Abstract
Quantum sensors are highly sensitive since they capitalise on fragile quantum properties such as coherence, while enabling ultrahigh spatial resolution. For sensing, the crux is to minimise the measurement uncertainty in a chosen range within a given time. However, basic quantum sensing protocols cannot simultaneously achieve both a high sensitivity and a large range. Here, we demonstrate a nonadaptive algorithm for increasing this range, in principle without limit, for alternatingcurrent field sensing, while being able to get arbitrarily close to the best possible sensitivity. Therefore, it outperforms the standard measurement concept in both sensitivity and range. Also, we explore this algorithm thoroughly by simulation, and discuss the T^{−2} scaling that this algorithm approaches in the coherent regime, as opposed to the T^{−1/2} of the standard measurement. The same algorithm can be applied to any modulolimited sensor.
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Introduction
Supreme sensitivities are realisable by exploiting the coherence of quantum sensors^{1}. For quantumsensing applications, nitrogenvacancy (NV) centres in diamond have attracted considerable attention due to their exceptional quantummechanical properties^{1,2}, including long spincoherence times^{3,4}, and due to their great potential for farfield optical nanoscopy^{5,6,7,8}. Furthermore, an increase in sensitivity can be gained for alternating current (AC) field sensing by prolonging the NV spin coherence with dynamical decoupling of the centre’s spin from its environment^{2,3,9,10,11,12}. Therefore, AC field sensing is applied in various areas of physics, chemistry and biology: to detect single spins^{13,14,15}, for nuclear magneticresonance of tiny samplevolumes^{16,17,18,19,20}, for nanoscale magneticresonance imaging^{13,21,22,23} and to search for new particles beyond the standard model^{24,25}. In these applications, both a wide range of the AC field amplitude and a high sensitivity are very important, because the magnitude of the AC field strongly depends on the distance r from the NV spin (r^{−3} in case of a magnetic dipole field). This outlines the most relevant variable for this field of research: the dynamic range, which is the ratio of the range to the sensitivity, the latter being a measure for the smallest measurable field amplitude.
In previous research, NV centres were utilised for sensitive highdynamic range direct current (DC) magnetic field measurements. A theory paper^{26} discussed the application of a more general phaseestimation method^{27} to a single NV nuclear spin in diamond, read out with singleshot measurements. They combined Ramsey interferometry on the nuclear spin with different delays to improve the sensitivity via Bayes’ theorem applied to binary data, which precision, given full visibility, scaled as \({T}_{{\rm{meas}}}^{1}\) (with T_{meas} the measurement time), dubbed Heisenberglike scaling^{28}. Adaptive^{27} and nonadaptive^{28,29} approaches were discussed, but they found to their surprise that under more realistic circumstances, only the nonadaptive method could still show sub\({T}_{{\rm{meas}}}^{0.5}\) scaling, by applying different amounts of iterations in a linear way^{29}. The range itself remained the same as with the standard measurement, but they improved the sensitivity for this range, hence improving the dynamic range. This theory was applied to the electron spin^{30} and the nuclear spin^{31} of the NV centres via the nonadaptive method. Indeed, they found that the uncertainty scaled sub\({T}_{{\rm{meas}}}^{0.5}\) (\({T}_{{\rm{meas}}}^{0.77}\)^{ 30} and \({T}_{{\rm{meas}}}^{0.85}\)^{ 31}), while they improved the dynamic range by 8.5^{30} and 7.4^{31}. More recently, in an experiment at low temperature the adaptive method showed improved results, with scaling close to \({T}_{{\rm{meas}}}^{1}\) and a claimed improvement (compared to refs. ^{30,31}) of the dynamic range by two orders of magnitude^{32}.
