A network-matrix framework for superconducting circuits yields a quantization algorithm predicting that some conjugate variables drop out of the Hamiltonian, plus tools for circuit decomposition and model extraction.
Efficiently Building and Characterizing Electromagnetic Models of Multi-Qubit Superconducting Circuits
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abstract
In an attempt to better leverage superconducting quantum computers, scaling efforts have become the central concern. These efforts have been further exacerbated by the increased complexity of these circuits. The added complexity can introduce parasitic couplings and resonances, which may hinder the overall performance and scalability of these devices. We explore a method of modeling and characterization based on multiport impedance functions that correspond to multi-qubit circuits. By combining vector fitting techniques with a novel method for interconnecting rational impedance functions, we are able to efficiently construct Hamiltonians for multi-qubit circuits using electromagnetic simulations. Our methods can also be applied to circuits that contain both lumped and distributed element components. The constructed Hamiltonians account for all the interactions within a circuit that are described by the impedance function. We then present characterization methods that allow us to estimate effective qubit coupling rates, state-dependent dispersive shifts of resonant modes, and qubit relaxation times.
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quant-ph 1years
2024 1verdicts
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Reciprocal lumped-element superconducting circuits: quantization, decomposition, and model extraction
A network-matrix framework for superconducting circuits yields a quantization algorithm predicting that some conjugate variables drop out of the Hamiltonian, plus tools for circuit decomposition and model extraction.