3 research outputs found
Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics
Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum architecture. Quantum circuit compilation (QCC) aims to generate feasible mappings such that a quantum circuit can be executed in a given hardware platform with acceptable confidence in outcomes. Physical constraints of a NISQ computer change frequently, requiring QCC process to be repeated often. When a circuit cannot directly be executed on a quantum hardware due to its physical limitations, it is necessary to modify the circuit by adding new quantum gates and auxiliary qubits, increasing its space and time complexity. An inefficient QCC may significantly increase error rate and circuit latency for even the simplest algorithms. In this article, we present artificial intelligence (AI)-based and heuristic-based methods recently reported in the literature that attempt to address these QCC challenges. We group them based on underlying techniques that they implement, such as AI algorithms including genetic algorithms, genetic programming, ant colony optimization and AI planning, and heuristics methods employing greedy algorithms, satisfiability problem solvers, dynamic, and graph optimization techniques. We discuss performance of each QCC technique and evaluate its potential limitations
A Novel Fast Path Planning Approach for Mobile Devices using Hybrid Quantum Ant Colony Optimization Algorithm
With IoT systems' increasing scale and complexity, maintenance of a large
number of nodes using stationary devices is becoming increasingly difficult.
Hence, mobile devices are being employed that can traverse through a set of
target locations and provide the necessary services. In order to reduce energy
consumption and time requirements, the devices are required to traverse
following a Hamiltonian path. This problem can be formulated as a Travelling
Salesman Problem (TSP), an NP-hard problem. Moreover, in emergency services,
the devices must traverse in real-time, demanding speedy path planning from the
TSP instance. Among the well-known optimization techniques for solving the TSP
problem, Ant Colony Optimization has a good stronghold in providing good
approximate solutions. Moreover, ACO not only provides near-optimal solutions
for TSP instances but can also output optimal or near-optimal solutions for
many other demanding hard optimization problems. However, to have a fast
solution, the next node selection, which needs to consider all the neighbors
for each selection, becomes a bottleneck in the path formation step. Moreover,
classical computers are constrained to generate only pseudorandom numbers. Both
these problems can be solved using quantum computing techniques, i.e., the next
node can be selected with proper randomization, respecting the provided set of
probabilities in just a single execution and single measurement of a quantum
circuit. Simulation results of the proposed Hybrid Quantum Ant Colony
Optimization algorithm on several TSP instances have shown promising results,
thus expecting the proposed work to be important in implementing real-time path
planning in quantum-enabled mobile devices