A similar method for AC magnetic field sensing applied different order dynamicdecoupling sequences^{33}. Their improvement of the dynamic range compared to a sequence with 16 πpulses was about 26, and they explored the effect of the phase of the measured field in depth. Besides, one of the advantages of the previously reported dynamical sensitivity control^{11} was the increase in the range by 4000 times, up to a theoretical maximum of 5000 times. Their uncertainty for a single measurement was about double that of a similar standard measurement, while the required multimeasurement for the large range worsened the sensitivity further (which is the uncertainty times \(\sqrt{{T}_{{\rm{meas}}}}\)) by \(\sqrt{{N}_{\phi }}\) with N_{ϕ} the number of phases applied in their method (the more phases, the larger the range, but each phase requires an additional measurement).
As to see why dynamicrange increasing algorithms are required, we look at the standard measurement. In the standard method to measure the AC magnetic field with NV centres with a synchronised Hahnecho measurement^{2,3,9,10} (Fig. 1b), after initialisation into a superposition state with a laser pulse and the first microwave (MW) π/2pulse, the AC magnetic field is applied. Hence, the spin rotates along the zaxis, thus its phase changes. Halfway the period of the magnetic field, a MW πpulse flips the spin, such that the phase accumulated during the negative half of the period doubles the acquired phase. The final phase is essentially converted into a population with a final MW π/2pulse before readout with a laser pulse. The larger the amplitude of the field, the further the spin rotates, thus the final phase of the spin relates directly to this amplitude.
However, the phase of the spin can be determined only within 2π at best, thus the range of amplitudes is limited. If the sensor is more sensitive, the spin accumulates more phase, thus it revolves for 2π for a smaller AC field amplitude already. Therefore, the more sensitive the system, the smaller the range is. Thus, to benefit from extremely sensitive sensors which utilise entanglement^{34,35,36} without the limitation of their minuscule range, it is important to increase this range, while retaining their high sensitivity (thus low uncertainty) as much as possible. Moreover, since the measurements of the electron spin of a single NV centre consist of iterating a sequence many times to accumulate sufficient signal (photons for NV centres), the uncertainty scales as \({T}_{{\rm{meas}}}^{0.5}\)^{ 37}.
In this work, we demonstrate and explore a nonadaptive algorithm for quantum sensors to measure AC fields with a large range for which the loss in sensitivity is negligible (thus maximising the dynamic range), both by measurement and extensive simulation. This shows that our algorithm scales nearly Heisenberglike (here \({T}_{{\rm{meas}}}^{2}\)) under realistic circumstances, thus even with the reduced contrast in the spin readout (normally about 30% for NV centres); we explain why this happens, and its importance. Finally, we establish with our algorithm how to increase the range beyond the limit given by the best possible standard measurement, which in principle allows to extend it without bound. Throughout this paper, we use the electron spin of a single NV centre to measure magnetic fields with the phase of the spin coherence. However, the insights of this paper remain the same for similar quantum systems.
Results
Base algorithm
We start with explaining the base of our algorithm (illustrated in Fig. 1), and we clarify the terms referred to throughout the paper and supplementary information. The standard measurement for AC magnetic fields, applying the Hahnecho sequence, has a limited range B_{range} = B_{period}/2 due to the sinusoidal shape (with period B_{period}) of the signal response to magnetic field amplitudes (Fig. 1a). The sensitivity is defined as \({\sigma }_{B}\sqrt{{T}_{{\rm{meas}}}}\) with σ_{B} the uncertainty of the sensed quantity (here magnetic field amplitude) and T_{meas} the measurement time. For this standard measurement, \({\sigma }_{B}={\sigma }_{S}/{{\rm{grad}}}_{\max }\) where σ_{S} is the uncertainty in the measured signal of a single measurement (in our case shotnoise limited), and \({{\rm{grad}}}_{\max }\) the maximum gradient in the response^{3} (for example at the inflection point of the sinusoid in Fig. 1a). Therefore for these measurements, the shorter B_{period} (thus the smaller the range), the steeper the slope, thus the more sensitive, as mentioned earlier.
