arXiv Machine Learning
Jul 3
Optimal Stabilizer Testing and Learning with Limited Quantum Memory
arXiv:2607. 02444v1 Announce Type: cross Abstract: We study stabilizer state testing and learning with limited coherent quantum memory.
Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
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Hugging Face Trending Papers
Jul 2
Optimal Stabilizer Testing and Learning with Limited Quantum Memory
We study stabilizer state testing and learning with limited coherent quantum memory. Here an algorithm sequentially receives copies of an unknown $n$-qubit state, but may keep only $k$ qubits of coherent quantum memory between measurements.
arXiv Machine Learning
Sep 30
Optimal Quantum-Classical Separations for Exact Learning
arXiv:2609.38073v1 Announce Type: cross Abstract: We study exact learning with membership queries for concept classes $\mathcal C\subseteq\{0,1\}^N$, focusing on the relationships among their determi...
arXiv Statistics ML
Oct 1
Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries
The paper investigates whether having only forward access to a state-preparation unitary—without its inverse—can reduce the number of queries needed for quantum PAC learning. By analyzing worst-case scenarios over all compatible unitaries and finite dimensions, the authors prove that the optimal forward-only query complexities for realizable and agnostic learning are θ((d+log(1/δ))/ε) and θ((d+log(1/δ))/ε²), respectively, matching classical and quantum-copy bounds. These results demonstrate that forward-only access offers no asymptotic advantage over classical data or quantum copies, highlighting the essential role of inverse access for any improvement in the realizable setting.
arXiv Machine Learning
Jun 11
Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
arXiv:2606. 12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest.
arXiv Machine Learning
Jun 3
Coherent Swap Regret and Channel-Proof Learning
arXiv:2606. 02655v1 Announce Type: cross Abstract: External regret certifies stability only against replacing one's behavior by a fixed alternative.
arXiv Machine Learning
Sep 23
When are bosonic Gaussian states classical to learn?
arXiv:2609.26705v1 Announce Type: cross Abstract: A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to...
arXiv Machine Learning
Aug 12
The Exact Replica Threshold for Nonlinear Moments of Quantum States
arXiv:2604. 22627v2 Announce Type: replace-cross Abstract: Joint measurements on multiple copies of a quantum state provide access to nonlinear observables such as $\operatorname{tr}(\rho^t)$, but whether replica number marks a sharp information-theoretic resource boundary has remained unclear.
arXiv Machine Learning
Jun 25
Minimax PAC Bounds for Learning in Exogenous Contextual MDPs
arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.
arXiv Machine Learning
Jul 21
AQKA: Active Quantum Kernel Acquisition Under a Shot Budget
arXiv:2605. 14672v2 Announce Type: replace Abstract: Estimating an $N \times N$ quantum kernel from circuit fidelities requires $\Theta(N^2 S)$ measurement shots, the dominant bottleneck for deployment on near-term hardware.
arXiv Machine Learning
Jul 31
Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise
arXiv:2607. 25492v2 Announce Type: replace Abstract: We study stochastic optimization with heavy-tailed gradient noise.
arXiv Machine Learning
Oct 5
Where Quantum Fourier Sampling Stops Short: A Three-Gate Audit Protocol for Delay-PUF Security Models
arXiv:2610.02636v1 Announce Type: cross Abstract: Quantum Fourier sampling may help audit the spectral learnability of delay-based physical unclonable functions (PUFs). We ask whether that promise su...