Improving Qubit Readout with Hidden Markov Models

Abstract

We demonstrate the application of pattern recognition algorithms via hidden Markov models (HMM) for qubit readout. This scheme provides a state-path trajectory approach capable of detecting qubit state transitions and makes for a robust classification scheme with higher starting state assignment fidelity than when compared to a multivariate Gaussian (MVG) or a support vector machine (SVM) scheme. Therefore, the method also eliminates the qubit-dependent readout time optimization requirement in current schemes. Using a HMM state discriminator we estimate fidelities reaching the ideal limit. Unsupervised learning gives access to transition matrix, priors, and IQ distributions, providing a toolbox for studying qubit state dynamics during strong projective readout.

arXiv comment10 pages, 10 figures

References in corpus (24)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Cavity quantum electrodynamics for superconducting electrical circuits: an architecture for quantum computation
• Quantum information processing with superconducting circuits: a review
• Realization of Three-Qubit Quantum Error Correction with Superconducting Circuits
• Approaching Unit Visibility for Control of a Superconducting Qubit with Dispersive Readout
• Detecting arbitrary quantum errors via stabilizer measurements on a sublattice of the surface code
• AC-Stark Shift and Dephasing of a Superconducting Qubit Strongly Coupled to a Cavity Field
• Robust determination of molecular spectra on a quantum processor
• Digital quantum simulation of fermionic models with a superconducting circuit
• Realizing Rapid, High-Fidelity, Single-Shot Dispersive Readout of Superconducting Qubits
• Dispersive regime of circuit QED: photon-dependent qubit dephasing and relaxation rates
• Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice
• Thermal and Residual Excited-State Population in a 3D Transmon Qubit
• Protocols for optimal readout of qubits using a continuous quantum nondemolition measurement
• Machine learning for discriminating quantum measurement trajectories and improving readout
• High-fidelity gates in a Josephson qubit
• Single-shot readout of multiple nuclear spin qubits in diamond under ambient conditions
• Restless Tuneup of High-Fidelity Qubit Gates
• Fast high fidelity quantum non-demolition qubit readout via a non-perturbative cross-Kerr coupling
• Rigorous measurement error correction
• High-fidelity software-defined quantum logic on a superconducting qudit
• Machine learning assisted readout of trapped-ion qubits
• Role of relaxation in the quantum measurement of a superconducting qubit using a nonlinear oscillator
• Robust readout of bosonic qubits in the dispersive coupling regime

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• Parallel tomography of quantum non-demolition measurements in multi-qubit devices
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