For the maximum sensitivity, a standard Hahnecho sequence is performed over the full period of the magnetic field (Fig. 1b, for single NV centres this period should be shorter than about half the coherence time^{3}). Since the acquired phase of the spin is proportional to the area under the magnetic field curve (see Supplementary Information of ref. ^{3}), and hence B_{period} is proportional to this area as well, by reducing the measured area M times (Fig. 1b), the effective period increases by M (Fig. 1a, d). The time delay between the π/2pulses in the sequence follows from integration to compute the probed area (Fig. 1c). Hereafter, measuring an area A means applying a sequence with this calculated time delay, and A_{0} is the maximum area. Thus, performing a measurement with a sufficiently small area would be the simplest approach for a largerange measurement. However, roughly comparing with the measurement over the maximum area, using the same number of iterations of the sequence (thus σ_{S} is similar) and the same measurement time (no optimisations), the gradient for the reduced area \({{\rm{grad}}}_{\max ,M}={{\rm{grad}}}_{\max ,1}/M\), hence its sensitivity is M times worse.
To improve the sensitivity for a large range, initially, a number of measurements with different areas are combined to uniquely define the magnetic field amplitude in a range limited by the measurement with the smallest area (Fig. 1d). Consequently, only part of the measurement time is spent on the largest area, which has the best sensitivity (but a small range), while the remainder of the time is spent on areas with a worse sensitivity. Therefore, the sensitivity of the combined measurement is strictly worse than this best sensitivity. Using halved areas (hence requiring at least \({\mathrm{log}\,}_{2}\left(M\right)\) additional areas) and the same number of iterations for each area and using no optimisations, for roughly the same σ_{B}, the measurement time for the combined sequence \({T}_{{\rm{meas,}}M}=\lceil 1+{\mathrm{log}\,}_{2}\left(M\right)\rceil {T}_{{\rm{meas}},1}\). Thus, the sensitivity would become \(\sqrt{\lceil 1+{\mathrm{log}\,}_{2}\left(M\right)\rceil }\) times worse, which is already a significant improvement compared to the straightforward case in the last paragraph.
The measurements resulting from different areas are combined via Bayes’ theorem. For area A_{n}, the measurement gives signal S_{n} (for example the crosses/circles/triangles on the sinusoids in Fig. 1a, d for three areas). The posterior probability distribution for the magnetic field B given measured signal S_{n} is
with \(P\left(B\right)\) the prior distribution, \(P\left({S}_{n}\right)\) independent of B, and
with \(S\left(B\right)\) the relation between the signal S and the applied field B (the sinusoids in Fig. 1a, d, e). Fig. 1e visualises these equations. \(P\left({S}_{n} S\right)\) is a Poisson distribution (counting photons), but it can be approximated by a normal distribution (green line along yaxis in Fig. 1e) when more than ~10 photons arrive (with continuity correction). This is generally the case when the uncertainty is below the maximum uncertainty, as described later. For the first measurement, the prior distribution is flat since there is no initial knowledge about the field, and for the remainder of the measurements, the previous posterior is the new prior distribution. This results in a combined distribution as demonstrated in Fig. 1f.
Uncertainty
Before performing measurements and simulating the algorithm, a definition of merit is required that facilitates both the sensitivity and the range. Therefore, we choose the uncertainty in magnetic field σ_{B}, defined as the standard deviation of the magnetic field distribution centred around its maximum value. For sufficiently long measurement times, this gives the same result compared to applying the normal formula. However, the difference is visible for short measurement times, since it takes the range into account: we know the magnetic field is in the given range, which means that if the probability distribution is flat, the uncertainty is at its maximum \({\sigma }_{B,\max }={B}_{{\rm{range}}}/\sqrt{12}\) (see Supplementary Note 1). σ_{B} multiplied by \(\sqrt{{T}_{{\rm{meas}}}}\) gives the sensitivity, but this is unsuitable as figure of merit at short measurement times, since its limit is 0 nT Hz^{−1/2} for T_{meas} = 0 s while approaching the asymptotic maximum uncertainty.
