Digitized Counterdiabatic Quantum Algorithms for Logistics Scheduling

• URL: http://arxiv.org/abs/2405.15707v1
• Date: Fri, 24 May 2024 16:53:30 GMT
• Title: Digitized Counterdiabatic Quantum Algorithms for Logistics Scheduling
• Authors: Archismita Dalal, Iraitz Montalban, Narendra N. Hegade, Alejandro Gomez Cadavid, Enrique Solano, Abhishek Awasthi, Davide Vodola, Caitlin Jones, Horst Weiss, Gernot Füchsel,
• Abstract summary: We study a job shop scheduling problem for an automatized robot and a travelling salesperson problem. In DCQO, we find the solution of an optimization problem via an adiabatic quantum dynamics. We experimentally implement our algorithms on superconducting and trapped-ion quantum processors.
• Score: 33.04597339860113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study a job shop scheduling problem for an automatized robot in a high-throughput laboratory and a travelling salesperson problem with recently proposed digitized counterdiabatic quantum optimization (DCQO) algorithms. In DCQO, we find the solution of an optimization problem via an adiabatic quantum dynamics, which is accelerated with counterdiabatic protocols. Thereafter, we digitize the global unitary to encode it in a digital quantum computer. For the job-shop scheduling problem, we aim at finding the optimal schedule for a robot executing a number of tasks under specific constraints, such that the total execution time of the process is minimized. For the traveling salesperson problem, the goal is to find the path that covers all cities and is associated with the shortest traveling distance. We consider both hybrid and pure versions of DCQO algorithms and benchmark the performance against digitized quantum annealing and the quantum approximate optimization algorithm (QAOA). In comparison to QAOA, the DCQO solution is improved by several orders of magnitude in success probability using the same number of two-qubit gates. Moreover, we experimentally implement our algorithms on superconducting and trapped-ion quantum processors. Our results demonstrate that circuit compression using counterdiabatic protocols is amenable to current NISQ hardware and can solve logistics scheduling problems, where other digital quantum algorithms show insufficient performance.

Related papers

• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• A Fast and Adaptable Algorithm for Optimal Multi-Qubit Pathfinding in Quantum Circuit Compilation [0.0]
  This work focuses on multi-qubit pathfinding as a critical subroutine within the quantum circuit compilation mapping problem. We introduce an algorithm, modelled using binary integer linear programming, that navigates qubits on quantum hardware optimally with respect to circuit SWAP-gate depth. We have benchmarked the algorithm across a variety of quantum hardware layouts, assessing properties such as computational runtimes, solution SWAP depths, and accumulated SWAP-gate error rates.
  arXiv  Detail & Related papers  (2024-05-29T05:59:15Z)
• Depth scaling of unstructured search via quantum approximate optimization [0.0]
  Variational quantum algorithms have become the de facto model for current quantum computations. One such problem is unstructured search which consists on finding a particular bit of string. We trotterize a CTQW to recover a QAOA sequence, and employ recent advances on the theory of Trotter formulas to bound the query complexity.
  arXiv  Detail & Related papers  (2024-03-22T18:00:03Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction [77.97248520278123]
  We propose Shuffle-QUDIO to involve shuffle operations into local Hamiltonians during the quantum distributed optimization. Compared with QUDIO, Shuffle-QUDIO significantly reduces the communication frequency among quantum processors and simultaneously achieves better trainability.
  arXiv  Detail & Related papers  (2022-09-26T06:51:20Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Approximate quantum gates compiling with self-navigation algorithm [0.0]
  We propose an algorithm to approximately compile single-qubit gates with arbitrary accuracy. Evaluation results show that the overall rotation distance generated by our algorithm is significantly shorter than the commonly used $U3$ gate.
  arXiv  Detail & Related papers  (2022-04-06T03:07:17Z)
• Quantum Algorithms for solving Hard Constrained Optimisation Problems [0.0]
  The thesis deals with Quantum Algorithms for solving Hard Constrained Optimization Problems. It shows how quantum computers can solve everyday problems such as finding the best schedule for social workers. We have proposed EVA: a quantum Exponential Value Approximation algorithm that speeds up the VQE.
  arXiv  Detail & Related papers  (2022-02-26T12:23:17Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.