Computing exact moments of local random quantum circuits via tensor networks.
Quantum Mach. Intell., December, 2024
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
Quantum, March, 2024
On the practical usefulness of the Hardware Efficient Ansatz.
Quantum, 2024
Efficient quantum-enhanced classical simulation for patches of quantum landscapes.
CoRR, 2024
Random ensembles of symplectic and unitary states are indistinguishable.
CoRR, 2024
Classically estimating observables of noiseless quantum circuits.
CoRR, 2024
Quantum Convolutional Neural Networks are (Effectively) Classically Simulable.
CoRR, 2024
Exact spectral gaps of random one-dimensional quantum circuits.
CoRR, 2024
Gate-based quantum simulation of Gaussian bosonic circuits on exponentially many modes.
CoRR, 2024
Architectures and random properties of symplectic quantum circuits.
CoRR, 2024
A Review of Barren Plateaus in Variational Quantum Computing.
CoRR, 2024
A semi-agnostic ansatz with variable structure for variational quantum algorithms.
Quantum Mach. Intell., December, 2023
Variational Quantum Linear Solver.
Quantum, November, 2023
The battle of clean and dirty qubits in the era of partial error correction.
Quantum, July, 2023
Unifying and benchmarking state-of-the-art quantum error mitigation techniques.
Quantum, June, 2023
Subtleties in the trainability of quantum machine learning models.
Quantum Mach. Intell., June, 2023
Theory of overparametrization in quantum neural networks.
Nat. Comput. Sci., 2023
Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing.
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CoRR, 2023
Deep quantum neural networks form Gaussian processes.
CoRR, 2023
On the universality of S<sub>n</sub>-equivariant k-body gates.
CoRR, 2023
Effects of noise on the overparametrization of quantum neural networks.
CoRR, 2023
Diagnosing Barren Plateaus with Tools from Quantum Optimal Control.
Quantum, September, 2022
Non-trivial symmetries in quantum landscapes and their resilience to quantum noise.
Quantum, September, 2022
Challenges and opportunities in quantum machine learning.
Nat. Comput. Sci., 2022
Resource frugal optimizer for quantum machine learning.
CoRR, 2022
Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks.
CoRR, 2022
Theory for Equivariant Quantum Neural Networks.
CoRR, 2022
Representation Theory for Geometric Quantum Machine Learning.
CoRR, 2022
Exponential concentration and untrainability in quantum kernel methods.
CoRR, 2022
Inference-Based Quantum Sensing.
CoRR, 2022
Group-Invariant Quantum Machine Learning.
CoRR, 2022
Covariance matrix preparation for quantum principal component analysis.
CoRR, 2022
Effect of barren plateaus on gradient-free optimization.
Quantum, 2021
Generalization in quantum machine learning from few training data.
CoRR, 2021
Entangled Datasets for Quantum Machine Learning.
CoRR, 2021
Equivalence of quantum barren plateaus to cost concentration and narrow gorges.
CoRR, 2021
A semi-agnostic ansatz with variable structure for quantum machine learning.
CoRR, 2021
Connecting ansatz expressibility to gradient magnitudes and barren plateaus.
CoRR, 2021
Variational Quantum Fidelity Estimation.
Quantum, 2020
Optimizing parametrized quantum circuits via noise-induced breaking of symmetries.
CoRR, 2020
Absence of Barren Plateaus in Quantum Convolutional Neural Networks.
CoRR, 2020
Noise-Induced Barren Plateaus in Variational Quantum Algorithms.
CoRR, 2020
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets.
CoRR, 2020
Trainability of Dissipative Perceptron-Based Quantum Neural Networks.
CoRR, 2020
Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks.
CoRR, 2020