At first, since the uncertainty in a range is limited by the worst uncertainty in this range, we simulated the homogeneity of the uncertainty in the complete range. For a standard measurement, the usually reported uncertainty (\({\sigma }_{B}={\sigma }_{S}/{{\rm{grad}}}_{\max }\)) is only true for a single magnetic field amplitude at infinite measurement time, but otherwise it is worse and inhomogeneous. By combining two measurements with the same area but their response shifted by a phase of π/2, the uncertainty becomes more homogeneous, and guarantees a lower uncertainty than the standard measurement across its range. Thus, such a measurement consists of two phases (see for example Supplementary Fig. 7a). The homogeneity is improved further by increasing the number of phases; four phases are used throughout this paper. Supplementary Note 2 describes the details of homogeneity for our algorithm, and for previous ones it is explored in ref. ^{38}. Since the uncertainty is nearly homogeneous, which field is applied is irrelevant while determining this uncertainty. Without prior knowledge or feedback, the uncertainty is ultimately limited by this combination of four phases for the largest area possible^{3}.
Measurement compared with simulation
For our measurements, we use an ntype diamond sample. This was epitaxially grown onto a Ibtype (111)oriented diamond substrate by microwave plasmaassisted chemicalvapour deposition with enriched ^{12}C (99.998%) and with a phosphorus concentration of ~6 × 10^{16} atoms cm^{−3}^{ 3,39}. We address individual electron spins residing in NV centres with a standard inhouse built confocal microscope. MW pulses are applied via a thin copper wire, while magnetic fields are induced with a coil around the sample. All experiments are conducted at room temperature. We use single NV centres with T_{2}s of about 2 ms.
We measure and simulate σ_{B} for five sequences to show the consistency between the measurements and simulations, and to get an idea of the working of the base of our algorithm. Please note that the only difference between our measurements and simulations is that the simulations calculate the signal otherwise measured using the known sequence, the set magnetic field amplitude, and the parameters of the measured NV centre. The analysis applied otherwise is exactly the same, thus realistic circumstances are simulated (small contrast, shotnoise as described in Supplementary Note 2, decay due to coherence time T_{2}). The first sequence measures the largest area (here a single period of the field); the second, third and fourth use half, a quarter and an eighth of the largest area; and the fifth sequence includes these four sequences equally in a separate measurement/simulation. All include four phases as mentioned in the last subsection. Initially, the objective is to investigate the details of the algorithm itself, hence to nullify artefacts stemming from overhead times (which are implementationdependent, and could include laser pulses, MW pulses and waiting times), these are ignored at first and explored in the discussion.
The results are shown in Fig. 2a, which reveals a number of important points. Firstly, the measurements closely match simulations. Secondly, below a certain measurement time, no knowledge about the field is gained, and hence σ_{B} is at its maximum. Thirdly, for longer measurement times, σ_{B} scales as \({T}_{{\rm{meas}}}^{0.5}\). Fourthly, for the combined sequence there is a region in T_{meas} where σ_{B} scales more steeply (here referred to as the steep region). Finally, as explained in the basealgorithm subsection, the uncertainty of the combined sequence is always higher than the uncertainty of the largestarea sequence, since the former spends measurement time on sequences other than this largestarea sequence which has the lowest uncertainty. Of course, the advantage of the combined sequence over the largestarea sequence is its larger range (please remember that \({B}_{{\rm{range}}}\propto {\sigma }_{B,\max }\), see Supplementary Note 1).
Algorithm design
To design our eventual algorithm, its principle is explored in more detail with additional simulations. Fig. 3a shows the result for changing the relative number of iterations for each area, which reveals that there is a tradeoff between the lowest uncertainty reached for measurement times at the steep region and at long measurement times. In other words, depending on T_{meas}, a different relative number of iterations gives the lowest uncertainty. When fixing these (Figs. 2a and 3a), the uncertainty is not optimised, and thus it can display very steep curves that can be tuned to even subHeisenberglike scaling (for example \({T}_{{\rm{meas}}}^{4.0}\) in Fig. 3a).
For our algorithm, we optimise the relative number of iterations at each measurement time to minimise the uncertainty. The result for this measurementtimewise optimisation is plotted in Fig. 3b. This shows that the longer T_{meas}, the closer the sensitivity gets to its ultimate limit, where the scaling approaches \({T}_{{\rm{meas}}}^{0.5}\). At the steep region of this optimum, the scaling is \({T}_{{\rm{meas}}}^{0.98}\).
