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Quantum sensing with tunable superconducting qubits: optimization and speed-up
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Quantum sensing with tunable superconducting qubits: optimization and speed-up
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Sensing and metrology play an important role in fundamental science and applications by fulfilling the ever-present need for more precise data sets and by allowing researchers to make more reliable conclusions on the validity of theoretical models. Sensors are ubiquitous. They are used in applications across a diverse range of fields including gravity imaging, geology, navigation, security, timekeeping, spectroscopy, chemistry, magnetometry, healthcare, and medicine. Current progress in quantum technologies has inevitably triggered the exploration of the use of quantum systems as sensors with new and improved capabilities. This article describes the optimization of the quantum-enhanced sensing of external magnetic fluxes with a Kitaev phase estimation algorithm based on a sensor with tunable transmon qubits. It provides the optimal flux biasing point for sensors with different maximal qubit transition frequencies. An estimation of decoherence rates is made for a given design. The use of $2-$ and $3-$qubit entangled states for sensing are compared in simulation with the single qubit case. The flux sensing accuracy reaches $10^{-8}\cdot\Phi_0$ and scales with time as $\sim\ 1/t$ which proves the speed-up of sensing with high ultimate accuracy.
Forward citations
Cited by 3 Pith papers
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Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference
Joint-DP co-optimizes sensing geometry with Bellman-optimal adaptive policies via differentiable dynamic programming and relaxations, scaling to photonic designs exceeding 10^5 pixels.
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Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network
A dilated causal CNN quantized to fixed point and synthesized to an FPGA detects charge jumps in superconducting qubits at 6.19 μs latency with 0.843 efficiency, close to the 0.866 of the offline χ2 method on |Δq|∈[0.1,0.5]e.
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Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference
Joint dynamic programming co-optimizes continuous hardware geometry and Bellman-optimal adaptive policies, yielding large gains over baselines in radar POMDPs, qubit sensors, and 90k-pixel photonic metasensors.
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