reducing errors.

Enhancing Performance with Characterisation

The ultimate goal of noise characterisation is to improve quantum computing accuracy. Understand the noise environment to develop efficient error mitigation solutions like ZNE and PEC. These strategies use noise knowledge to correct compute results, improving accuracy without ideal hardware.

New stabilisation methods have been developed because to greater understanding of noise instabilities, especially qubit-TLS interactions. With the "optimised noise strategy," qubit coherence is improved by continuously monitoring the TLS environment and adjusting control parameters to avoid strong interaction durations. In the "averaged noise strategy," oscillations are reduced by averaging over different noise environments, making the system more stable and predictable.