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Optimal control of a quantum sensor: A fast algorithm based on an analytic solution

Antonello Scardicchio, Santiago Hernández-Gómez, Federico Balducci, Giovanni Fasiolo, Paola Cappellaro, Nicole Fabbri

Published May 17, 2022• 3 citations
Publisher

Abstract

Abstract Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of finding the optimal pulsed control field can be mapped to the determination of the ground state of a spin chain. We find an approximate but analytic solution of this problem, which provides a \emph{lower bound} for the sensor sensitivity, and a pulsed control very close to optimal, which we further use as initial guess for realizing a fast simulated annealing algorithm. We experimentally demonstrate the sensitivity improvement for a spin-qubit magnetometer based on a nitrogen-vacancy center in diamond.

DOI
10.21203/rs.3.rs-1526494/v1

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Abstract Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of finding the optimal pulsed control field can be mapped to the determination of the ground state of a spin chain. We find an approximate but analytic solution of this problem, which provides a \emph{lower bound} for the sensor sensitivity, and a pulsed control very close to optimal, which we further use as initial guess for realizing a fast simulated annealing algorithm. We experimentally demonstrate the sensitivity improvement for a spin-qubit magnetometer based on a nitrogen-vacancy center in diamond.
Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of finding the pulsed control field that optimizes the sensitivity (i.e., the smallest detectable signal) can be mapped to the determination of the ground state of a spin chain. We find an approximate but analytic solution of this problem, which provides a lower bound for the sensitivity and a pulsed control very close to optimal, which we further use as initial guess for realizing a fast simulated annealing algorithm. We experimentally demonstrate the sensitivity improvement for a spin-qubit magnetometer based on a nitrogen-vacancy center in diamond.
The nitrogen-vacancy (NV) center in diamond is a quantum defect in diamond with unique properties for use in high-sensitive, high-resolution quantum sensors of magnetic fields. One of the most interesting and challenging application of NV quantum sensors is nanoscale magnetic resonance imaging (nano-MRI), which would enable to address single biomolecules. To this goal, improving the sensitivity of the NV sensor is a crucial task. Here, we present a quantum optimal control method that optimizes the sensitivity of NV sensor to specific weak magnetic signals with biologically-relevant, complex spectrum. The method, based on the mapping of the sensing problem on a problem of energy optimization of an Ising chain, allows us to improve sensitivity by three orders of magnitude compared to standard control sequences.
Designing optimal control for multiparameter quantum sensing is essential for approaching the ultimate precision limits. However, analytical solutions are generally available only for simple systems, while realistic scenarios often involve coupled qubits and time-dependent Hamiltonians. Here we numerically investigate optimal control of a two-qubit sensor-ancilla system coupled via an Ising term using Gradient Ascent Pulse Engineering (GRAPE) to minimize the objective function. By seeding the optimization recursively with solutions obtained for smaller coupling strengths and selecting a suitable initial guess, we achieve robust convergence and high precision across a wide range of interaction strengths and field configurations. The proposed approach offers a practical route toward high-sensitivity, robust multiparameter magnetometry and it is applicable to solid-state quantum sensors such as nitrogen-vacancy (NV) centers in realistic experimental settings.
Quantum systems are powerful detectors with wide-ranging applications from scanning probe microscopy of materials to biomedical imaging. Nitrogen vacancy (NV) centers in diamond, for instance, can be operated as qubits for sensing of magnetic field, temperature, or related signals. By well-designed application of pulse sequences, experiments can filter this signal from environmental noise, allowing extremely sensitive measurements with single NV centers. Recently, optimal control has been used to further improve sensitivity by modification of the pulse sequence, most notably by optimal placement of πππ pulses. Here we consider extending beyond πππ pulses, exploring optimization of a continuous, time-dependent control field. We show that the difficulty of optimizing these protocols can be mapped to the difficulty of finding minimum free energy in a classical frustrated spin system. While most optimizations we consider show autocorrelations of the sensing protocol that grow as a power law -- similar to an Ising spin glass -- the continuous control shows slower logarithmic growth, suggestive of a harder Heisenberg-like glassy landscape.
Decoherence is a major challenge for quantum technologies, whose negative effects can be mitigated by quantum optimal control. The decoherence dynamics significantly varies depending on the characteristics of the system’s environment, consequently affecting the optimization outcomes. In this work, we investigate the optimization of a negatively charged nitrogen-vacancy ( NV − ) center qubit-spin-inversion control pulse, and observe two error-compensation mechanisms, depending on how long the correlation time is compared to the pulse duration. Additionally, we analyze the effects of control space constraints and optimization options to identify a strategy to improve the optimization performance. Finally, we present experimental realizations of the optimized pulses, exhibiting the applicability of the procedure. Our work serves as a generic yet essential guide to implementing optimal control in the presence of realistic noise.
The development of efficient algorithms that generate robust quantum controls is crucial for the realization of quantum technologies. The commonly used gradient-based optimization algorithms are limited by their sensitivity to the initial guess, which affects their performance. Here we propose combining the gradient method with the simulated annealing technique to formulate a hybrid algorithm. Our numerical analysis confirms its superior convergence rate. Using the hybrid algorithm, we generate spin-selective $\ensuremath{\pi}$ pulses and employ them for experimental measurement of local noise spectra in a three-qubit nuclear magnetic resonance system. Moreover, here we describe a general method to construct noise-resilient quantum controls by incorporating noisy fields within the optimization routine of the hybrid algorithm. Upon experimental comparison with similar sequences obtained from standard algorithms, we find remarkable robustness of the hybrid sequences against dephasing errors.