Quantum speedups for lattice sieves are tenuous at best.

Martin R. Albrecht, Vlad Gheorghiu, Eamonn W. Postlethwaite +1 more

André Chailloux, Johanna Loyer

Lynn Engelberts, Yanlin Chen, Amin Shiraz Gilani +3 more

Beomgeun Cho, Minki Hhan, Taehyun Kim +2 more

In this paper, we study the requirement for quantum random access memory (QRAM) in quantum lattice sieving, a fundamental algorithm for lattice-based cryptanalysis. First, we obtain a lower bound on the cost of quantum lattice sieving with a bounded size QRAM. We do so in a new query model encompassing a wide range of lattice sieving algorithms similar to those in the classical sieving lower bound by Kirshanova and Laarhoven [CRYPTO 21]. This implies that, under reasonable assumptions, quantum speedups in lattice sieving require the use of QRAM. In particular, no quantum speedup is possible without QRAM. Second, we investigate the trade-off between the size of QRAM and the quantum speedup. We obtain a new interpolation between classical and quantum lattice sieving. Moreover, we show that further improvements require a novel way to use the QRAM by proving the optimality of some subroutines. An important caveat is that this trade-off requires a strong assumption on the efficient replacement of QRAM data, indicating that even speedups with a small QRAM are already challenging. Finally, we provide a circuit for quantum lattice sieving without using QRAM. Our circuit has a better depth complexity than the best classical algorithms but requires an exponential amount of qubits. To the best of our knowledge, this is the first quantum speedup for lattice sieving without QRAM in the standard quantum circuit model. We explain why this circuit does not contradict our lower bound, which considers the query complexity.

Christoph Ludwig

Nishant Rodrigues, Brad Lackey

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.