A Variation-Aware Quantum Circuit Mapping Approach Based on Multi-agent
Cooperation
- URL: http://arxiv.org/abs/2111.09033v3
- Date: Wed, 1 Dec 2021 00:52:40 GMT
- Title: A Variation-Aware Quantum Circuit Mapping Approach Based on Multi-agent
Cooperation
- Authors: Pengcheng Zhu, Weiping Ding, Lihua Wei, Zhijin Guan, and Shiguang Feng
- Abstract summary: We propose a quantum circuit mapping method based on multi-agent cooperation.
It consists of two core components: the qubit placement algorithm and the qubit routing method.
Compared with the stateof-the-art method, our method can improve the success rate by 25.86% on average and 95.42% at maximum.
- Score: 8.239525962555586
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The quantum circuit mapping approach is an indispensable part of the software
stack for the noisy intermediatescale quantum (NISQ) device. It has a
significant impact on the reliability of computational tasks on NISQ devices.
To improve the overall fidelity of physical circuits, we propose a quantum
circuit mapping method based on multi-agent cooperation. This approach
considers the Spatio-temporal variation of quantum operation quality on the
NISQ device when inserting ancillary operation. It consists of two core
components: the qubit placement algorithm and the qubit routing method. The
qubit placement algorithm exploits the iterated local search framework to find
a desirable initial mapping for the reduced symmetric form of the original
circuit. The qubit routing method generates the physical circuit through
multi-agent communication and collaboration. Each agent inserts the ancillary
gates independently according to its environment state. The quality of the
physical circuit evolves according to an information-exchanging mechanism
between agents, which combines the local search and global search. To
experiment on the benchmark circuits (with hundreds of quantum gates) beyond
the capacity of current NISQ devices, we build a noisy simulator with gate
error 10x lower than that of the latest NISQ device of IBM. The experimental
results confirm the performance of our approach in improving circuit fidelity.
Compared with the stateof-the-art method, our method can improve the success
rate by 25.86% on average and 95.42% at maximum.
Related papers
- Improving and benchmarking NISQ qubit routers [0.0]
We benchmark various routing techniques considering random quantum circuits on one-dimensional and square lattice connectivities.
We introduce circuit fidelity as a comprehensive metric that captures the effects of SWAP and circuit depth overheads.
arXiv Detail & Related papers (2025-02-06T09:31:51Z) - MLQM: Machine Learning Approach for Accelerating Optimal Qubit Mapping [13.958125071955742]
We propose a machine learning approach for accelerating optimal qubit mapping (MLQM)
First, the method proposes a global search space pruning scheme based on prior knowledge and machine learning.
Second, to address the limited availability of effective samples in the learning task, MLQM introduces a novel data augmentation and refinement scheme.
arXiv Detail & Related papers (2024-12-04T11:49:09Z) - 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) - Reinforcement learning-assisted quantum architecture search for variational quantum algorithms [0.0]
This thesis focuses on identifying functional quantum circuits in noisy quantum hardware.
We introduce a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently.
In dealing with various VQAs, our RL-based QAS outperforms existing QAS.
arXiv Detail & Related papers (2024-02-21T12:30:39Z) - 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) - Toward Consistent High-fidelity Quantum Learning on Unstable Devices via
Efficient In-situ Calibration [5.0854551390284]
In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will significantly reduce the fidelity of quantum computing.
In this paper, we propose a novel quantum pulse-based noise adaptation framework, namely QuPAD.
Experiments show that the runtime on quantum devices of QuPAD with 8-10 qubits is less than 15 minutes, which is up to 270x faster than the parameter-shift approach.
arXiv Detail & Related papers (2023-09-12T15:39:06Z) - Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most.
We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
arXiv Detail & Related papers (2022-04-12T19:39:31Z) - 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) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z) - Improving the Performance of Deep Quantum Optimization Algorithms with
Continuous Gate Sets [47.00474212574662]
Variational quantum algorithms are believed to be promising for solving computationally hard problems.
In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances.
Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
arXiv Detail & Related papers (2020-05-11T17:20:51Z)
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.