Autonomous adaptive noise characterization in quantum computers
arXiv:1904.07225 · doi:10.1038/s41534-020-0286-0
Abstract
New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for decoherence mitigation on neighboring data qubits, but little work has addressed how such schemes may be efficiently implemented in order to maximize information utilization. Techniques from classical estimation and dynamic control, suitably adapted to the strictures of quantum measurement, provide an opportunity to extract augmented hardware performance through automation of low-level characterization and control. In this work, we present an autonomous learning framework, Noise Mapping for Quantum Architectures (NMQA), for adaptive scheduling of sensor-qubit measurements and efficient spatial noise mapping (prior to actuation) across device architectures. Via a two-layer particle filter, NMQA receives binary measurements and determines regions within the architecture that share common noise processes; an adaptive controller then schedules future measurements to reduce map uncertainty. Numerical analysis and experiments on an array of trapped ytterbium ions demonstrate that NMQA outperforms brute-force mapping by up-to x (x) in simulations (experiments), calculated as a reduction in the number of measurements required to map a spatially inhomogeneous magnetic field with a target error metric. As an early adaptation of robotic control to quantum devices, this work opens up exciting new avenues in quantum computer science.
arXiv comment9 pages, 4 figures. (Supplements: 6 pages, 3 figures.)
References in corpus (17)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
- Large Scale Modular Quantum Computer Architecture with Atomic Memory and Photonic Interconnects
- Silicon CMOS architecture for a spin-based quantum computer
- Dynamically Error-Corrected Gates for Universal Quantum Computation
- Scalable Architecture for a Room Temperature Solid-State Quantum Information Processor
- Arbitrarily accurate composite pulses
- Experimental noise filtering by quantum control
- A General Transfer-Function Approach to Noise Filtering in Open-Loop Quantum Control
- Robustness of composite pulses to time-dependent control noise
- Prediction and real-time compensation of qubit decoherence via machine-learning
- Time-resolved magnetic sensing with electronic spins in diamond
- Real-time calibration with spectator qubits
- Integration of spectator qubits into quantum computer architectures for hardware tuneup and calibration
- Machine learning for predictive estimation of qubit dynamics subject to dephasing
- Experimental quantification of spatial correlations in quantum dynamics
- A functional basis for efficient physical-layer classical control in quantum processors
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- Integration of spectator qubits into quantum computer architectures for hardware tuneup and calibration
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- Surpassing spectator qubits with photonic modes and continuous measurement for Heisenberg-limited noise mitigation
- Adaptive filtering of projective quantum measurements using discrete stochastic methods
- A practical guide for building superconducting quantum devices
- Imperfection analyses for random-telegraph-noise mitigation using spectator qubits