A Hardware-Aware Heuristic for the Qubit Mapping Problem in the NISQ Era

• URL: http://arxiv.org/abs/2010.03397v1
• Date: Tue, 6 Oct 2020 07:03:35 GMT
• Title: A Hardware-Aware Heuristic for the Qubit Mapping Problem in the NISQ Era
• Authors: Siyuan Niu (LIRMM), Adrien Suau (LIRMM, CERFACS), Gabriel Staffelbach (CERFACS), Aida Todri-Sanial (LIRMM, CNRS)
• Abstract summary: We propose a Hardware-Aware mapping transition algorithm (HA) that takes the calibration data into account. Results on IBM quantum hardware show that our HA approach can outperform the state of the art.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to several physical limitations in the realisation of quantum hardware, today's quantum computers are qualified as Noisy Intermediate-Scale Quantum (NISQ) hardware. NISQ hardware is characterized by a small number of qubits (50 to a few hundred) and noisy operations. Moreover, current realisations of superconducting quantum chips do not have the ideal all-to-all connectivity between qubits but rather at most a nearest-neighbour connectivity. All these hardware restrictions add supplementary low-level requirements. They need to be addressed before submitting the quantum circuit to an actual chip. Satisfying these requirements is a tedious task for the programmer. Instead, the task of adapting the quantum circuit to a given hardware is left to the compiler. In this paper, we propose a Hardware-Aware mapping transition algorithm (HA) that takes the calibration data into account with the aim to improve the overall fidelity of the circuit. Evaluation results on IBM quantum hardware show that our HA approach can outperform the state of the art both in terms of the number of additional gates and circuit fidelity.

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)
• Scaling quantum computing with dynamic circuits [0.6990493129893112]
  Quantum computers process information with the laws of quantum mechanics. Current quantum hardware is noisy, can only store information for a short time, and is limited to a few quantum bits, i.e., qubits. Here we overcome these limitations with error mitigated dynamic circuits and circuit-cutting to create quantum states requiring a periodic connectivity employing up to 142 qubits.
  arXiv  Detail & Related papers  (2024-02-27T19:00:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• 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)
• 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)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Full-stack quantum computing systems in the NISQ era: algorithm-driven and hardware-aware compilation techniques [1.3496450124792878]
  We will provide an overview on current full-stack quantum computing systems. We will emphasize the need for tight co-design among adjacent layers as well as vertical cross-layer design.
  arXiv  Detail & Related papers  (2022-04-13T13:26:56Z)
• Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era [0.0]
  We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis for noise quantum computers with limited number of qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.
  arXiv  Detail & Related papers  (2021-04-27T16:11:27Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• Demonstrating NISQ Era Challenges in Algorithm Design on IBM's 20 Qubit Quantum Computer [0.0]
  We present results from experiments run on IBM's 20-qubit Poughkeepsie' architecture. Results demonstrate various qubit qualities and challenges that arise in designing quantum algorithms.
  arXiv  Detail & Related papers  (2020-03-02T16:36:33Z)
• A Depth-Aware Swap Insertion Scheme for the Qubit Mapping Problem [9.024095103187657]
  NISQ architectures prevent most quantum algorithms from being directly executed on quantum devices. We propose a depth-aware SWAP insertion scheme for qubit mapping problem in the NISQ era.
  arXiv  Detail & Related papers  (2020-02-17T22:42:02Z)

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.