Demonstrating efficient and robust bosonic state reconstruction via optimized excitation counting
- URL: http://arxiv.org/abs/2403.03080v3
- Date: Mon, 25 Mar 2024 09:13:28 GMT
- Title: Demonstrating efficient and robust bosonic state reconstruction via optimized excitation counting
- Authors: Tanjung Krisnanda, Clara Yun Fontaine, Adrian Copetudo, Pengtao Song, Kai Xiang Lee, Ni-Ni Huang, Fernando Valadares, Timothy C. H. Liew, Yvonne Y. Gao,
- Abstract summary: We introduce an efficient and robust technique for optimized reconstruction based on excitation number sampling (ORENS)
Our work provides a crucial and valuable primitive for practical quantum information processing using bosonic modes.
- Score: 33.12402484053305
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum state reconstruction is an essential element in quantum information processing. However, efficient and reliable reconstruction of non-trivial quantum states in the presence of hardware imperfections can be challenging. This task is particularly demanding for high-dimensional states encoded in continuous-variable (CV) systems, as many error-prone measurements are needed to cover the relevant degrees of freedom of the system in phase space. In this work, we introduce an efficient and robust technique for optimized reconstruction based on excitation number sampling (ORENS). We use a standard bosonic circuit quantum electrodynamics (cQED) setup to experimentally demonstrate the robustness of ORENS and show that it outperforms the existing cQED reconstruction techniques such as Wigner and Husimi Q tomography. Our investigation highlights that ORENS is naturally free of parasitic system dynamics and resilient to decoherence effects in the hardware. Finally, ORENS relies only on the ability to accurately measure the excitation number of the state, making it a versatile and accessible tool for a wide range of CV platforms and readily scalable to multimode systems. Thus, our work provides a crucial and valuable primitive for practical quantum information processing using bosonic modes.
Related papers
- Experimental demonstration of enhanced quantum tomography via quantum reservoir processing [0.8672788660913944]
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.
arXiv Detail & Related papers (2024-12-15T02:02:43Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Compact fermionic quantum state preparation with a natural-orbitalizing variational quantum eigensolving scheme [0.0]
Near-term quantum state preparation is typically realized by means of the variational quantum eigensolver (VQE) algorithm.
We present a refined VQE scheme that consists in topping VQE with state-informed updates of the elementary fermionic modes.
For a fixed circuit structure, the method is shown to enhance the capabilities of the circuit to reach a state close to the target state without incurring too much overhead from shot noise.
arXiv Detail & Related papers (2024-06-20T10:23:28Z) - On-demand transposition across light-matter interaction regimes in
bosonic cQED [69.65384453064829]
Bosonic cQED employs the light field of high-Q superconducting cavities coupled to non-linear circuit elements.
We present the first experiment to achieve fast switching of the interaction regime without deteriorating the cavity coherence.
Our work opens up a new paradigm to probe the full range of light-matter interaction dynamics within a single platform.
arXiv Detail & Related papers (2023-12-22T13:01:32Z) - Machine Learning Assisted Cognitive Construction of a Shallow Depth
Dynamic Ansatz for Noisy Quantum Hardware [0.0]
We develop a novel protocol that capitalizes on regenerative machine learning methodologies and many-body theoretic measures to construct a highly expressive and shallow ansatz.
The proposed method is highly compatible with state-of-the-art neural error mitigation techniques.
arXiv Detail & Related papers (2023-10-12T16:27:53Z) - Quantum tomography of Rydberg atom graphs by configurable ancillas [1.0965065178451106]
We propose to use ancillas of which the continuously-tunable interactions can generate independent base measurements tomographically sufficient for the quantum state reconstruction of the system of interest.
Experimental tests are performed for Rydberg atom arrays in $N$-body $W$ states, of which the results demonstrate reliable high-fidelity full quantum state reconstruction of the proposed method.
arXiv Detail & Related papers (2022-11-15T06:38:01Z) - Potential and limitations of quantum extreme learning machines [55.41644538483948]
We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements.
Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
arXiv Detail & Related papers (2022-10-03T09:32:28Z) - 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) - On Fast Simulation of Dynamical System with Neural Vector Enhanced
Numerical Solver [59.13397937903832]
We introduce a deep learning-based corrector called Neural Vector (NeurVec)
NeurVec can compensate for integration errors and enable larger time step sizes in simulations.
Our experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability.
arXiv Detail & Related papers (2022-08-07T09:02:18Z) - Scalable photonic platform for real-time quantum reservoir computing [0.0]
Quantum Reservoir Computing exploits the information processing capabilities of quantum systems to solve non-trivial temporal tasks.
Recent progress has shown the potential of QRC exploiting the enlarged Hilbert space.
We propose a photonic platform suitable for real-time QRC based on a physical ensemble of reservoirs in the form of identical optical pulses recirculating through a closed loop.
arXiv Detail & Related papers (2022-07-28T11:44:44Z) - Regression of high dimensional angular momentum states of light [47.187609203210705]
We present an approach to reconstruct input OAM states from measurements of the spatial intensity distributions they produce.
We showcase our approach in a real photonic setup, generating up-to-four-dimensional OAM states through a quantum walk dynamics.
arXiv Detail & Related papers (2022-06-20T16:16:48Z) - 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) - Quantum verification and estimation with few copies [63.669642197519934]
The verification and estimation of large entangled systems represents one of the main challenges in the employment of such systems for reliable quantum information processing.
This review article presents novel techniques focusing on a fixed number of resources (sampling complexity) and thus prove suitable for systems of arbitrary dimension.
Specifically, a probabilistic framework requiring at best only a single copy for entanglement detection is reviewed, together with the concept of selective quantum state tomography.
arXiv Detail & Related papers (2021-09-08T18:20:07Z) - 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) - Encoding strongly-correlated many-boson wavefunctions on a photonic
quantum computer: application to the attractive Bose-Hubbard model [0.0]
Variational quantum algorithms (VQA) are some of the most promising methods to determine the properties of complex strongly correlated quantum many-body systems.
We introduce two different ansatz architectures and demonstrate that the proposed continuous variable quantum circuits can efficiently encode the strongly correlated many-boson wavefunction.
arXiv Detail & Related papers (2021-03-28T00:04:03Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - Entanglement transfer, accumulation and retrieval via quantum-walk-based
qubit-qudit dynamics [50.591267188664666]
Generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies.
We propose a protocol that is able to attain entangled states of $d$-dimensional systems through a quantum-walk-based it transfer & accumulate mechanism.
In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
arXiv Detail & Related papers (2020-10-14T14:33:34Z) - Fast and robust quantum state tomography from few basis measurements [65.36803384844723]
We present an online tomography algorithm designed to optimize all the aforementioned resources at the cost of a worse dependence on accuracy.
The protocol is the first to give provably optimal performance in terms of rank and dimension for state copies, measurement settings and memory.
Further improvements are possible by executing the algorithm on a quantum computer, giving a quantum speedup for quantum state tomography.
arXiv Detail & Related papers (2020-09-17T11:28:41Z)
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.