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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

Experimental apparatus.(a) A scanning electron microscope image of the double QD with a schematic of the apparatus used for adaptive qubit control. A floating metal gate protruding from the right can be seen which increases the capacitance between the qubit and an adjacent qubit (not pictured), which is left inactive for this work. The reflected read-out drive signal is demodulated to DC, digitized by a correlated double sampler (CDS), and ΔBz is estimated in real time by the field-programmable gate array (FPGA). The FPGA updates the digital to analogue converter (DAC) to keep the voltage controlled oscillator (VCO) resonant with the estimated value of ΔBz. The VCO controls the voltage detuning, ε(t) between the QDs, which, in turn, modulates J at ΩJ. (b) The Bloch sphere representation for the S−T0 qubit showing the two axes of control, J and ΔBz.
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f1: Experimental apparatus.(a) A scanning electron microscope image of the double QD with a schematic of the apparatus used for adaptive qubit control. A floating metal gate protruding from the right can be seen which increases the capacitance between the qubit and an adjacent qubit (not pictured), which is left inactive for this work. The reflected read-out drive signal is demodulated to DC, digitized by a correlated double sampler (CDS), and ΔBz is estimated in real time by the field-programmable gate array (FPGA). The FPGA updates the digital to analogue converter (DAC) to keep the voltage controlled oscillator (VCO) resonant with the estimated value of ΔBz. The VCO controls the voltage detuning, ε(t) between the QDs, which, in turn, modulates J at ΩJ. (b) The Bloch sphere representation for the S−T0 qubit showing the two axes of control, J and ΔBz.

Mentions: The quantum system that we study is a singlet-triplet (S−T0) qubit45 which is formed by two gate-defined lateral quantum dots (QDs) in a GaAs/AlGaAs heterostructure (Fig. 1a, Supplementary Fig. 1), similar to that of refs 6, 7. The qubit can be rapidly initialized in the singlet state /S› in ≈20 ns and read out with 98% fidelity in ≈1 μs (refs 8, 9; Supplementary Fig. 2). Universal quantum control is provided by two distinct drives10: the exchange splitting, J, between /S› and /T0›, and the magnetic field gradient, ΔBz, due to the hyperfine interaction with host Ga and As nuclei. The Bloch sphere representation for this qubit can be seen in Fig. 1b. In this work, we focus on qubit evolution around ΔBz (Fig. 2a). Due to statistical fluctuations of the nuclei, ΔBz varies randomly in time, and consequently oscillations around this field gradient decay in a time (ref. 4). A nuclear feedback scheme relying on dynamic nuclear polarization11 can be employed to set the mean gradient, (g*μBΔBz/h≈60 MHz in this work) as well as reduce the variance of the fluctuations. Here, g*≈−0.44 is the effective gyromagnetic ratio in GaAs, μB is the Bohr magneton and h is Planck’s constant. In what follows, we adopt units where g*μB/h=1. The nuclear feedback relies on the avoided crossing between the /S› and /T+› states. When the electrons are brought adiabatically through this crossing, their total spin changes by Δms=±1, which is accompanied by a nuclear spin flip to conserve angular momentum. With the use of this feedback, the coherence time improves to (ref. 11; Fig. 2b), limited by the low nuclear pumping efficiency10. Crucially, the residual fluctuations are considerably slower than the timescale of qubit operations12.


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)

Experimental apparatus.(a) A scanning electron microscope image of the double QD with a schematic of the apparatus used for adaptive qubit control. A floating metal gate protruding from the right can be seen which increases the capacitance between the qubit and an adjacent qubit (not pictured), which is left inactive for this work. The reflected read-out drive signal is demodulated to DC, digitized by a correlated double sampler (CDS), and ΔBz is estimated in real time by the field-programmable gate array (FPGA). The FPGA updates the digital to analogue converter (DAC) to keep the voltage controlled oscillator (VCO) resonant with the estimated value of ΔBz. The VCO controls the voltage detuning, ε(t) between the QDs, which, in turn, modulates J at ΩJ. (b) The Bloch sphere representation for the S−T0 qubit showing the two axes of control, J and ΔBz.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Experimental apparatus.(a) A scanning electron microscope image of the double QD with a schematic of the apparatus used for adaptive qubit control. A floating metal gate protruding from the right can be seen which increases the capacitance between the qubit and an adjacent qubit (not pictured), which is left inactive for this work. The reflected read-out drive signal is demodulated to DC, digitized by a correlated double sampler (CDS), and ΔBz is estimated in real time by the field-programmable gate array (FPGA). The FPGA updates the digital to analogue converter (DAC) to keep the voltage controlled oscillator (VCO) resonant with the estimated value of ΔBz. The VCO controls the voltage detuning, ε(t) between the QDs, which, in turn, modulates J at ΩJ. (b) The Bloch sphere representation for the S−T0 qubit showing the two axes of control, J and ΔBz.
Mentions: The quantum system that we study is a singlet-triplet (S−T0) qubit45 which is formed by two gate-defined lateral quantum dots (QDs) in a GaAs/AlGaAs heterostructure (Fig. 1a, Supplementary Fig. 1), similar to that of refs 6, 7. The qubit can be rapidly initialized in the singlet state /S› in ≈20 ns and read out with 98% fidelity in ≈1 μs (refs 8, 9; Supplementary Fig. 2). Universal quantum control is provided by two distinct drives10: the exchange splitting, J, between /S› and /T0›, and the magnetic field gradient, ΔBz, due to the hyperfine interaction with host Ga and As nuclei. The Bloch sphere representation for this qubit can be seen in Fig. 1b. In this work, we focus on qubit evolution around ΔBz (Fig. 2a). Due to statistical fluctuations of the nuclei, ΔBz varies randomly in time, and consequently oscillations around this field gradient decay in a time (ref. 4). A nuclear feedback scheme relying on dynamic nuclear polarization11 can be employed to set the mean gradient, (g*μBΔBz/h≈60 MHz in this work) as well as reduce the variance of the fluctuations. Here, g*≈−0.44 is the effective gyromagnetic ratio in GaAs, μB is the Bohr magneton and h is Planck’s constant. In what follows, we adopt units where g*μB/h=1. The nuclear feedback relies on the avoided crossing between the /S› and /T+› states. When the electrons are brought adiabatically through this crossing, their total spin changes by Δms=±1, which is accompanied by a nuclear spin flip to conserve angular momentum. With the use of this feedback, the coherence time improves to (ref. 11; Fig. 2b), limited by the low nuclear pumping efficiency10. Crucially, the residual fluctuations are considerably slower than the timescale of qubit operations12.

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