Quantum Pattern Detection: Accurate State- and Circuit-based Analyses
- URL: http://arxiv.org/abs/2501.15895v1
- Date: Mon, 27 Jan 2025 09:42:41 GMT
- Title: Quantum Pattern Detection: Accurate State- and Circuit-based Analyses
- Authors: Julian Shen, Joshua Ammermann, Christoph König, Ina Schaefer,
- Abstract summary: We propose a framework for the automatic detection of quantum patterns using state- and circuit-based code analysis.
In an empirical evaluation, we show that our framework is able to detect quantum patterns very accurately and that it outperforms existing quantum pattern detection approaches.
- Score: 2.564905016909138
- License:
- Abstract: Quantum computers have the potential to solve certain problems faster than classical computers by exploiting quantum mechanical effects such as superposition. However, building high-quality quantum software is challenging due to the fundamental differences between quantum and traditional programming and the lack of abstraction mechanisms. To mitigate this challenge, researchers have introduced quantum patterns to capture common high-level design solutions to recurring problems in quantum software engineering. In order to utilize patterns as an abstraction level for implementation, a mapping between the theoretical patterns and the source code is required, which has only been addressed to a limited extent. To close this gap, we propose a framework for the automatic detection of quantum patterns using state- and circuit-based code analysis. Furthermore, we contribute a dataset for benchmarking quantum pattern detection approaches. In an empirical evaluation, we show that our framework is able to detect quantum patterns very accurately and that it outperforms existing quantum pattern detection approaches in terms of detection accuracy.
Related papers
- QCircuitNet: A Large-Scale Hierarchical Dataset for Quantum Algorithm Design [17.747641494506087]
We introduce QCircuitNet, the first benchmark and test dataset designed to evaluate AI's capability in designing and implementing quantum algorithms.
Unlike using AI for writing traditional codes, this task is fundamentally different and significantly more complicated due to highly flexible design space and intricate manipulation of qubits.
arXiv Detail & Related papers (2024-10-10T14:24:30Z) - 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) - Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits [0.0]
We propose an anomaly detection algorithm based on a parametrized quantum circuit.
This algorithm has been trained on a classical computer and tested with simulations as well as on real quantum hardware.
arXiv Detail & Related papers (2023-12-07T11:50:42Z) - Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
The anticipated applications of quantum computers span across science and industry.
We present a survey of several potential application areas of quantum algorithms.
We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Model-free Quantum Gate Design and Calibration using Deep Reinforcement
Learning [7.683965448804695]
We propose a novel training framework using deep reinforcement learning for model-free quantum control.
The proposed framework relies only on the measurement at the end of the control process and offers the ability to find the optimal control policy.
arXiv Detail & Related papers (2023-02-05T12:36:03Z) - Quantum Neural Architecture Search with Quantum Circuits Metric and
Bayesian Optimization [2.20200533591633]
We propose a new quantum gates distance that characterizes the gates' action over every quantum state.
Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
arXiv Detail & Related papers (2022-06-28T16:23:24Z) - 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) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Robustness Verification of Quantum Classifiers [1.3534683694551501]
We define a formal framework for the verification and analysis of quantum machine learning algorithms against noises.
A robust bound is derived and an algorithm is developed to check whether or not a quantum machine learning algorithm is robust with respect to quantum training data.
Our approach is implemented on Google's Quantum classifier and can verify the robustness of quantum machine learning algorithms with respect to a small disturbance of noises.
arXiv Detail & Related papers (2020-08-17T11:56:23Z)
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.