Programming Quantum Hardware via Levenberg Marquardt Machine Learning
- URL: http://arxiv.org/abs/2204.07011v2
- Date: Thu, 1 Dec 2022 22:18:47 GMT
- Title: Programming Quantum Hardware via Levenberg Marquardt Machine Learning
- Authors: James E. Steck, Nathan L. Thompson, Elizabeth C. Behrman
- Abstract summary: Machine learning can be used as a systematic method to nonalgorithmically program quantum computers.
We have shown that our machine learning approach is robust to both noise and to decoherence.
Results from this have been successfully ported to the IBM hardware and trained using a powerful hybrid reinforcement learning technique.
- Score: 2.127049691404299
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Significant challenges remain with the development of macroscopic quantum
computing, hardware problems of noise, decoherence, and scaling, software
problems of error correction, and, most important, algorithm construction.
Finding truly quantum algorithms is quite difficult, and many quantum
algorithms, like Shor prime factoring or phase estimation, require extremely
long circuit depth for any practical application, necessitating error
correction. Machine learning can be used as a systematic method to
nonalgorithmically program quantum computers. Quantum machine learning enables
us to perform computations without breaking down an algorithm into its gate
building blocks, eliminating that difficult step and potentially reducing
unnecessary complexity. In addition, we have shown that our machine learning
approach is robust to both noise and to decoherence, which is ideal for running
on inherently noisy NISQ devices which are limited in the number of qubits
available for error correction. We demonstrated this using a fundamentally non
classical calculation, experimentally estimating the entanglement of an unknown
quantum state. Results from this have been successfully ported to the IBM
hardware and trained using a powerful hybrid reinforcement learning technique
which is a modified Levenberg Marquardt LM method. The LM method is ideally
suited to quantum machine learning as it only requires knowledge of the final
measured output of the quantum computation, not intermediate quantum states
which are generally not accessible. Since it processes all the learning data
simultaneously, it also requires significantly fewer hits on the quantum
hardware. Machine learning is demonstrated with results from simulations and
runs on the IBM Qiskit hardware interface.
Related papers
- Quantum Information Processing with Molecular Nanomagnets: an introduction [49.89725935672549]
We provide an introduction to Quantum Information Processing, focusing on a promising setup for its implementation.
We introduce the basic tools to understand and design quantum algorithms, always referring to their actual realization on a molecular spin architecture.
We present some examples of quantum algorithms proposed and implemented on a molecular spin qudit hardware.
arXiv Detail & Related papers (2024-05-31T16:43:20Z) - A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers [8.50998018964906]
Current quantum computers have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers.
This paper proposes a practical machine learning approach, called Q-LEAR, to mitigate noise errors in quantum software outputs.
Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators.
arXiv Detail & Related papers (2024-04-19T13:51:40Z) - 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) - Quantum-Assisted Simulation: A Framework for Designing Machine Learning
Models in the Quantum Computing Domain [0.0]
We explore the history of quantum computing, examine existing QML algorithms, and aim to present a simplified procedure for setting up simulations of QML algorithms.
We conducted simulations on a dataset using both machine learning and quantum machine learning approaches.
arXiv Detail & Related papers (2023-11-17T07:33:42Z) - 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) - Machine Learning for Practical Quantum Error Mitigation [0.0]
We show that machine learning for quantum error mitigation can drastically reduce overheads, maintain or even surpass the accuracy of conventional methods.
Our results highlight the potential of classical machine learning for practical quantum computation.
arXiv Detail & Related papers (2023-09-29T16:17:12Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - 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) - A non-algorithmic approach to "programming" quantum computers via
machine learning [0.0]
We show that machine learning can be used as a systematic method to construct algorithms, that is, to non-algorithmically "program" quantum computers.
We demonstrate this using a fundamentally non-classical calculation: experimentally estimating the entanglement of an unknown quantum state.
Results from this have been successfully ported to the IBM hardware and trained using a hybrid reinforcement learning method.
arXiv Detail & Related papers (2020-07-16T13:36:21Z) - QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to
Enhance ML Classifiers and Feature Spaces [0.49841205356595936]
Machine learning and quantum computing are causing a paradigm shift in the performance and behavior of certain algorithms.
This paper first understands the mathematical intuition for the implementation of quantum feature space.
We build a noisy variational quantum circuit KNN which mimics the classification methods of a traditional KNN.
arXiv Detail & Related papers (2020-02-22T04:14:32Z) - Statistical Limits of Supervised Quantum Learning [90.0289160657379]
We show that if the bound on the accuracy is taken into account, quantum machine learning algorithms for supervised learning cannot achieve polylogarithmic runtimes in the input dimension.
We conclude that, when no further assumptions on the problem are made, quantum machine learning algorithms for supervised learning can have at most speedups over efficient classical algorithms.
arXiv Detail & Related papers (2020-01-28T17:35:32Z)
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.