Machine-learning certification of multipartite entanglement for noisy quantum hardware

• URL: http://arxiv.org/abs/2408.12349v1
• Date: Thu, 22 Aug 2024 12:47:58 GMT
• Title: Machine-learning certification of multipartite entanglement for noisy quantum hardware
• Authors: Andreas J. C. Fuchs, Eric Brunner, Jiheon Seong, Hyeokjea Kwon, Seungchan Seo, Joonwoo Bae, Andreas Buchleitner, Edoardo G. Carnio,
• Abstract summary: Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. We develop a certification pipeline that feeds statistics of random local measurements into a non-linear dimensionality reduction algorithm. We verify the accuracy of its predictions on simulated test data, and apply it to states prepared on IBM quantum computing hardware.
• Score: 1.204553980682492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable -- a task referred to as the separability problem -- poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware.

Related papers

• Scalable multipartite entanglement criteria for continuous variables [6.181008505226926]
  We propose a quite general entanglement detection method for all kinds of multipartite entanglement of multimode continuous variable systems. Our criterion can detect entanglement, genuine entanglement and other kinds of inseparabilities almost imidiately.
  arXiv  Detail & Related papers  (2024-11-05T13:27:19Z)
• Bounding the Sample Fluctuation for Pure States Certification with Local Random Measurement [4.923287660970805]
  Recent advancements in randomized measurement techniques have provided fresh insights in this area. We investigate the fundamental properties of schemes that certify pure quantum states through random local Haar measurements. Our results unveil the intrinsic interplay between operator complexity and the efficiency of quantum algorithms, serving as an obstacle to local certification of pure states with long-range entanglement.
  arXiv  Detail & Related papers  (2024-10-22T02:26:44Z)
• 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)
• Mixed-Dimensional Qudit State Preparation Using Edge-Weighted Decision Diagrams [3.393749500700096]
  Quantum computers have the potential to solve intractable problems. One key element to exploiting this potential is the capability to efficiently prepare quantum states for multi-valued, or qudit, systems. In this paper, we investigate quantum state preparation with a focus on mixed-dimensional systems.
  arXiv  Detail & Related papers  (2024-06-05T18:00:01Z)
• Measurement-Device-Independent Detection of Beyond-Quantum State [53.64687146666141]
  We propose a measurement-device-independent (MDI) test for beyond-quantum state detection. We discuss the importance of tomographic completeness of the input sets to the detection.
  arXiv  Detail & Related papers  (2023-12-11T06:40:13Z)
• Multimodal deep representation learning for quantum cross-platform verification [60.01590250213637]
  Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms. We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities. We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
  arXiv  Detail & Related papers  (2023-11-07T04:35:03Z)
• Analysing quantum systems with randomised measurements [0.4179230671838898]
  We present the advancements made in utilising randomised measurements in various scenarios of quantum information science. We describe how to detect and characterise different forms of entanglement, including genuine multipartite entanglement and bound entanglement. We also present an overview on the estimation of non-linear functions of quantum states and shadow tomography from randomised measurements.
  arXiv  Detail & Related papers  (2023-07-03T18:00:01Z)
• Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning [47.991114317813555]
  Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots. This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
  arXiv  Detail & Related papers  (2023-04-06T22:05:21Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• Heterogeneous Multipartite Entanglement Purification for Size-Constrained Quantum Devices [68.8204255655161]
  Purifying entanglement resources after their imperfect generation is an indispensable step towards using them in quantum architectures. Here we depart from the typical purification paradigm for multipartite states explored in the last twenty years. We find that smaller sacrificial' states, like Bell pairs, can be more useful in the purification of multipartite states than additional copies of these same states.
  arXiv  Detail & Related papers  (2020-11-23T19:00:00Z)
• Reconstructing quantum states with quantum reservoir networks [4.724825031148412]
  We introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network, and operates as a comprehensive device for reconstructing an arbitrary quantum state.
  arXiv  Detail & Related papers  (2020-08-14T14:01:55Z)

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.