Efficient estimation of resonant coupling between quantum systems

Abstract

We present an efficient method for the characterization of two coupled discrete quantum systems, one of which can be controlled and measured. For two systems with transition frequencies ${\omega }_q$, ${\omega }_r$, and coupling strength $g$ we show how to obtain estimates of $g$ and ${\omega }_r$ whose error decreases exponentially in the number of measurement shots rather than as a power law expected in simple approaches. Our algorithm can thereby identify $g$ and ${\omega }_r$ simultaneously with high precision in a few hundred measurement shots. This is achieved by adapting measurement settings upon data as it is collected. We also introduce a method to eliminate erroneous estimates with small overhead. Our algorithm is robust against the presence of relaxation and typical noise. Our results are applicable to many candidate technologies for quantum computation, in particular, for the characterization of spurious two-level systems in superconducting qubits or stripline resonators.

5 pages, 4 figures, and supplemental material

References in corpus (8)

• Entanglement-free Heisenberg-limited phase estimation
• Nonlinear response of the vacuum Rabi resonance
• Entanglement-enhanced measurement of a completely unknown phase
• Quantum-enhanced optical phase tracking
• Hamiltonian Learning and Certification Using Quantum Resources
• Robust Online Hamiltonian Learning
• Quantum Hamiltonian Learning Using Imperfect Quantum Resources
• Two-Qubit Hamiltonian Tomography by Bayesian Analysis of Noisy Data

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