arXiv subjects

Jiazhao Tian

Publications and source records attributed to Jiazhao Tian.

9 recordsLinked to original sources

arXiv2026

Quantum Lock-In Detection via Successive Adiabatic Evolution

In recent years, quantum lock-in detection has emerged as a promising technique to accurately detect weak signals submerged in background noise. However, the signal-to-noise ratio of existing protocols is severely limited by spectral leakage resulting from control operations implemented in pulse form. Here, we propose a general protocol for realizing quantum lock-in detection by employing successive quantum adiabatic evolution. In our protocol, the signal modulation is achieved by adiabatically controlling the time evolution of the quantum probe, which enables the implementation of sinusoidal modulation functions. The realization of sinusoidal-wave modulation substantially mitigates the problem of spectral leakage and facilitates the extraction of the complete characteristics of the target signals. We present a practical implementation scheme of adiabatic quantum lock-in detection based on nitrogen-vacancy centers in diamond, and demonstrate that the proposed protocol possesses strong resilience against experimental imperfections. Our results establish adiabatic quantum lock-in detection as a robust and experimentally accessible approach to detection of weak alternating signals in noisy environments, thus promoting the advance of real-world quantum sensing technologies.

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arXiv2026

Quantum compressed sensing

How many measurements are fundamentally required to capture a signal. Shannon's information theory established the bedrock of this question in 1948, the Nyquist Shannon theorem set the first answer, and compressed sensing (CS) rewrote it in 2006 by reducing the required measurement number to M = O(Klog(N/K)) for a K sparse signal. Here, we propose quantum compressed sensing (QCS), a paradigm that reframes signal acquisition as a unitary quantum evolution. By encoding high dimensional signal information into a single quantum probe state, then introducing domain-alignment evolution,a physically realizable unitary transformation that maps the sparse basis directly onto the measurement basis. QCS executes the support-set search at the quantum level without consuming measurement trials. The logarithmic penalty vanishes, compressing the required measurement number from the classical bound to M =O(K) and reducing reconstruction from ill posed optimization to linear estimation. We experimentally validate QCS using frequency and time domain sparse signals, confirming that the measurement number scales linearly with sparsity and decouples entirely from the signal dimension. Our work provides a physical pathway toward ultimate information acquisition efficiency, with broad implications for sensing, imaging, and communication.

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arXiv2026

High-resolution wide-field magnetic imaging with sparse sampling using nitrogen-vacancy centers

Nitrogen-vacancy (NV) centers in diamond enable quantitative magnetic imaging, yet practical implementations must balance spatial resolution against acquisition time (and thus per-pixel sensitivity). Single-NV scanning magnetometry achieves genuine nanoscale resolution, nonetheless requires typically a slow pixel-by-pixel acquisition. Meanwhile, wide-field NV-ensemble microscopy provides parallel readout over a large field of view, however is jointly limited by the optical diffraction limit and the sensor-sample standoff. Here, we present a sparse-sampling strategy for reconstructing high-resolution wide-field images from only a small number of measurements. Using simulated NV-ensemble detection of ac magnetic fields, we show that a mean-adjusted Bayesian estimation (MABE) framework can reconstruct 10000-pixel images from only 25 sampling points, achieving SSIM values exceeding 0.999 for representative smooth field distributions, while optimized dynamical-decoupling pulse sequences yield an approximately twofold improvement in magnetic-field sensitivity. The method further clarifies how sampling patterns and sampling density affect reconstruction accuracy and suggests a route toward faster and more scalable magnetic-imaging architectures that may extend to point-scanning NV sensors and other magnetometry platforms, such as SQUIDs, Hall probes, and magnetic tunnel junctions.

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arXiv2024

Rapid transform optimisation strategy for decoherence-protected quantum register in diamond

Decoherence-protected spins associated with nitrogen-vacancy color centers in diamond possess remarkable long coherence time, which make them one of the most promising and robust quantum registers. The current demand is to explore practical rapid control strategies for preparing and manipulating the such register. Our work provides all-microwave control strategies optimized using multiple optimization methods to significantly reduce the processing time by $80\%$ with a set of smooth near-zero-endpoints control fields that are shown to be experimentally realizable. Furthermore, we optimize and analyze the robustness of these strategies under frequency and amplitude imperfections of the control fields, during which process we use only $16$ samples to give a fair estimation of the robustness map with $2500$ pixels. Overall, we provide a ready-to-implement recipe to facilitate high-performance information processing via decoherence-protected quantum register for future quantum technology applications.

