Implementation of Multi-Harmonic Controlled Noise Drowning with Subharmonic Driving (MHCND-SD) to enhance qubit stability using Qiskit.
This repository demonstrates the Multi-Harmonic Controlled Noise Drowning with Subharmonic Driving (MHCND-SD) methodology for enhancing qubit stability in quantum computing, implemented in Qiskit.
MHCND-SD combines controlled noise drowning with subharmonic driving frequencies to mitigate high-frequency environmental noise, thereby extending qubit coherence times. This approach offers a scalable, energy-efficient alternative to traditional quantum error correction methods. For more information, please see the full paper in this repo.
- notebooks/: Contains Jupyter Notebooks with code implementing the MHCND-SD approach in Qiskit.
- paper/: Holds the original research paper for this project, titled "Multi-Harmonic Subharmonic Driving and Controlled Noise Drowning for Enhanced Qubit Stability."
- images/: Contains visualizations, including the plot of state probabilities over time under MHCND-SD.
The MHCND-SD method enhances qubit coherence by introducing controlled noise and multi-harmonic subharmonic driving. This approach reduces sensitivity to environmental noise and provides a robust buffer against decoherence. Simulations in Qiskit validate the MHCND-SD approach, showing its potential to enhance qubit stability.
The following example initializes qubit frequencies and sets up subharmonic driving frequencies as used in the MHCND-SD methodology:
# Initialize qubit frequencies and subharmonic driving frequencies
omega_q1 = 2 * np.pi * 5.0 # Qubit 1 frequency (5 GHz)
omega_d1 = omega_q1 / 2 # First harmonic (1/2) for both qubits
omega_d2 = omega_q1 / 3 # Second harmonic (1/3)
omega_d3 = omega_q1 / 4 # Third harmonic (1/4)
# Amplitudes for each harmonic
A_d1 = 0.05 * omega_q1 # Amplitude for first harmonic
A_d2 = 0.05 * omega_q1 # Amplitude for second harmonic
A_d3 = 0.05 * omega_q1 # Amplitude for third harmonicThe plot below shows the state probabilities over time under the MHCND-SD protocol with stochastic noise:
Figure 1: State probabilities of the 2-qubit system simulated over 500 ns under MHCND-SD protocol with stochastic noise.
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S. E. Nigg, H. Paik, B. Vlastakis, G. Kirchmair, S. Shankar, L. Frunzio, M. H. Devoret, and R. J. Schoelkopf, "Fast superconducting qubit control with sub-harmonic drives," arXiv preprint arXiv:2306.10162, 2023. [Online]. Available: https://arxiv.org/abs/2306.10162
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J. Schirk, M. Singh, L. Södergren, E. Dionis, D. Sugny, M. Werninghaus, K. Liegener, C. M. F. Schneider, and S. Filipp, "Protected Fluxonium Control with Sub-harmonic Parametric Driving," arXiv preprint arXiv:2410.00495, 2024. [Online]. Available: https://arxiv.org/abs/2410.00495
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J. P. Santos, L. C. Céleri, G. T. Landi, and M. Paternostro, "Reservoir engineering for maximally efficient quantum engines," Phys. Rev. Res., vol. 2, p. 043419, 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevResearch.2.043419
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K. Fujii and K. Nakajima, "Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices," arXiv preprint arXiv:2011.04890, 2020. [Online]. Available: https://arxiv.org/abs/2011.04890
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T. O. MacLean, T. F. O'Brien, P. S. Żuchowski, and D. Jaksch, "Quantum Reservoir Computing Using Arrays of Rydberg Atoms," PRX Quantum, vol. 3, p. 030325, 2022. [Online]. Available: https://link.aps.org/doi/10.1103/PRXQuantum.3.030325