Network coordination - synchronising traffic signals, scheduling trains, assigning communication slots requires minimising pairwise costs across coupled systems. These problems are NP-hard yet share a common Fourier-sparse structure exploitable by quantum algorithms. We introduce the Fourier Network Coordination problem (Fourier-NC),unifying eight application domains. For abelian and dihedral groups, classical sparse Fourier transforms match quantum in the same oracle model, limiting the advantage to at most polynomial. The genuine separation emerges for the symmetric group Sk: a conditional super-exponential speedup of k! -> poly(k) for class-function costs with non-trivial minimisers. When the minimising conjugacy class is structurally determined, the problem lies in NP (int) BQP and is conditionally outside P (Corollary 6.5), placing it in the intermediate complexity regime alongside integer factorisation and graph isomorphism. We formalise the abelian index α(G) = [G : Amax] as the structural invariant governing the quantum-classical gap and identify a three-regime complexity trichotomy: abelian ({α= 1, classical sFFT suffices), nearly abelian (α= dmax, polynomial advantage), and strongly non-abelian (α>>dmax, super-exponential advantage).
The quantum Fourier transform (QFT) has emerged as the primary tool in quantum algorithms which achieve exponential advantage over classical computation and lies at the heart of the solution to the abelian hidden subgroup problem, of which Shor's celebrated factoring and discrete log algorithms are a special case. We begin by addressing various computational issues surrounding the QFT and give improved parallel circuits for both the QFT over a power of 2 and the QFT over an arbitrary cyclic group. These circuits are based on new insight into the relationship between the discrete Fourier transform over different cyclic groups. We then exploit this insight to extend the class of hidden subgroup problems with efficient quantum solutions. First we relax the condition that the underlying hidden subgroup function be distinct on distinct cosets of the subgroup in question and show that this relaxation can be solved whenever G is a finitely-generated abelian group. We then extend this reasoning to the hidden cyclic subgroup problem over the reals, showing how to efficiently generate the bits of the period of any sufficiently piecewise-continuous function on R. Finally, we show that this problem of period-finding over R, viewed as an oracle promise problem, is strictly harder than its integral counterpart. In particular, period-finding over R lies outside the complexity class MA, a class which contains period-finding over the integers.
There has been increasing interest in developing efficient quantum algorithms for hard classical problems. The Network Signal Coordination (NSC) problem is one such problem known to be NP complete. We implement Grover's search algorithm to solve the NSC problem to provide quadratic speedup. We further extend the algorithm to a Robust NSC formulation and analyse its complexity under both constant and polynomial-precision robustness parameters. The Robust NSC problem determines whether there exists a fraction (alpha) of solutions space that will lead to system delays less than a maximum threshold (K). The key contributions of this work are (1) development of a quantum algorithm for the NSC problem, and (2) a quantum algorithm for the Robust NSC problem whose iteration count is O(1/sqrt(alpha)), independent of the search space size, and (3) an extension to polynomial-precision robustness where alpha = alpha_o/p(N) decays polynomially with network size, retaining a quadratic quantum speedup. We demonstrate its implementation through simulation and on an actual quantum computer.
We study the forrelation problem: given a pair of
n-bit Boolean functions
f and
g, estimate the correlation between
f and the Fourier transform of
g. This problem is known to provide the largest possible quantum speedup in terms of its query complexity and achieves the landmark oracle separation between the complexity class BQP and the Polynomial Hierarchy. Our first result is a classical algorithm for the forrelation problem which has runtime
O(n2n/2). This is a nearly quadratic improvement over the best previously known algorithm. Secondly, we show that quantum query algorithm that makes
t queries to an
n-bit oracle can be simulated by classical query algorithm making only
O(2n(1−1/2t)) queries. This fixes a gap in the literature arising from a recently discovered critical error in a previous proof; it matches recently established lower bounds (up to
poly(n,t)) factors) and thus characterizes the maximal separation in query complexity between quantum and classical algorithms. Finally, we introduce a graph-based forrelation problem where
n binary variables live at vertices of some fixed graph and the functions
f,g are products of terms describing interactions between nearest-neighbor variables. We show that the graph-based forrelation problem can be solved on a classical computer in time
O(n) for any bipartite graph, any planar graph, or, more generally, any graph which can be partitioned into two subgraphs of constant treewidth. The graph-based forrelation is simply related to the variational energy achieved by the Quantum Approximate Optimization Algorithm (QAOA) with two entangling layers and Ising-type cost functions. By exploiting the connection between QAOA and the graph-based forrelation we were able to simulate the recently proposed Recursive QAOA with two entangling layers and
225 qubits on a laptop computer.
