Yoshinori Aono

2papers

2 Papers

CRNov 11, 2021
The Present and Future of Discrete Logarithm Problems on Noisy Quantum Computers

Yoshinori Aono, Sitong Liu, Tomoki Tanaka et al.

The discrete logarithm problem (DLP) is the basis for several cryptographic primitives. Since Shor's work, it has been known that the DLP can be solved by combining a polynomial-size quantum circuit and a polynomial-time classical post-processing algorithm. Evaluating and predicting the instance size that quantum devices can solve is an emerging research topic. In this paper, we propose a quantitative measure based on the success probability of the post-processing algorithm to determine whether an experiment on a quantum device (or a classical simulator) succeeded. We also propose a procedure to modify bit strings observed from a Shor circuit to increase the success probability of a lattice-based post-processing algorithm. We report preliminary experiments conducted on IBM-Quantum quantum computers and near-future predictions based on noisy-device simulations. We conducted our experiments with the ibm_kawasaki device and discovered that the simplest circuit (7 qubits) from a 2-bit DLP instance achieves a sufficiently high success probability to proclaim the experiment successful. Experiments on another circuit from a slightly harder 2-bit DLP instance, on the other hand, did not succeed, and we determined that reducing the noise level by half is required to achieve a successful experiment. Finally, we give a near-term prediction based on required noise levels to solve some selected small DLP and integer factoring instances.

CRJun 2, 2014
A faster method for computing Gama-Nguyen-Regev's extreme pruning coefficients

Yoshinori Aono

This paper considers Gama-Nguyen-Regev's strategy [GNR10] for optimizing pruning coefficients for lattice vector enumeration. We give a table of optimized coefficients and proposes a faster method for computing near-optimized coefficients for any parameters by interpolation. From the first version published in 2014, we inserted new Section 3.3 to introduce our recent technique to compute approximations of enumeration cost and success probability; both are completed in O(n^2) floating point operations where n is the lattice dimension. For readers who are interested in this topic, we keep the descriptions of our heuristic optimization method in Section 4 although they are outdated now.