Attention to Quantum Complexity

• URL: http://arxiv.org/abs/2405.11632v1
• Date: Sun, 19 May 2024 17:46:40 GMT
• Title: Attention to Quantum Complexity
• Authors: Hyejin Kim, Yiqing Zhou, Yichen Xu, Kaarthik Varma, Amir H. Karamlou, Ilan T. Rosen, Jesse C. Hoke, Chao Wan, Jin Peng Zhou, William D. Oliver, Yuri D. Lensky, Kilian Q. Weinberger, Eun-Ah Kim,
• Abstract summary: We introduce the Quantum Attention Network (QuAN), a versatile classical AI framework. QuAN treats measurement snapshots as tokens while respecting their permutation invariance. We rigorously test QuAN across three distinct quantum simulation settings.
• Score: 21.766643620345494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The imminent era of error-corrected quantum computing urgently demands robust methods to characterize complex quantum states, even from limited and noisy measurements. We introduce the Quantum Attention Network (QuAN), a versatile classical AI framework leveraging the power of attention mechanisms specifically tailored to address the unique challenges of learning quantum complexity. Inspired by large language models, QuAN treats measurement snapshots as tokens while respecting their permutation invariance. Combined with a novel parameter-efficient mini-set self-attention block (MSSAB), such data structure enables QuAN to access high-order moments of the bit-string distribution and preferentially attend to less noisy snapshots. We rigorously test QuAN across three distinct quantum simulation settings: driven hard-core Bose-Hubbard model, random quantum circuits, and the toric code under coherent and incoherent noise. QuAN directly learns the growth in entanglement and state complexity from experimentally obtained computational basis measurements. In particular, it learns the growth in complexity of random circuit data upon increasing depth from noisy experimental data. Taken to a regime inaccessible by existing theory, QuAN unveils the complete phase diagram for noisy toric code data as a function of both noise types. This breakthrough highlights the transformative potential of using purposefully designed AI-driven solutions to assist quantum hardware.

Related papers

• 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)
• Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness [0.0]
  We prove the natural robustness of quantum statistical queries for learning quantum processes. We adapt a learning algorithm for constant-depth quantum circuits to the quantum statistical query setting. We show the hardness of the quantum threshold search problem from quantum statistical queries.
  arXiv  Detail & Related papers  (2024-05-20T14:55:20Z)
• Unconditionally decoherence-free quantum error mitigation by density matrix vectorization [4.2630430280861376]
  We give a new paradigm of quantum error mitigation based on the vectorization of density matrices. Our proposal directly changes the way of encoding information and maps the density matrices of noisy quantum states to noiseless pure states. Our protocol requires no knowledge of the noise model, no ability to tune the noise strength, and no ancilla qubits for complicated controlled unitaries.
  arXiv  Detail & Related papers  (2024-05-13T09:55:05Z)
• 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)
• 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)
• 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 benefit of the quantum equation of motion for the strongly coupled many-body problem [0.0]
  The quantum equation of motion (qEOM) is a hybrid quantum-classical algorithm for computing excitation properties of a fermionic many-body system. We demonstrate explicitly that the qEOM exhibits a quantum benefit due to the independence of the number of required quantum measurements.
  arXiv  Detail & Related papers  (2023-09-18T22:10:26Z)
• Complexity analysis of weakly noisy quantum states via quantum machine learning [1.203955415344484]
  We focus on the complexity of weakly noisy states, which we define as the size of the shortest quantum circuit required to prepare the noisy state. We propose a quantum machine learning (QML) algorithm that exploits the intrinsic-connection property of structured quantum neural networks.
  arXiv  Detail & Related papers  (2023-03-31T06:02:44Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• A semi-agnostic ansatz with variable structure for quantum machine learning [0.3774866290142281]
  Variational Quantum Algorithms (VQAs) offer a powerful, flexible paradigm for programming near-term quantum computers. We present a variable structure approach to build ansatzes for VQAs. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications.
  arXiv  Detail & Related papers  (2021-03-11T14:58:40Z)

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.