We discuss the fundamental role of entanglement as the essential nonclassical feature providing the computational speedup in the known quantum algorithms. We review the construction of the Fourier transform on an Abelian group and the principles underlying the fast Fourier transform algorithm. We describe the implementation of the FFT algorithm for the group of integers modulo 2n in the quantum context, showing how the group–theoretic formalism leads to the standard quantum network and identifying the property of entanglement that gives rise to the exponential speedup (compared to the classical FFT). Finally we outline the use of the Fourier transform in extracting periodicities, which underlies its utility in the known quantum algorithms.
Establishing quantum speedup for computationally hard problems of practical relevance, particularly combinatorial optimization problems, remains a central challenge in quantum computation. In this work, we identify a structurally defined family of classically hard maximum independent set (MIS) instances, and design and analyze a non-stoquastic adiabatic quantum optimization algorithm that exploits this structure. The algorithm runs in polynomial time and achieves an exponential speedup over both transverse-field quantum annealing and state-of-the-art classical solvers on these instances, under assumptions supported by analytical and numerical evidence. We identify the essential quantum mechanism enabling the speedup as the use of a non-stoquastic XX-driver to access a larger sign-structured admissible subspace beyond the stoquastic regime, which allows sign-generating quantum interference to create smooth evolution paths that bypass tunneling. This identifies a distinctive quantum mechanism underlying the speedup and explains why no efficient classical analogue is likely to exist. In addition, our analysis produces scalable small-scale models, derived from our structural reduction, that capture the essential dynamics of the algorithm. These models provide a concrete opportunity for verification of the quantum advantage mechanism on currently available universal quantum computers.
The search for practical problems that can be solved exponentially faster on a quantum computer is one of the primary goals in research into algorithms for quantum computers. It has long been known that such speedups are only possible for highly structured problems.
We consider two problems with algebraic structure: (1) the hidden subgroup problem, where we have an unknown subgroup, a function that is constant on cosets of the subgroup, and the task is to find the unknown subgroup; (2) the hidden shift problem, where we have an unknown element of a group, a function that has been shifted by that group element, and the task is to find the unknown group element.
We show that the Fourier transform is intimately connected to both these problems. This is because the Fourier transform respects group symmetries.
For the hidden subgroup problem, we introduce the idea of using information theoretic techniques to design quantum algorithms. This consists of first finding an optimal set of measurements for distinguishing between different subgroups, followed by attempts to implement this set of measurements.
We show that the standard algorithm for the abelian hidden subgroup problem is optimal. For the nonabelian hidden subgroup problem, the optimal measurements respect the Fourier transform in that they can be implemented by first performing a Fourier transform on each coset state, measuring the name of the irreducible representation for each state, followed by a joint measurement over the resulting lower dimensional space. This shows that the “strong standard method” cannot be used to implement the optimal set of measurements.
For some instances of the hidden shift problem, we give a quantum algorithms that are exponentially faster than the best known classical algorithm. The algorithms make use of the Fourier transform in a way that may be interpreted as solving a deconvolution problem. As evidence of hardness, we give a reduction from algebraically homomorphic cryptosystems to the shifted Legendre symbol problem. This is an example of a problem which admits an exponential quantum speedup yet is not an instance of the abelian hidden subgroup problem.