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Researcher profile

Jiaming Li

Jiaming Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.quant-gasArtificial IntelligenceComputer VisionMachine Learningphysics.app-phphysics.ins-detphysics.opticsquant-ph

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Published work

5 published item(s)

preprint2026arXiv

Accelerating evaporative cooling of a strongly interacting Fermi gas by tilting the optical trap with a magnetic field gradient

We present a rapid evaporative cooling scheme for a strongly interacting $^{6}\mathrm{Li}$ Fermi gas in an optical dipole trap. The method uses a magnetic-field-gradient--induced tilt of the trapping potential to accelerate cooling in the unitarity-limited regime. In evaporation based only on lowering the optical trap depth, the unitarity-limited scattering cross section can support runaway cooling; however, the cooling rate slows around $T/T_F \simeq 0.5$, and the runaway behavior is no longer maintained. We improve on this approach by applying a magnetic-field gradient when the gas temperature reaches about half the Fermi temperature. The induced tilt opens an escape channel for energetic atoms while keeping the trap frequencies nearly unchanged. This modification increases the cooling speed and cools the gas below the superfluid transition temperature, reaching $T/T_F = 0.16$ on a timescale of $\sim 25\,\mathrm{ms}$. Our results provide a simple and robust route for rapidly cooling a strongly interacting Fermi gas into the superfluid regime, facilitating studies of the physics of unitary Fermi superfluids.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment

Reinforcement learning (RL) has shown extraordinary potential in aligning diffusion models to downstream tasks, yet most of them still suffer from significant reward hacking, which degrades generative diversity and quality by inducing visual mode collapse and amplifying unreliable rewards. We identify the root cause as the mode-seeking nature of these methods, which maximize expected reward without effectively constraining probability distribution over acceptable trajectories, causing concentration on a few high-reward paths. In contrast, we propose Trajectory Matching Policy Optimization (TMPO), which replaces scalar reward maximization with trajectory-level reward distribution matching. Specifically, TMPO introduces a Softmax Trajectory Balance (Softmax-TB) objective to match the policy probabilities of K trajectories to a reward-induced Boltzmann distribution. We prove that this objective inherits the mode-covering property of forward KL divergence, preserving coverage over all acceptable trajectories while optimizing reward. To further reduce multi-trajectory training time on large-scale flow-matching models, TMPO incorporates Dynamic Stochastic Tree Sampling, where trajectories share denoising prefixes and branch at dynamically scheduled steps, reducing redundant computation while improving training effectiveness. Extensive results across diverse alignment tasks such as human preference, compositional generation and text rendering show that TMPO improves generative diversity over state-of-the-art methods by 9.1%, and achieves competitive performance in all downstream and efficiency metrics, attaining the optimal trade-off between reward and diversity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Beam pointing stabilization of an acousto-optic modulator with thermal control

Diffraction beams generated by an acousto-optic modulator (AOM) are widely used in various optical experiments, some of which require high angular stability with the temporal modulation of optical power. Usually, it is difficult to realize both angular stability and high-power modulation in a passive setup without a servo system of radio-frequency compensation. Here, we present a method to suppress the angular drift and pointing noise only with the thermal management of the AOM crystal. We analyze the dependence of the angular drift on the refractive index variation, and find that the angular drift is very sensitivity to the temperature gradient which could induce the refractive index gradient inside the AOM crystal. It reminds us such angular drift could be significantly suppressed by carefully overlapping the zero temperature gradient area with the position of the acousto-optic interaction zone. We implement a water-cooling setup, and find that the angular drift of an AOM is reduced over 100 times during the thermal transient, and the angular noise is also suppressed to 1/3 of the non-cooled case. It should be emphasized that this thermal control method is a general to suppress the beam drift in both the diffraction and the perpendicular-to-diffraction directions. The refractive index thermal coefficient of tellurium dioxide crystal at 1064 nm determined by this angular drift-temperature model is 16$\times$10$^{-6}$ K$^{-1}$ consistent with previous studies. This thermal control technique provides potential applications for optical trapping and remote sensoring that demand for intensity ramps.

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preprint2022arXiv

Controllable Production of Degenerate Fermi Gases of $^6$Li Atoms in the 2D-3D Crossover

The many-body physics in the dimensional crossover regime attracts much attention in cold atom experiments, but yet to explore systematically. One of the technical difficulties existed in the experiments is the lack of the experimental technique to quantitatively tune the atom occupation ratio of the different lattice bands. In this letter, we report such techniques in a process of transferring a 3D Fermi gas into a 1D optical lattice, where the capability of tuning the occupation of the energy band is realized by varying the trapping potentials of the optical dipole trap (ODT) and the lattice, respectively. We could tune a Fermi gas with the occupation in the lowest band from unity to 50$\%$ quantitatively. This provides a route to experimentally study the dependence of many-body interaction on the dimensionality in a Fermi gas.

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preprint2020arXiv

Observation of two PT transitions in an electric circuit with balanced gain and loss

We investigate PT -symmetry breaking transitions in a dimer comprising two LC oscillators, one with loss and the second with gain. The electric energy of this four-mode model oscillates between the two LC circuits, and between capacitive and inductive energy within each LC circuit. Its dynamics are described by a non-Hermitian, PT -symmetric Hamiltonian with three different phases separated by two exceptional points. We systematically measure the eigenfrequencies of energy dynamics across the three regions as a function of gain-loss strength. In addition to observe the well-studied PT transition for oscillations across the two LC circuits, at higher gain-loss strength, transition within each LC circuit is also observed. With their extraordinary tuning ability, PT -symmetric electronics are ideally suited for classical simulations of non-Hermitian systems

0 citations0 reviewsNo code linkedOpen access