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arXiv2023

Single-pixel imaging based on deep learning

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still impede the practical application of single-pixel imaging. Recently, deep learning has been introduced into single-pixel imaging, which has attracted a lot of attention due to its exceptional reconstruction quality, fast reconstruction speed, and the potential to complete advanced sensing tasks without reconstructing images. Here, this advance is discussed and some opinions are offered. Firstly, based on the fundamental principles of single-pixel imaging and deep learning, the principles and algorithms of single-pixel imaging based on deep learning are described and analyzed. Subsequently, the implementation technologies of single-pixel imaging based on deep learning are reviewed. They are divided into super-resolution single-pixel imaging, single-pixel imaging through scattering media, photon-level single-pixel imaging, optical encryption based on single-pixel imaging, color single-pixel imaging, and image-free sensing according to diverse application fields. Finally, major challenges and corresponding feasible approaches are discussed, as well as more possible applications in the future.

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arXiv2023

Bayesian-based hybrid method for rapid optimization of NV center sensors

NV center is one of the most promising platforms in the field of quantum sensing. Magnetometry based on NV center, especially, has achieved a concrete development in regions of biomedicine and medical diagnostics. Improving the sensitivity of NV center sensor under wide inhomogeneous broadening and filed amplitude drift is one crucial issue of continuous concern, which relies on the coherent control of NV center with higher average fidelity. Quantum optimal control (QOC) methods provide access to this target, nevertheless the high time consumption of current methods due to the large number of needful sample points as well as the complexity of the parameter space has hindered their usability. In this paper we propose the Bayesian estimation phase-modulated (B-PM) method to tackle this problem. In the case of state transforming of NV center ensemble, the B-PM method reduces the time consumption by more than $90\%$ compared to the conventional standard Fourier base (SFB) method while increasing the average fidelity from $0.894$ to $0.905$. In AC magnetometry scenery, the optimized control pulse given by B-PM method achieves a eight-fold extension of the coherence time $T_2$ compared to rectangular $π$ pulse. Similar application can be made in other sensing situations. As a general algorithm, the B-PM method can be further extended to open- and closed-loop optimization of complex systems based on a variety of quantum platforms.

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arXiv2022

Optimal quantum optical control of spin in diamond

The nitrogen-vacancy (NV) center spin represents an appealing candidate for quantum information processing. Besides the widely used microwave control, its coherent manipulation may also be achieved using laser as mediated by the excited energy levels. Nevertheless, the multiple levels of the excited state of NV center spin make the coherent transition process become complex and may affect the fidelity of coherent manipulation. Here, we adopt the strategy of optimal quantum control to accelerate coherent state transfer in the ground state manifold of NV center spin using laser. The results demonstrate improved performance in both the speed and the fidelity of coherent state transfer which will be useful for optical control of NV center spin in diamond.

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arXiv2020

Quantum optimal control using phase-modulated driving fields

Quantum optimal control represents a powerful technique to enhance the performance of quantum experiments by engineering the controllable parameters of the Hamiltonian. However, the computational overhead for the necessary optimization of these control parameters drastically increases as their number grows. We devise a novel variant of a gradient-free optimal-control method by introducing the idea of phase-modulated driving fields, which allows us to find optimal control fields efficiently. We numerically evaluate its performance and demonstrate the advantages over standard Fourier-basis methods in controlling an ensemble of two-level systems showing an inhomogeneous broadening. The control fields optimized with the phase-modulated method provide an increased robustness against such ensemble inhomogeneities as well as control-field fluctuations and environmental noise, with one order of magnitude less of average search time. Robustness enhancement of single quantum gates is also achieved by the phase-modulated method. Under environmental noise, an XY-8 sequence constituted by optimized gates prolongs the coherence time by $50\%$ compared with standard rectangular pulses in our numerical simulations, showing the application potential of our phase-modulated method in improving the precision of signal detection in the field of quantum sensing.

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arXiv2019

Pulsed Quantum-State Reconstruction of Dark Systems

We propose a novel strategy to reconstruct the quantum state of dark systems, i.e., degrees of freedom that are not directly accessible for measurement or control. Our scheme relies on the quantum control of a two-level probe that exerts a state-dependent potential on the dark system. Using a sequence of control pulses applied to the probe makes it possible to tailor the information one can obtain and, for example, allows us to reconstruct the density operator of a dark spin as well as the Wigner characteristic function of a harmonic oscillator. Because of the symmetry of the applied pulse sequence, this scheme is robust against slow noise on the probe. The proof-of-principle experiments are readily feasible in solid-state spins and trapped ions.

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