When we look at Fig. 3c, which depicts the relative number of iterations, we can understand how our algorithm works. For very short T_{meas}, all measurement time is allotted to the smallest area, since the larger areas are at their maximum uncertainty and hence cannot contribute. But for longer T_{meas}, at some time the next area becomes relevant and thus turns on, since it can receive sufficient measurement time to lower σ_{B} below its maximum uncertainty. This continues until the largest area turns on, which then keeps increasing in relative importance, at which point the scaling of the uncertainty is about \({T}_{{\rm{meas}}}^{0.5}\). Thus for longer T_{meas}, the largest area receives increasingly more relative measurement time, meaning the uncertainty continuously approaches this ultimate uncertainty, as plotted by the green dashed line in Fig. 3c.
If we would increase the number of areas in the sequence, the uncertainty becomes steeper during the turningon region (which is the steep region). Figure 3d plots the result for a large amount of areas, indicating that the uncertainty scales as \({T}_{{\rm{meas}}}^{2}\) up to nearby the largest area. The scaling follows from the quadratic dependence of the area on the subsequence length (see Fig. 1c). Since closer to the largest area, this is not quadratic yet, it becomes less steep (lowest yellow crosses in Fig. 3d). The decay in coherence due to the finite T_{2} negatively effects the uncertainty as well in this region, further decreasing the steepness. Analogue for DC measurements, the uncertainty scales as \({T}_{{\rm{meas}}}^{1}\) in the steep region. Supplementary Note 3 discusses scaling in more detail beyond the indication given here. When taking any overhead time into account, the effective measurement time decreases, thus the curves would become even steeper.
So far in the examples with our algorithm, we used halved areas (A_{n} = A_{0}/2^{n} for integer n ≥ 0). Even though the uncertainty is mostly defined by the largest area, and the range by the smallest, the middle areas are important for reaching the lowest uncertainty (see Fig. 3c: they partake in the optimal combination). Adding more areas at integer multiples of the smallest area decreases the uncertainty, though slightly (see Supplementary Note 4).
Algorithm measurement
In Fig. 2b, measurement results of our algorithm in the steep region are plotted (for details of the measurement see Supplementary Note 5), together with the Heisenberg limit (which is only true for a small range and infinite T_{2}) and the approximate largerange limit explained in Supplementary Note 3. As mentioned before, and just like in Fig. 3d, the focus is on the scaling that originates from the algorithm, hence all overhead time is ignored. Our algorithm is very close to the limit, as could be expected since at long measurement times most time is spent on the sequence with the largest area. Moreover, our results scale approximately as \({T}_{{\rm{meas}}}^{1.6}\), which is less steep than the Heisenberglike scaling of \({T}_{{\rm{meas}}}^{2}\), since our algorithm keeps approaching this limit.
When merely halving areas in a measurement sequence, its range is defined by the smallest area. Therefore, it would only improve the uncertainty with respect to the standard singlearea measurement, but not the range. In this way, given a limit on the time delay between the π/2pulses, for example owing to a maximum time resolution or waiting time requirements, the maximum range is restricted. However, the range of our algorithm is the inverse of the greatest common divisor of the frequencies in measured signal of all included areas (see Supplementary Note 6). For halved areas, since all larger areas are integer multiples of the smaller ones, this means that the greatest common divisor is the lowest frequency, thus the one related to the smallest area. To increase the range beyond this limit, we combine areas that are not integer multiples of each other. When purely looking at the range, combining two sequences for slightly different areas increases the range far beyond the standard measurement’s range. Thus in principle, the range can be extended unlimitedly. Adding the large areas as well, it is still possible to get arbitrarily close to the ultimate uncertainty (for details see Supplementary Note 6).
