CODAR: A Contextual Duration-Aware Qubit Mapping for Various NISQ
Devices
- URL: http://arxiv.org/abs/2002.10915v1
- Date: Mon, 24 Feb 2020 04:30:05 GMT
- Title: CODAR: A Contextual Duration-Aware Qubit Mapping for Various NISQ
Devices
- Authors: Haowei Deng, Yu Zhang and Quanxi Li
- Abstract summary: We propose a Multi-architecture Adaptive Quantum Abstract Machine (maQAM) and a COntext-sensitive and Duration-Aware Remapping algorithm (CODAR)
The CODAR remapper is aware of gate duration difference and program context, enabling it to extract more parallelism from programs.
- Score: 4.866886176084101
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Quantum computing devices in the NISQ era share common features and
challenges like limited connectivity between qubits. Since two-qubit gates are
allowed on limited qubit pairs, quantum compilers must transform original
quantum programs to fit the hardware constraints. Previous works on qubit
mapping assume different gates have the same execution duration, which limits
them to explore the parallelism from the program. To address this drawback, we
propose a Multi-architecture Adaptive Quantum Abstract Machine (maQAM) and a
COntext-sensitive and Duration-Aware Remapping algorithm (CODAR). The CODAR
remapper is aware of gate duration difference and program context, enabling it
to extract more parallelism from programs and speed up the quantum programs by
1.23 in simulation on average in different architectures and maintain the
fidelity of circuits when running on Origin Quantum noisy simulator.
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) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - 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) - TeD-Q: a tensor network enhanced distributed hybrid quantum machine
learning framework [59.07246314484875]
TeD-Q is an open-source software framework for quantum machine learning.
It seamlessly integrates classical machine learning libraries with quantum simulators.
It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
arXiv Detail & Related papers (2023-01-13T09:35:05Z) - 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) - Quantum compiling with a variational instruction set for accurate and
fast quantum computing [1.0131895986034314]
We propose a quantum variational instruction set (QuVIS) for higher speed and accuracy of quantum computing.
The controlling of qubits for realizing the gates in a QuVIS is variationally achieved using the fine-grained time optimization algorithm.
With the same requirement on quantum hardware, the time cost for QuVIS is reduced to less than one half of that for QuMIS.
arXiv Detail & Related papers (2022-03-29T13:53:19Z) - Enabling Multi-programming Mechanism for Quantum Computing in the NISQ
Era [0.0]
NISQ devices have several physical limitations and unavoidable noisy quantum operations.
Only small circuits can be executed on a quantum machine to get reliable results.
We propose a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously.
arXiv Detail & Related papers (2021-02-10T08:46:16Z) - SQUARE: Strategic Quantum Ancilla Reuse for Modular Quantum Programs via
Cost-Effective Uncomputation [7.92565122267857]
We present a compilation infrastructure that tackles allocation and reclamation of scratch qubits (called ancilla) in quantum programs.
At its core, SQUARE strategically performs uncomputation to create opportunities for qubit reuse.
Our results show that SQUARE improves the average success rate of NISQ applications by 1.47X.
arXiv Detail & Related papers (2020-04-18T06:34:37Z) - 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) - Context-Sensitive and Duration-Aware Qubit Mapping for Various NISQ
Devices [4.866886176084101]
We propose a COntext-sensitive and Duration-Aware Remapping algorithm (Codar) based on the Quantum Abstract Machine (QAM)
By introducing lock for each qubit, Codar is aware of gate duration difference and program context.
Compared to the best-known algorithm, Codar halves the total execution time of several quantum algorithms and cut down 17.5% - 19.4% total execution time on average in different architectures.
arXiv Detail & Related papers (2020-01-19T19:35:43Z)
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.