Assuming post-quantum one-way functions, we construct device-independent oblivious transfer and bit commitment that tolerate independent honest faults at a constant rate and adaptive, bidirectional quantum leakage between the laboratories at a constant rate. Honest parties use trusted classical computation to operate untrusted quantum devices, which may share arbitrary entanglement and perform joint measurements without assumptions of independence or identical distribution. Security is simulation-based against quantum polynomial-time adversaries and composes sequentially with efficient simulators. For security parameter $λ$ and a prescribed total leakage budget $\ell$, one complete one-bit oblivious transfer uses $Θ(\ell+\log^2λ)$ device coordinates in at most three single-use device windows, including preprocessing, and polynomial classical computation and communication. The leakage budget is shared across these windows. The commitment has efficient simulators against both parties and yields device-independent coin tossing with abort; each uses one window. Completeness of oblivious transfer gives device-independent secure computation with abort for every efficiently computable classical functionality on a fixed number of parties, against static corruption of any proper subset, provided each reached call to oblivious transfer satisfies the device and budget conditions. The construction combines short selectors with code-based choice masking, a global audit, and error correction across field symbols. The commitment extractor changes a classical code coset while leaving the device execution and its leakage unchanged. Sender security uses a global Fourier analysis and a selector-aware Magic Square repetition bound derived from the two-round threshold theorem of Kundu and Tan (2023).
We study the problem of learning arbitrary $n$-qubit Lindbladians, the generators of Markovian open-system dynamics, from their time evolution. We give the first known efficient algorithm that learns arbitrary Lindbladians without any structural assumptions. For an $n$-qubit Lindbladian of dynamical strength at most $Λ$, the algorithm estimates every coefficient to error $\varepsilon$ using $\widetilde O(Λ^2/\varepsilon^2)$ experiments and $\widetilde O(Λ/\varepsilon^2)$ total evolution time, with classical running time polynomial in $n$ and $Λ/\varepsilon$. Both experimental costs match known lower bounds up to polylogarithmic factors. The algorithm identifies candidate labels through product Pauli experiments and estimates their coefficients using random stabilizer preparations and Clifford measurements. It requires no ancillas or control during the unknown evolution. For constant $k$, our algorithm learns generic $k$-local Lindbladians of local dynamical strength at most $Λ_{\mathrm{loc}}$ using $\widetilde O(n^2Λ_{\mathrm{loc}}^2/\varepsilon^2)$ experiments and $\widetilde O(nΛ_{\mathrm{loc}}/\varepsilon^2)$ total evolution time, improving the upper bounds of prior works in terms of the dependence on $n$. We further improve the lower bounds of Arad, Chen, Guo, Rebentrost, and Yu (SODA 2027) using a family of $3$-local Lindbladians. They already hold when the support is known, and only one Hamiltonian coefficient is requested, even for adaptive protocols with arbitrary ancillas and quantum memory. These lower bounds establish that our algorithm is optimal up to polylogarithmic factors in both experiment count and total evolution time for every fixed $k \geq 3$.