arXiv subjects

Viet T. Tran

Publications and source records attributed to Viet T. Tran.

4 recordsLinked to original sources

arXiv2026

An infinite hierarchy of multi-copy quantum learning tasks

Learning properties of quantum states from measurement data is a fundamental challenge in quantum information. The sample complexity of such tasks depends crucially on the measurement primitive. While shadow tomography achieves sample-efficient learning by allowing entangling measurements across many copies, it requires prohibitively deep circuits. At the other extreme, two-copy measurements already yield exponential advantages over single-copy strategies in tasks such as Pauli tomography. In this work we show that such sharp separations extend far beyond the two-copy regime: for every prime c we construct explicit learning tasks of degree c, which are exponentially hard with (c - 1)-copy measurements but efficiently solvable with c-copy measurements. Our protocols are not only sample-efficient but also realizable with shallow circuits. Extending further, we show that such finite-degree tasks exist for all square-free integers c, pointing toward a general principle underlying their existence. Together, our results reveal an infinite hierarchy of multi-copy learning problems, uncovering new phase transitions in sample complexity and underscoring the role of reliable quantum memory as a key resource for exponential quantum advantage.

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arXiv2026

Parametric Quantum State Tomography with HyperRBMs

Quantum state tomography (QST) is essential for validating quantum devices but suffers from exponential scaling in system size. Neural-network quantum states, such as Restricted Boltzmann Machines (RBMs), can efficiently parameterize individual many-body quantum states and have been successfully used for QST. However, existing approaches are point-wise and require retraining at every parameter value in a phase diagram. We introduce a parametric QST framework based on a hypernetwork that conditions an RBM on Hamiltonian control parameters, enabling a single model to represent an entire family of quantum ground states. Applied to the transverse-field Ising model, our HyperRBM achieves high-fidelity reconstructions from local Pauli measurements on 1D and 2D lattices across both phases and through the critical region. Crucially, the model accurately reproduces the fidelity susceptibility and identifies the quantum phase transition without prior knowledge of the critical point. These results demonstrate that hypernetwork-modulated neural quantum states provide an efficient and scalable route to tomographic reconstruction across full phase diagrams.

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arXiv2025

One, Two, Three: One empirical evaluation of a two-copy shadow tomography scheme with triple efficiency

Shadow tomography protocols have recently emerged as powerful tools for efficient quantum state learning, aiming to reconstruct expectation values of observables with fewer resources than traditional quantum state tomography. For the particular case of estimating Pauli observables, entangling two-copy measurement schemes can offer an exponential improvement in sample complexity over any single-copy strategy conceivable [1, Huang, Kueng, Preskill, PRL(2021)]. A recent refinement of these ideas by King et al. [2, King, Gosset, Kothari, Babbush, SODA (2025)] does not only achieve polynomial sample complexity, but also maintains reasonable computational demands and utilizes joint measurements on only a small constant number of state copies. This `triple efficiency' is achievable for any subset of $n$-qubit Pauli observables, whereas single-copy strategies can only be efficient if the Pauli observables have advantageous structure. In this work, we complement existing theoretical performance guarantees with the empirical evaluation of triply efficient shadow tomography using classical, noise-free simulations. Our findings indicate that the empirical sample complexity aligns closely with theoretical predictions for stabilizer states and, notably, demonstrates slightly improved scaling for random Gibbs states compared to established theoretical bounds. In addition, we improve a central subroutine in the triply-efficient shadow protocol by leveraging insights from a refined quantum and quantum-inspired convex optimization algorithm [3, Henze et al. arXiv:2502.15426 (2025)]. To summarize, our empirical sample complexity studies of triply efficient shadow tomography not only confirm existing theoretical scaling behavior, but also showcase that the actual constants involved are comparatively benign. Hence, this protocol has the potential to also be very sample-efficient in practice.

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arXiv2023

Improved machine learning algorithm for predicting ground state properties

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an $n$-qubit gapped local Hamiltonian after learning from only $\mathcal{O}(\log(n))$ data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\mathcal{O}(n^c)$ data for a large constant $c$. Furthermore, the training and prediction time of the proposed ML model scale as $\mathcal{O}(n \log n)$ in the number of qubits $n$. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.

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