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Learning the Hamiltonian underlying a quantum many-body system in thermal equilibrium is a fundamental task in quantum learning theory and experimental sciences. To learn the Gibbs state of local Hamiltonians at any inverse temperature β, the state-of-the-art provable algorithms fall short of the optimal sample and computational complexity, in sharp contrast with the locality and simplicity in the classical cases. In this work, we present a learning algorithm that learns each local term of a n-qubit D-dimensional Hamiltonian to an additive error ϵ with sample complexity $$\tilde{O}\left(\frac{e^{\mathrm{poly}(\beta)}}{\beta^2\epsilon^2}\right)\log(n)$$. The protocol uses parallelizable local quantum measurements that act within bounded regions of the lattice and near-linear-time classical post-processing. Thus, our complexity is near optimal with respect to n, ϵ and is polynomially tight with respect to β. We also give a learning algorithm for Hamiltonians with bounded interaction degree with sample and time complexities of similar scaling on n but worse on β, ϵ. At the heart of our algorithm is the interplay between locality, the Kubo-Martin-Schwinger condition, and the operator Fourier transform at arbitrary temperatures. 

Abstract Incorporation of colloidal quantum emitters into silicon-based photonic devices would enable major advances in quantum optics. However, deterministic placement of individual sub-10 nm colloidal particles onto micron-sized photonic structures with nanometer-scale precision remains an outstanding challenge. Here, we introduce Cavity-Shape Modulated Origami Placement (CSMOP) that leverages the structural programmability of DNA origami to precisely deposit colloidal nanomaterials within lithographically-defined resist cavities. CSMOP enables clean and accurate patterning of origami templates onto photonic chips with high yields. Soft-silicification-passivation stabilizes deposited origamis, while preserving their binding sites to attach and align colloidal quantum rods (QRs) to control their nanoscale positions and emission polarization. We demonstrate QR integration with photonic device structures including waveguides, micro-ring resonators, and bullseye photonic cavities. CSMOP therefore offers a general platform for the integration of colloidal quantum materials into photonic circuits, with broad potential to empower quantum science and technology. 

We design a quantum algorithm for ground state preparation in the early fault tolerant regime. As a Monte Carlo style quantum algorithm, our method features a Lindbladian where the target state is stationary. The construction of this Lindbladian is algorithmic and should not be seen as a specific approximation to some weakly coupled system-bath dynamics in nature. Our algorithm can be implemented using just one ancilla qubit and efficiently simulated on a quantum computer. It can prepare the ground state even when the initial state has zero overlap with the ground state, bypassing the most significant limitation of methods like quantum phase estimation. As a variant, we also propose a discrete-time algorithm, demonstrating even better efficiency and providing a near-optimal simulation cost depending on the desired evolution time and precision. Numerical simulations using Ising and Hubbard models demonstrate the efficacy and applicability of our method. Published by the American Physical Society2024 

DNA nanotechnology has broad applications in biomedical drug delivery and pro- grammable materials. Characterization of the self-assembly of DNA origami and quan- tum dots (QDs) is necessary for the development of new DNA-based nanostructures. We use computation and experiment to show that the self-assembly of 3D hierarchi- cal nanostructures can be controlled by programming the binding site number and their positions on DNA origami. Using biotinylated pentagonal pyramid wireframe DNA origamis and streptavidin capped QDs, we demonstrate that DNA origami with 1 binding site at the outer vertex can assemble multi-meric origamis with up to 6 DNA origamis on 1 QD, and DNA origami with 1 binding site at the inner center can only assemble monomeric and dimeric origamis. Meanwhile, the yield percentages of differ- ent multi-meric origamis are controlled by the QD:DNA-origami stoichiometric mixing ratio. DNA origamis with 2 binding sites at the αγ positions (of the pentagon) make larger nanostructures than those with binding sites at the αβ positions. In general, increasing the number of binding sites leads to increases in the nanostructure size. At high DNA origami concentration, the QD number in each cluster becomes the limiting factor for the growth of nanostructures. We find that reducing the QD size can also affect the self-assembly because of the reduced access to the binding sites from more densely packed origamis. 

Abstract Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of short-range ordering (SRO) on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach. Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFEs). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress.