We generalize the classical shadow tomography scheme to a broad class of finite-depth or finite-time local unitary ensembles, known as locally scrambled quantum dynamics, where the unitary ensemble is invariant under local basis transformations. In t
his case, the reconstruction map for the classical shadow tomography depends only on the average entanglement feature of classical snapshots. We provide an unbiased estimator of the quantum state as a linear combination of reduced classical snapshots in all subsystems, where the combination coefficients are solely determined by the entanglement feature. We also bound the number of experimental measurements required for the tomography scheme, so-called sample complexity, by formulating the operator shadow norm in the entanglement feature formalism. We numerically demonstrate our approach for finite-depth local unitary circuits and finite-time local-Hamiltonian generated evolutions. The shallow-circuit measurement can achieve a lower tomography complexity compared to the existing method based on Pauli or Clifford measurements. Our approach is also applicable to approximately locally scrambled unitary ensembles with a controllable bias that vanishes quickly. Surprisingly, we find a single instance of time-dependent local Hamiltonian evolution is sufficient to perform an approximate tomography as we numerically demonstrate it using a paradigmatic spin chain Hamiltonian modeled after trapped ion or Rydberg atom quantum simulators. Our approach significantly broadens the application of classical shadow tomography on near-term quantum devices.
Spontaneous time-reversal symmetry (TRS) breaking plays an important role in studying strongly correlated unconventional superconductors. When the superconducting gap functions with different pairing symmetries compete, an Ising ($Z_2$) type symmetry
breaking occurs due to the locking of the relative phase $Deltatheta_{12}$ via a second order Josephson coupling. The phase locking can take place even in the normal state in the phase fluctuation regime before the onset of superconductivity. If $Deltatheta_{12}=pmfrac{pi}{2}$, then TRS is broken, otherwise, if $Deltatheta_{12}=0$, or, $pi$, rotational symmetry is broken leading to a nematic state. In both cases, the order parameters possess a 4-fermion structure beyond the scope of mean-field theory. We employ an effective two-component $XY$-model assisted by a renormalization group analysis to address this problem. In addition, a quartetting, or, charge-``4e, superconductivity can also occur above $T_c$. Monte-Carlo simulations are performed and the results are in a good agreement with the renormalization group analysis. Our results provide useful guidance for studying novel symmetry breakings in strongly correlated superconductors.
Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key idea of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative mode
l, called RG-Flow, which can separate information at different scales of images with disentangled representations at each scale. We demonstrate our method mainly on the CelebA dataset and show that the disentangled representations at different scales enable semantic manipulation and style mixing of the images. To visualize the latent representations, we introduce receptive fields for flow-based models and find that the receptive fields learned by RG-Flow are similar to those in convolutional neural networks. In addition, we replace the widely adopted Gaussian prior distribution by a sparse prior distribution to further enhance the disentanglement of representations. From a theoretical perspective, the proposed method has $O(log L)$ complexity for image inpainting compared to previous generative models with $O(L^2)$ complexity.
We present an algorithm for the generalized search problem (searching $k$ marked items among $N$ items) based on a continuous Hamiltonian and exploiting resonance. This resonant algorithm has the same time complexity $O(sqrt{N/k})$ as the Grover algo
rithm. A natural extension of the algorithm, incorporating auxiliary monitor qubits, can determine $k$ precisely, if it is unknown. The time complexity of our counting algorithm is $O(sqrt{N})$, similar to the best quantum approximate counting algorithm, or better, given appropriate physical resources.
