Number Partitioning with Grover's Algorithm in Central Spin Systems

Abstract

Numerous conceptually important quantum algorithms rely on a black-box device known as an oracle, which is typically difficult to construct without knowing the answer to the problem that the algorithm is intended to solve. A notable example is Grover's search algorithm. Here we propose a Grover search for solutions to a class of NP-complete decision problems known as subset sum problems, including the special case of number partitioning. Each problem instance is encoded in the couplings of a set of qubits to a central spin or boson, which enables a realization of the oracle without knowledge of the solution. The algorithm provides a quantum speedup across a known phase transition in the computational complexity of the partition problem, and we identify signatures of the phase transition in the simulated performance. We propose and analyze implementation schemes with cold atoms, including Rydberg-atom and cavity-QED platforms.

Large-scale Sustainable Search on Unconventional Computing Hardware

“…Mapping a real-life optimisation problem into such Hamiltonian and its concomitant minimisation by the natural or guided evolution of the systems promises to solve hard optimisation tasks. The various platforms for such optimisation include optical parametric oscillators [17,18], electronic oscillators [19,20], memristors [21], lasers [22][23][24][25], photonic simulators [26,27], cold atoms [28,29], trapped ions [30], polariton condensates [31,32], photon condensates [33], QED [34,35], and others [36][37][38]. While the demonstration of their ability to find the global minima of computationally hard problems faster than the classical von Neumann architecture remains elusive, many of these disparate physical systems can either efficiently perform matrix-vector multiplication [26,[39][40][41][42] or mimic the Hopfield neural networks [21,43,44].…”