Fast optimization algorithms and the cosmological constant

Denef and Douglas have observed that in certain landscape models the problem of finding small values of the cosmological constant is a large instance of an NP-hard problem. The number of elementary operations (quantum gates) needed to solve this problem by brute force search exceeds the estimated computational capacity of the observable universe. Here we describe a way out of this puzzling circumstance: despite being NP-hard, the problem of finding a small cosmological constant can be attacked by more sophisticated algorithms whose performance vastly exceeds brute force search. In fact, in some parameter regimes the average-case complexity is polynomial. We demonstrate this by explicitly finding a cosmological constant of order $10^{-120}$ in a randomly generated $10^9$-dimensional ADK landscape.Comment: 19 pages, 5 figure

Quantum weight enumerators and tensor networks

We examine the use of weight enumerators for analyzing tensor network constructions, and specifically the quantum lego framework recently introduced. We extend the notion of quantum weight enumerators to so-called tensor enumerators, and prove that the trace operation on tensor networks is compatible with a trace operation on tensor enumerators. This allows us to compute quantum weight enumerators of larger codes such as the ones constructed through tensor network methods more efficiently. We also provide an analogue of the MacWilliams identity for tensor enumerators.Comment: 21 pages, 3 figures. Sets up the tensor enumerator formalis

Quantum Lattice Sieving

Lattices are very important objects in the effort to construct cryptographic primitives that are secure against quantum attacks. A central problem in the study of lattices is that of finding the shortest non-zero vector in the lattice. Asymptotically, sieving is the best known technique for solving the shortest vector problem, however, sieving requires memory exponential in the dimension of the lattice. As a consequence, enumeration algorithms are often used in place of sieving due to their linear memory complexity, despite their super-exponential runtime. In this work, we present a heuristic quantum sieving algorithm that has memory complexity polynomial in the size of the length of the sampled vectors at the initial step of the sieve. In other words, unlike most sieving algorithms, the memory complexity of our algorithm does not depend on the number of sampled vectors at the initial step of the sieve.Comment: A reviewer pointed out an error in the amplitude amplification step in the analysis of Theorem 6. While we believe this error can be resolved, we are not sure how to do it at the moment and are taking down this submissio