The dynamic range of our algorithm is explored with measurements in Fig. 2c, which plots the sensitivity with respect to the range of the measurement sequence. Initially, for each increase in the range, an additional subsequence of half the smallest area is added. However, for the final four ranges, a single area is added at 1.5, 1.25, 1.1 or 1.05 times the smallest area. To compare with shorter sequences and with other results fairly, the sensitivity is chosen instead of the uncertainty (to calculate the dynamic range) and the overhead time is still ignored. It is computed by combining measurements from both the left side and right side of the designed range (as explained before, given the homogeneity of the uncertainty in our algorithm, the applied magnetic field does not matter). The sensitivity for the standard measurement with the same range is plotted as well (derived from the smallest area of our algorithm), and the sensitivity of the most sensitive sequence (derived from the largest area of our algorithm), the latter having a small range only (~10^{2} nT). Our algorithm is nearly as sensitive as the most sensitive sequence, and its range can go beyond that of a standard measurement. In these measurements, the maximum range was limited by our equipment only, and could be improved further.
Discussion
Given a fixed sequence, a subsequence contributes only to the result when the measurement time it receives is sufficiently long to lower the measured uncertainty below its maximum (see Fig. 3c). Therefore in our algorithm, the optimum sequence for a given measurement time includes contributing subsequences only. On the contrary, when combining subsequences in a fixed way with the least sensitive subsequence measured most often, for short measurement times, the more sensitive subsequences do not contribute, and hence their measurement time is wasted. This results in a steeper measurement time dependence in the same way as overhead time does. This illustrates one conclusion of Supplementary Note 3: a steeper dependence leads to a worse algorithm, since when decreasing the measurement time, the uncertainty increases more quickly for a steeper curve.
For our algorithm, if it is possible to choose for which subsequence to increase the number of iterations while measuring, the uncertainty can be minimised for all measurement times (red line in Fig. 3b), since the absolute number of iterations for each subsequence is monotonically increasing over measurement time (see Supplementary Note 7). Please note that it is known beforehand for which subsequence to increase the number of iterations, it does not depend on the measurement results, thus it is a nonadaptive method. Moreover, since our algorithm spends most time on the largest area, any overhead time (which is generally independent of the subsequence) is relatively as short as possible. This is visualised in Fig. 2c, which plots the sensitivities both with and without all potential overhead times, illustrating the overhead is negligible indeed.
For measurement times in the steep region, since quantum sensing is generally chosen for its high sensitivity, a sensor would rather unlikely be used given the high uncertainty. Therefore, this region and its scaling are fairly irrelevant: if a short measurement time is desired, less subsequences are required, which effectively puts the sensor just at the inflection point (when scaling starts to be \({T}_{{\rm{meas}}}^{0.5}\)).
For measurement times beyond the steep region, σ_{B} and thus sensitivity are very close to the limit for a homogeneous range. This is still about \(\sqrt{2}\) worse than the standard sensitivity for a single field at infinite measurement time (Supplementary Fig. 2). It is possible to improve towards this by applying feedback of intermediate results during the measurement, and dropping all but two phases in the process to focus on the two phases with the field to measure located at their maximum gradient, which gives the smallest uncertainty. There is a tradeoff between added complexity of such an adaptive measurement^{32} (realtime processing of data, changing the sequence during the measurement and/or set any phase in the measurement instead of just four with inphasequadrature modulation) and gained sensitivity (\(\sqrt{2}\) at best for infinite measurement time), even when ignoring the processing overhead. Moreover, even under these ideal circumstances, the dynamic range, rather relevant for largerange measurements, is actually \(\sqrt{2}\) worse for standard adaptive measurements compared to nonadaptive measurements (see Supplementary Note 2).
An important point ignored so far is how to implement this algorithm at all for AC fields, since its shape needs to be taken into account. As opposed to DC measurements, where the area can be reduced simply by shortening the time delay between the π/2 pulses proportionally, for AC it is more complicated, as illustrated in Fig. 1b, c. Moreover, it might seem that for each iteration another period of the magnetic field is required (resulting in the practical but nonoptimal sensitivity plotted with pentagons in Fig. 2c), while for DC all measurements can be strung together, the latter limiting the measurement time. However, something similar is possible for AC fields, since the DC part cancels, as explained in Supplementary Note 8. In our results, we neglected the effect this stringing has on the total measurement time to focus on the working of the algorithm. However, since AC stringing is only slightly less effective than DC stringing, and since often most measurement time is dedicated to the largest area (which has no stringing disadvantage), it justifies the choice to ignore the overhead time of these stringing effects. This is explored in detail in Supplementary Note 8, which describes how to design compact measurement sequences (resulting in the practical closertooptimal sensitivity plotted with circles in Fig. 2c). Measurements with these compact sequences illustrate that the practical sensitivity, compared to the overheadignored sensitivity, would worsen with about 5% when all overhead time is included using a basic sequence design (see Supplementary Fig. 10).
