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Suppressing qubit dephasing using real-time Hamiltonian estimation.

Shulman MD, Harvey SP, Nichol JM, Bartlett SD, Doherty AC, Umansky V, Yacoby A - Nat Commun (2014)

Bottom Line: Strategies such as environmental and materials engineering, quantum error correction and dynamical decoupling can mitigate decoherence, but generally increase experimental complexity.Here we improve coherence in a qubit using real-time Hamiltonian parameter estimation.Because the technique demonstrated here is compatible with arbitrary qubit operations, it is a natural complement to quantum error correction and can be used to improve the performance of a wide variety of qubits in both meteorological and quantum information processing applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA.

ABSTRACT
Unwanted interaction between a quantum system and its fluctuating environment leads to decoherence and is the primary obstacle to establishing a scalable quantum information processing architecture. Strategies such as environmental and materials engineering, quantum error correction and dynamical decoupling can mitigate decoherence, but generally increase experimental complexity. Here we improve coherence in a qubit using real-time Hamiltonian parameter estimation. Using a rapidly converging Bayesian approach, we precisely measure the splitting in a singlet-triplet spin qubit faster than the surrounding nuclear bath fluctuates. We continuously adjust qubit control parameters based on this information, thereby improving the inhomogenously broadened coherence time (T2*) from tens of nanoseconds to >2 μs. Because the technique demonstrated here is compatible with arbitrary qubit operations, it is a natural complement to quantum error correction and can be used to improve the performance of a wide variety of qubits in both meteorological and quantum information processing applications.

No MeSH data available.


Related in: MedlinePlus

ΔBz diffusion.(a) The coherence time,  using the adaptive control and for a simulation show a peak, indicating that there is an optimal number of measurements to make when estimating ΔBz. (b) When many time traces of ΔBz are considered, their variance grows linearly with time, indicating a diffusion process. (c) The scaling of  as a function of Tdelay for software scaled data is consistent with diffusion of ΔBz. The red line is a fit to a diffusion model. (d) The performance of the Bayesian estimate of ΔBz can be estimated using software post-processing, giving , which corresponds to a precision of .
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f4: ΔBz diffusion.(a) The coherence time, using the adaptive control and for a simulation show a peak, indicating that there is an optimal number of measurements to make when estimating ΔBz. (b) When many time traces of ΔBz are considered, their variance grows linearly with time, indicating a diffusion process. (c) The scaling of as a function of Tdelay for software scaled data is consistent with diffusion of ΔBz. The red line is a fit to a diffusion model. (d) The performance of the Bayesian estimate of ΔBz can be estimated using software post-processing, giving , which corresponds to a precision of .

Mentions: Although the estimation scheme employed here is theoretically predicted to improve monotonically with N (ref. 13), we find that there is an optimum (N≈120), after which slowly decreases with increasing N (Fig. 4a). A possible explanation for this trend is fluctuation of the nuclear gradient during the estimation period. To investigate this, we obtain time records of ΔBz using the Bayesian estimate and find that its variance increases linearly in time at the rate of (6.7±0.7 kHz)2 μs−1 (Fig. 4c). The observed linear behaviour suggests a model where the nuclear gradient diffuses, which can arise, for example, from dipolar coupling between adjacent nuclei. Using the measured diffusion of ΔBz, we simulate the performance of the Bayesian estimate as a function of N (see Methods). Given that the simulation has no free parameters, we find good agreement with the observed , indicating that indeed, diffusion limits the accuracy with which we can measure ΔBz (Fig. 4a).


Suppressing qubit dephasing using real-time Hamiltonian estimation.

Shulman MD, Harvey SP, Nichol JM, Bartlett SD, Doherty AC, Umansky V, Yacoby A - Nat Commun (2014)

ΔBz diffusion.(a) The coherence time,  using the adaptive control and for a simulation show a peak, indicating that there is an optimal number of measurements to make when estimating ΔBz. (b) When many time traces of ΔBz are considered, their variance grows linearly with time, indicating a diffusion process. (c) The scaling of  as a function of Tdelay for software scaled data is consistent with diffusion of ΔBz. The red line is a fit to a diffusion model. (d) The performance of the Bayesian estimate of ΔBz can be estimated using software post-processing, giving , which corresponds to a precision of .
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214408&req=5

f4: ΔBz diffusion.(a) The coherence time, using the adaptive control and for a simulation show a peak, indicating that there is an optimal number of measurements to make when estimating ΔBz. (b) When many time traces of ΔBz are considered, their variance grows linearly with time, indicating a diffusion process. (c) The scaling of as a function of Tdelay for software scaled data is consistent with diffusion of ΔBz. The red line is a fit to a diffusion model. (d) The performance of the Bayesian estimate of ΔBz can be estimated using software post-processing, giving , which corresponds to a precision of .
Mentions: Although the estimation scheme employed here is theoretically predicted to improve monotonically with N (ref. 13), we find that there is an optimum (N≈120), after which slowly decreases with increasing N (Fig. 4a). A possible explanation for this trend is fluctuation of the nuclear gradient during the estimation period. To investigate this, we obtain time records of ΔBz using the Bayesian estimate and find that its variance increases linearly in time at the rate of (6.7±0.7 kHz)2 μs−1 (Fig. 4c). The observed linear behaviour suggests a model where the nuclear gradient diffuses, which can arise, for example, from dipolar coupling between adjacent nuclei. Using the measured diffusion of ΔBz, we simulate the performance of the Bayesian estimate as a function of N (see Methods). Given that the simulation has no free parameters, we find good agreement with the observed , indicating that indeed, diffusion limits the accuracy with which we can measure ΔBz (Fig. 4a).

Bottom Line: Strategies such as environmental and materials engineering, quantum error correction and dynamical decoupling can mitigate decoherence, but generally increase experimental complexity.Here we improve coherence in a qubit using real-time Hamiltonian parameter estimation.Because the technique demonstrated here is compatible with arbitrary qubit operations, it is a natural complement to quantum error correction and can be used to improve the performance of a wide variety of qubits in both meteorological and quantum information processing applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA.

ABSTRACT
Unwanted interaction between a quantum system and its fluctuating environment leads to decoherence and is the primary obstacle to establishing a scalable quantum information processing architecture. Strategies such as environmental and materials engineering, quantum error correction and dynamical decoupling can mitigate decoherence, but generally increase experimental complexity. Here we improve coherence in a qubit using real-time Hamiltonian parameter estimation. Using a rapidly converging Bayesian approach, we precisely measure the splitting in a singlet-triplet spin qubit faster than the surrounding nuclear bath fluctuates. We continuously adjust qubit control parameters based on this information, thereby improving the inhomogenously broadened coherence time (T2*) from tens of nanoseconds to >2 μs. Because the technique demonstrated here is compatible with arbitrary qubit operations, it is a natural complement to quantum error correction and can be used to improve the performance of a wide variety of qubits in both meteorological and quantum information processing applications.

No MeSH data available.


Related in: MedlinePlus