The Quipper System

Algorithms.USV.USV

Description

This module provides an implementation of the main Unique Shortest Vector algorithm.

Synopsis

# Coherent arithmetic

Some arithmetic functions used in the reductions of the USV to the TPP and of the TPP to the DCP.

f_classical :: [[Integer]] -> Int -> Int -> Int -> (Int, [Int]) -> [Integer] Source #

Compute the function f, that selects a subset of lattice points. It is defined as:

The arguments are:

• bb_bar, an n-dimensional matrix;
• p, a prime such that np ≤ 2n;
• m, an integer such that 1mp-1;
• i0, an integer index such that 0i0n-1;
• t, an integer (either 0 or 1);
• a=(a1,...,an), an integer vector.

f_quantum :: [[Integer]] -> Int -> Int -> Int -> TwoPoint -> Circ [QDInt] Source #

Quantum version of f_classical.

Compute the function g defined as:

The arguments are:

• l, an integer (in principle, a real number, but the GFI only uses integer values);
• w, a real number in the interval [0,1);
• v, an integer.

We note that in the quantum version of this function, l and w will be parameters, and v will be a quantum input. We implement this operation using only integer division, using the following property: for all integers v, m and real numbers w,

g_classical :: Integer -> [Double] -> [Integer] -> [Integer] Source #

Compute the function g. The function g partitions the space into hypercubes of size 128l at a random offset w. It is defined as:

This is just the componentwise application of g1_classical.

Quantum version of g1_classical.

g_quantum :: Integer -> [Double] -> [QDInt] -> Circ [QDInt] Source #

Quantum version of g_classical.

h_classical :: [IntM] -> IntM Source #

Compute the function h, defined as:

The function h transforms a vector a=(a1,...,an) of 4n-bit integers into a 4n2+n-bit integer by inserting a 0 between each component of a.

h_quantum :: [QDInt] -> Circ QDInt Source #

Quantum version of h_classical.

# Algorithm 1: "uSVP"

uSVP :: [[Integer]] -> Circ [Integer] Source #

Find the shortest vector. The argument, bb, is an n-dimensional integer matrix. The algorithm first uses bb to generate a list of parameter tuples and then recursively goes through this list by calling algorithm_Q on each tuple until it either finds the shortest vector or exhausts the list and fails by returning 0.

Remark:

• Argument n is redundant, it can be inferred from bb.

usvp_aux :: Int -> [[Integer]] -> [(Int, Int, Int, Int)] -> StdGen -> Circ [Integer] Source #

For each tuple of parameters, call algorithm_Q and then test whether the returned vector is the shortest vector in the lattice. If it is, return it. If not, move on to the next tuple. If the end of the list is reached, return 0.

Remark:

• The algorithm takes as additional argument a random number generator. At each iteration, a new seed is extracted and used by the next iteration's generator.

# Algorithm 2: "Q"

algorithm_Q :: [[Integer]] -> (Int, Int, Int, Int) -> StdGen -> Circ [Integer] Source #

Compute algorithm_Q. The arguments are:

• bb_bar, an n-dimensional LLL-reduced basis;
• (l,m,i0,p), a 4-tuple of integer parameters;
• randomgen, a random number generator.

The algorithm first calls algorithm algorithm_R to prepare a list of TwoPoints parameterized on (l,m,i0,p) and then calls tPP on this list. With high probability, the returned vector is the shortest vector in the lattice up to one component.

Remark:

• Argument n is redundant, it can be inferred from bb_bar.

# Algorithm 3: "R"

algorithm_R :: [[Integer]] -> Int -> Int -> Int -> Int -> StdGen -> Circ TwoPoint Source #

Compute algorithm_R. The arguments are:

• bb_bar, an n-dimensional LLL-reduced basis,
• l, an integer approximation of the length of the shortest vector,
• p, a prime such that nn ≤ 2n,
• m, an integer such that 1mp-1,
• i0, an integer index such that 0i0n-1 and
• randomgen, a random number generator.

The algorithm first calls the functions f_quantum and g_quantum to prepare a superposition of hypercubes containing at most two lattice points, whose difference is the shortest vector. It then measures the output to collapses the state to a TwoPoint.

# Algorithm 4: "TPP"

tPP :: Int -> [TwoPoint] -> Circ [Integer] Source #

Perform Regev's reduction of the TPP to the DCP and then call dCP. The arguments are:

• n, an integer and
• states, a list of TwoPoints.

The algorithm transforms the TwoPoints in states into CosetStates using the function h_quantum, then calls dCP on this modified list to find the shortest vector.

# Algorithm 5: "DCP"

n_low_bits :: Int -> Int -> Psi_k -> [Bool] Source #

Given integers m and n and a Psi_k (q,k) compute the last n bits of the binary expansion of k on m bits.

pairing :: Int -> Int -> [Psi_k] -> ([(Psi_k, Psi_k)], [Psi_k]) Source #

Given integers m and n and a list l of Psi_ks, group the elements of l into pairs (psi_p, psi_q) where p and q share n low bits. Return the list of all such pairs together with the list of unpaired elements of l.

sieving :: Int -> Int -> [Psi_k] -> Circ [Psi_k] Source #

Perform Kuperberg's sieve. The arguments are:

• n, an integer,
• m, an integer and
• l, a list of Psi_ks.

The algorithm recursively combines and sieves the elements of l until it reaches a list whose elements have m2 trailing zeros. At each step, the list of remaining Psi_ks are paired and each pair ((q1, k1), (q2, k2)) is combined into a new Psi_k (q, k) with k= k1 ± k2. If k= k1 - k2, the Psi_k is preserved, otherwise it is discarded.

Remark:

• Uses dynamic_lift to determine whether to keep a discard a Psi_k.

dCP :: Int -> Integer -> Int -> [CosetState] -> Circ Integer Source #

Perform Kuperberg's algorithm solving the Dihedral Coset problem. The arguments are:

• n, an integer measuring the length of the output,
• d, an integer to hold the output initially set to 0,
• s, an integer counter initially set to 0 and
• states, a list of CosetStates.

The algorithm proceeds recursively. At each iteration it uses Kuperberg's sieve on the first n elements of states to compute the s-th bit of the output and updates d with the result. Then it increments s and repeats until states is exhausted.

Remark:

• The function dynamic_lift used in this algorithm is presumably very expensive in terms of resources. In this implementation it is used profusely but there is room for optimization.