Experimental demonstration of enhanced quantum tomography via quantum reservoir processing
- URL: http://arxiv.org/abs/2412.11015v1
- Date: Sun, 15 Dec 2024 02:02:43 GMT
- Title: Experimental demonstration of enhanced quantum tomography via quantum reservoir processing
- Authors: Tanjung Krisnanda, Pengtao Song, Adrian Copetudo, Clara Yun Fontaine, Tomasz Paterek, Timothy C. H. Liew, Yvonne Y. Gao,
- Abstract summary: We experimentally demonstrate a quantum reservoir processing approach for continuous-variable state reconstruction on a bosonic circuit quantum electrodynamics platform.
We show that the map learnt this way achieves high reconstruction fidelity for several test states, offering significantly enhanced performance over using map calculated based on an idealised model of the system.
- Score: 0.8672788660913944
- License:
- Abstract: Quantum machine learning is a rapidly advancing discipline that leverages the features of quantum mechanics to enhance the performance of computational tasks. Quantum reservoir processing, which allows efficient optimization of a single output layer without precise control over the quantum system, stands out as one of the most versatile and practical quantum machine learning techniques. Here we experimentally demonstrate a quantum reservoir processing approach for continuous-variable state reconstruction on a bosonic circuit quantum electrodynamics platform. The scheme learns the true dynamical process through a minimum set of measurement outcomes of a known set of initial states. We show that the map learnt this way achieves high reconstruction fidelity for several test states, offering significantly enhanced performance over using map calculated based on an idealised model of the system. This is due to a key feature of reservoir processing which accurately accounts for physical non-idealities such as decoherence, spurious dynamics, and systematic errors. Our results present a valuable tool for robust bosonic state and process reconstruction, concretely demonstrating the power of quantum reservoir processing in enhancing real-world applications.
Related papers
- Active Learning with Variational Quantum Circuits for Quantum Process Tomography [6.842224049271109]
We propose a framework for active learning (AL) to adaptively select a set of informative quantum states that improves the reconstruction most efficiently.
We design and evaluate three types of AL algorithms: committee-based, uncertainty-based, and diversity-based.
Results demonstrate that our algorithms achieve significantly improved reconstruction compared to the baseline method that selects a set of quantum states randomly.
arXiv Detail & Related papers (2024-12-30T13:12:56Z) - Quantum reservoir complexity by Krylov evolution approach [0.0]
We introduce a precise quantitative method, with strong physical foundations based on the Krylov evolution, to assess the wanted good performance in machine learning tasks.
Our results show that the Krylov approach to complexity strongly correlates with quantum reservoir performance, making it a powerful tool in the quest for optimally designed quantum reservoirs.
arXiv Detail & Related papers (2023-10-01T21:06:25Z) - 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) - Reconstructing complex states of a 20-qubit quantum simulator [0.6646556786265893]
We demonstrate an efficient method for reconstruction of significantly entangled multi-qubit quantum states.
We observe superior state reconstruction quality and faster convergence compared to the methods based on neural network quantum state representations.
Our results pave the way towards efficient experimental characterization of complex states produced by the quench dynamics of many-body quantum systems.
arXiv Detail & Related papers (2022-08-09T15:52:20Z) - Selective and efficient quantum process tomography for non-trace
preserving maps: a superconducting quantum processor implementation [0.0]
We describe a general type of quantum process that does not preserve the trace of the input quantum state.
We show that with the aid of it a priori information on the losses structure of the quantum channel, the reconstruction can be adapted to reconstruct the non-trace-preserving map.
Our results show that it is possible to efficiently reconstruct non trace-preserving processes, with high precision, and with significantly higher fidelity than when the process is assumed to be trace-preserving.
arXiv Detail & Related papers (2022-05-20T22:36:16Z) - Adaptive Quantum State Tomography with Active Learning [0.0]
We propose and implement an efficient scheme for quantum state tomography using active learning.
We apply the scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of the XXZ model in 1D and a kinetically constrained spin chain.
Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and characterizing quantum devices.
arXiv Detail & Related papers (2022-03-29T16:23:10Z) - Recompilation-enhanced simulation of electron-phonon dynamics on IBM
Quantum computers [62.997667081978825]
We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems.
We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling.
Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
arXiv Detail & Related papers (2022-02-16T19:00:00Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - Quantum-tailored machine-learning characterization of a superconducting
qubit [50.591267188664666]
We develop an approach to characterize the dynamics of a quantum device and learn device parameters.
This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data.
This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
arXiv Detail & Related papers (2021-06-24T15:58:57Z) - Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems.
We propose an algorithm to implement imaginary time propagation on a quantum computer.
arXiv Detail & Related papers (2021-02-24T12:48:00Z) - Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations.
We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
arXiv Detail & Related papers (2020-12-08T01:10:30Z)
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.