Adaptive characterization of spatially inhomogeneous fields and errors in qubit registers
| Autor: | Alistair Milne, Michael J. Biercuk, Riddhi Swaroop Gupta, Cornelius Hempel, Claire Edmunds |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Quantum decoherence
Computer Networks and Communications Computer science business.industry Statistical and Nonlinear Physics 01 natural sciences Automation lcsh:QC1-999 lcsh:QA75.5-76.95 010305 fluids & plasmas Computational Theory and Mathematics Control theory Qubit 0103 physical sciences Computer Science (miscellaneous) Electronic engineering Adaptive learning lcsh:Electronic computers. Computer science Quantum information 010306 general physics business Particle filter lcsh:Physics Quantum computer |
| Zdroj: | npj Quantum Information, Vol 6, Iss 1, Pp 1-10 (2020) |
| ISSN: | 2056-6387 |
| Popis: | New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for calibration or 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 adaptive learning framework, Noise Mapping for Quantum Architectures (NMQA), for 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 20× (3×) 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. |
| Databáze: | OpenAIRE |
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