Additionally, please note that the description of the algorithm focussed on areas to easily translate it to any field, such as DC fields or square waves. Moreover, the frequency of the AC field is not relevant, since for lower frequencies the largest area will not span a whole period, while for high frequencies additional πpulses are required to optimise the largest area. This defines the lowest uncertainty, which our algorithm approaches for every situation. This uncertainty increases for lower frequencies, since a smaller area is measured within the coherencelimited time delay, while for higher frequencies it decreases, due to the increase in coherence time by a dynamicdecoupling sequence (just for the larger areas, the largest defining the lowest uncertainty). Of course, the shape of the area vs timedelay graph (Fig. 1c) depends on the shape of the field and the chosen pulse sequences.
For practical implementations of the algorithm, as in the example with a single NV centre, the reader is advised that the larger the range becomes, the more prominent the effects of offresonance MW pulses become. For DC, this is even more important (see for example ref. ^{31}), while for AC, the pulses are often near low fields (for example for the sensitivitydefining large area they are at the inflection points). Thus, care should be taken during the design depending on the chosen quantum system and the available technology.
As a final remark, applying the optimal number of iterations for a long measurement time gives a small chance to conclude the wrong field, since relatively little time is spent in the smaller rangedefining areas (see Supplementary Note 9). However, the analysis does not return a single measured field amplitude, but a probability distribution of the field. As demonstrated in Supplementary Note 9, when the field is within a few σ_{B} of the actual field, there is a single pronounced peak in this distribution. Oppositely, there are multiple strong peaks if the expected field of the measurement is significantly different. Thus, such a result could easily be discarded (of course effectively slightly reducing the sensitivity to redo the measurement for these cases).
To conclude, we have introduced an ultrahigh dynamicrange algorithm for measuring magnetic fields with a quantum sensor, such as a single NV centre, for which the uncertainty, and hence sensitivity, can be arbitrarily close to the ultimate uncertainty/sensitivity by increasing the measurement time. The maximum range depends on the smallest difference in areas attainable, which results in a larger range than possible with a standard measurement. As example, we demonstrated a dynamic range of ~ 10^{7}, an improvement of two orders of magnitude compared to previous algorithms^{32} (please note that a fair comparison between algorithms corrects the results for the coherence time, the minimum/maximum time delays, the applied spinmeasurement method and the experimental equipment, as these change the results independent of the applied algorithm). Moreover, we explained the origin of Heisenberglike scaling in algorithms and why steeper scaling indicates a worse algorithm. Our algorithm and its implications are the same for other modulolimited sensors, thus it paves the way to optimally benefit from extremely sensitive entanglementbased sensors for largerange applications.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
The authors acknowledge the financial support from MEXT QLEAP (No. JPMXS0118067395), KAKENHI (No. 15H05868, 16H06326) and the Collaborative Research Program of ICR, Kyoto University (2019103). They also thank Prof H. Kosaka for helpful discussions.
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E.D.H. designed the algorithm, performed the experiments/simulations/analyses and conceived the supplementary; H.K. grew the phosphorusdoped diamond, assisted by T.M. and S.Y.; N.M. supervised the work; E.D.H. and N.M. wrote the manuscript, and all authors discussed it.
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Herbschleb, E.D., Kato, H., Makino, T. et al. Ultrahigh dynamic range quantum measurement retaining its sensitivity. Nat Commun 12, 306 (2021). https://doi.org/10.1038/s4146702020561x
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DOI: https://doi.org/10.1038/s4146702020561x
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