QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
- URL: http://arxiv.org/abs/2408.13352v1
- Date: Fri, 23 Aug 2024 19:57:40 GMT
- Title: QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
- Authors: Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Bao Bach, Ilya Safro,
- Abstract summary: emphQAdaPrune is an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters.
We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits.
- Score: 2.3332157823623403
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuits. This ensures that a range of proposed applications can be deployed on real quantum hardware. The efficiency of quantum circuit is desired both in the number of trainable gates and the depth of the overall circuit. The major concern of barren plateaus has made this need for efficiency even more acute. The problem of efficient quantum circuit realization has been extensively studied in the literature to reduce gate complexity and circuit depth. Another important approach is to design a method to reduce the \emph{parameter complexity} in a variational quantum circuit. Existing methods include hyperparameter-based parameter pruning which introduces an additional challenge of finding the best hyperparameters for different applications. In this paper, we present \emph{QAdaPrune} - an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters. We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits and in some cases may enhance trainability of the circuits even if the original quantum circuit gets stuck in a barren plateau.\\ \noindent{\bf Reproducibility}: The source code and data are available at \url{https://github.com/aicaffeinelife/QAdaPrune.git}
Related papers
- 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) - FragQC: An Efficient Quantum Error Reduction Technique using Quantum
Circuit Fragmentation [4.2754140179767415]
We present it FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold.
We achieve an increase of fidelity by 14.83% compared to direct execution without cutting the circuit, and 8.45% over the state-of-the-art ILP-based method.
arXiv Detail & Related papers (2023-09-30T17:38:31Z) - Efficient estimation of trainability for variational quantum circuits [43.028111013960206]
We find an efficient method to compute the cost function and its variance for a wide class of variational quantum circuits.
This method can be used to certify trainability for variational quantum circuits and explore design strategies that can overcome the barren plateau problem.
arXiv Detail & Related papers (2023-02-09T14:05:18Z) - 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) - Gaussian initializations help deep variational quantum circuits escape
from the barren plateau [87.04438831673063]
Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years.
However, quantum circuits with random structures have poor trainability due to the exponentially vanishing gradient with respect to the circuit depth and the qubit number.
This result leads to a general belief that deep quantum circuits will not be feasible for practical tasks.
arXiv Detail & Related papers (2022-03-17T15:06:40Z) - Circuit connectivity boosts by quantum-classical-quantum interfaces [0.4194295877935867]
High-connectivity circuits are a major roadblock for current quantum hardware.
We propose a hybrid classical-quantum algorithm to simulate such circuits without swap-gate ladders.
We numerically show the efficacy of our method for a Bell-state circuit for two increasingly distant qubits.
arXiv Detail & Related papers (2022-03-09T19:00:02Z) - Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
We propose that quantum circuits can be modeled as queuing networks.
Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
arXiv Detail & Related papers (2021-06-26T10:55:52Z) - Automatically Differentiable Quantum Circuit for Many-qubit State
Preparation [1.5662820454886202]
We propose the automatically differentiable quantum circuit (ADQC) approach to efficiently prepare arbitrary quantum many-qubit states.
The circuit is optimized by updating the latent gates using back propagation to minimize the distance between the evolved and target states.
Our work sheds light on the "intelligent construction" of quantum circuits for many-qubit systems by combining with the machine learning methods.
arXiv Detail & Related papers (2021-04-30T12:22:26Z) - Capacity and quantum geometry of parametrized quantum circuits [0.0]
Parametrized quantum circuits can be effectively implemented on current devices.
We evaluate the capacity and trainability of these circuits using the geometric structure of the parameter space.
Our results enhance the understanding of parametrized quantum circuits for improving variational quantum algorithms.
arXiv Detail & Related papers (2021-02-02T18:16:57Z) - 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) - 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)
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.