Cover Image

Learning depth-3 circuits via quantum agnostic boosting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Arunachalam, Srinivasan, Dutt, Arkopal, Gheorghiu, Alexandru, de Oliveira, Michael
Format:	Preprint
Published:	2025
Subjects:	Quantum Physics Computational Complexity Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning stabilizer structure of quantum states
by: Arunachalam, Srinivasan, et al.
Published: (2025)

Learning low-degree quantum objects
by: Arunachalam, Srinivasan, et al.
Published: (2024)

Polynomial-time tolerant testing stabilizer states
by: Arunachalam, Srinivasan, et al.
Published: (2024)

Testing and learning structured quantum Hamiltonians
by: Arunachalam, Srinivasan, et al.
Published: (2024)

A note on polynomial-time tolerant testing stabilizer states
by: Arunachalam, Srinivasan, et al.
Published: (2024)

On estimating the entropy of shallow circuit outputs
by: Gheorghiu, Alexandru, et al.
Published: (2020)

Distributed inner product estimation with limited quantum communication
by: Arunachalam, Srinivasan, et al.
Published: (2024)

Learning quantum states prepared by shallow circuits in polynomial time
by: Landau, Zeph, et al.
Published: (2024)

On the average-case complexity of learning output distributions of quantum circuits
by: Nietner, Alexander, et al.
Published: (2023)

Trade-offs between Entanglement and Communication
by: Arunachalam, Srinivasan, et al.
Published: (2023)

On the Complexity of Decoded Quantum Interferometry
by: Marwaha, Kunal, et al.
Published: (2025)

Quantum state isomorphism problems for groups
by: Gheorghiu, Alexandru, et al.
Published: (2026)

Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness
by: Wadhwa, Chirag, et al.
Published: (2024)

Learning unitaries with quantum statistical queries
by: Angrisani, Armando
Published: (2023)

Learning quantum states and unitaries of bounded gate complexity
by: Zhao, Haimeng, et al.
Published: (2023)

Matrix hypercontractivity, streaming algorithms and LDCs: the large alphabet case
by: Arunachalam, Srinivasan, et al.
Published: (2021)

Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation
by: Chen, Sitan, et al.
Published: (2024)

Unconditionally separating noisy $\mathsf{QNC}^0$ from bounded polynomial threshold circuits of constant depth
by: Hsieh, Min-Hsiu, et al.
Published: (2024)

Entanglement-induced provable and robust quantum learning advantages
by: Zhao, Haimeng, et al.
Published: (2024)

Optimising quantum circuits is generally hard
by: van de Wetering, John, et al.
Published: (2023)

Bell sampling from quantum circuits
by: Hangleiter, Dominik, et al.
Published: (2023)

Learning Robust Observable to Address Noise in Quantum Machine Learning
by: Khanal, Bikram, et al.
Published: (2024)

Classical Verification of Quantum Learning
by: Caro, Matthias C., et al.
Published: (2023)

Learning Quantum Processes with Quantum Statistical Queries
by: Wadhwa, Chirag, et al.
Published: (2023)

Scalable Quantum Error Mitigation with Neighbor-Informed Learning
by: Chen, Zhenyu, et al.
Published: (2025)

Quantum Learning Theory Beyond Batch Binary Classification
by: Mohan, Preetham, et al.
Published: (2023)

The power of quantum circuits in sampling
by: Blanc, Guy, et al.
Published: (2025)

Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noises
by: Zhang, Zhihan, et al.
Published: (2024)

Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
by: Khanal, Bikram, et al.
Published: (2024)

Learning junta distributions, quantum junta states, and QAC$^0$ circuits
by: Bao, Jinge, et al.
Published: (2024)

Complexity of quantum circuits via sensitivity, magic, and coherence
by: Bu, Kaifeng, et al.
Published: (2022)

Information-theoretic generalization bounds for learning from quantum data
by: Caro, Matthias, et al.
Published: (2023)

Learning to erase quantum states: thermodynamic implications of quantum learning theory
by: Zhao, Haimeng, et al.
Published: (2025)

Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
by: Meyer, Sabri, et al.
Published: (2025)

Spacetime-Efficient Low-Depth Quantum State Preparation with Applications
by: Gui, Kaiwen, et al.
Published: (2023)

Proper vs Improper Quantum PAC learning
by: Nayak, Ashwin, et al.
Published: (2024)

Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom
by: Grewal, Sabee, et al.
Published: (2022)

Reducing the Complexity of Matrix Multiplication to $O(N^2log_2N)$ by an Asymptotically Optimal Quantum Algorithm
by: Yao, Jiaqi, et al.
Published: (2026)

Quantum computing and persistence in topological data analysis
by: Gyurik, Casper, et al.
Published: (2024)

Efficient quantum circuits for high-dimensional representations of SU(n) and Ramanujan quantum expanders
by: Iyer, Vishnu, et al.
Published: (2026)