SearcharxivSearch

arXiv · 2309.06734

Reversible tuning of nanowire quantum dot to atomic transitions

Abstract

Quantum dots embedded in semiconductor photonic nanowires (NW-QDs) can deterministically produce single-photons and entangled photon pairs at high repetition rates. These photons can be efficiently coupled from the photonic nanowire into free space or optical fibers thanks to the sharp tip of the nanowire, which provides impedance matching. However, precise control of the NW-QD emission frequency in a way that is reversible, does not degrade the properties of the emitted photons, and can be used independently for individual NW-QDs on the same chip has so far remained a challenge. Resolving this issue is crucial for applications when interfacing the photons with quantum systems that require MHz to sub-GHz precision, such as atomic ensembles acting as memories in a quantum network. Here, we demonstrate a reversible tuning method that can tune the emission frequency of a NW-QD by more than 300 GHz with sub-GHz precision. We achieve this through gas condensation that is then partially reversed with localized laser ablation. This process finely adjusts stress applied to the quantum dots, thereby tuning their emission frequency. We validate the precision and stability of this method by tuning the frequency of the emitted single-photons across an atomic resonance to probe its absorption and dispersion. We observed up to 80\% absorption of the single-photons from NW-QD in hot caesium vapour at the D1-line resonances and a 75-fold decrease in group velocity associated with the hyperfine transitions of the D1-line ground states. We observed no discernible effects in the second-order autocorrelation function, lifetime, or linewidth of the NW-QD emission for up to 300 GHz of tuning and we saw minimal effects on the fine structure splitting of the NW-QD when tuning up to 100 GHz.

Explore related subjects

Keep this discovery

BibTeXRIS

Rubayet Al Maruf, Sreesh Venuturumilli, Divya Bharadwaj, Paul Anderson, Jiawei Qiu, Yujia Yuan, Mohd Zeeshan, Behrooz Semnani, Philip J. Poole, Dan Dalacu, Kevin Resch, Michael E. Reimer, Michal Bajcsy. 2023-09-13. Reversible tuning of nanowire quantum dot to atomic transitions. https://arxiv.org/abs/2309.06734

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Probing the Error-Mitigation Threshold with Matrix Product States

Quantum error mitigation relies on accurate noise characterization, but mismatches between the actual and characterized noise can be amplified and drive a sharp threshold between successful and failed mitigation. In random circuits, this threshold maps onto a random-field Ising transition, but previous exact numerics were limited to small one-dimensional and all-to-all systems, leaving explicit two-dimensional architectures unresolved. We develop a fixed-bond-dimension matrix-product-state method for the replicated transfer dynamics that extends threshold calculations beyond exact propagation while retaining the finite-size signatures of the transition. At system sizes beyond previous exact studies, we recover the predicted absence of a threshold for quenched disorder in 1D, obtain a sharper annealed all-to-all critical point, and resolve architecture-dependent finite-depth thresholds in 2D square and heavy-hex circuits. These results establish replicated tensor-network dynamics as a practical tool for probing error-mitigation thresholds in large and higher-dimensional noisy circuits.

quant-ph

Low-cost algorithm-to-execution framework for surface-code quantum computing

The execution of useful quantum algorithms on fault-tolerant processors requires more than a mapping from logical gates to encoded operations: the spatial organization, non-Clifford resource supply, and execution schedule must also be determined while keeping physical overhead within practical limits. Although the theoretical hierarchy from logical circuits to fault-tolerant operations is well established, these implementation choices are often specified and optimized separately. Here we develop a low-cost algorithm-to-execution framework for surface-code quantum computing. From hierarchical algorithm descriptions, it constructs dependency-preserving logical schedules and an executable workload capturing logical interactions, operation parallelism, and time-resolved non-Clifford demand, thereby linking logical computation to surface-code organization, resource-state preparation, and fault-tolerant execution in a traceable workflow. We apply the framework to twenty benchmark circuits across seven algorithm families and a hierarchically composed application-scale elliptic-curve discrete-logarithm workload. Physical costs vary substantially even for circuits with similar logical resource counts. Under our direct-rotation calibration, non-Clifford implementation selection reduces space-time volume by up to 241.5 times versus an all-synthesis baseline for the QAOA amplitude-amplification workload. Circuit-specific surface-code layouts reduce routed-latency estimates for all twenty benchmarks; thirteen also reduce space-time volume because communication savings outweigh added spatial overhead. These results show that low-cost fault-tolerant execution depends on computation scheduling and organization, not aggregate logical resource counts alone.

quant-ph

Sample-optimal learning of stabilizer states

It is well-known that learning a pure $n$-qubit stabilizer state $|\psi\rangle$ both requires, and can be accomplished with, access to a number of copies of $|\psi\rangle$ linear in $n$. However, the precise constant coefficient of this scaling does not appear to have been determined. Here we prove that $L_\delta(n)$, the smallest number of copies from which a quantum procedure can identify any stabilizer state with failure probability at most $0<\delta<1/8$, satisfies $n+\lceil\log_2(1/\delta)\rceil-3\leq L_\delta(n)\leq n+\left\lceil\log_2(1/\delta)\right\rceil+4$. We present a polynomial-time quantum learning algorithm that saturates this bound, achieving a constant factor improvement in sample-complexity over previously known approaches. As an immediate corollary, we obtain via the Choi-Jamiolkowski isomorphism an algorithm for learning an unknown $n$-qubit Clifford unitary from $2n+\left\lceil\log_2(1/\delta)\right\rceil+4$ queries, the $n$-dependence of which we show to be optimal. Our proof technique, which involves Fourier analysis on the abelian group $\mathbb{Z}_4^n \times \mathbb{F}_2^{n(n-1)/2}$, seems to be qualitatively different to previous approaches to stabilizer state learning, and may be of some independent interest; in particular, it admits natural generalisations to further problems in quantum learning theory.

quant-ph