Two meta-learning algorithms are introduced: Meta-Variational Quantum Thermalizer (Meta-VQT) and Neural Network Meta-VQT (NN-Meta VQT) for efficient thermal state preparation of parametrized Hamiltonians on Noisy Intermediate-Scale Quantum (NISQ) devices and a Quantum Boltzmann Machine (QBM) on a 2-qubit Heisenberg mod...
R. V. Bhat, Rahul Bhowmick, Avinash Singh et al.· arXiv.org· 3 citations
This work evaluates ground-state estimation of 6- to 18-qubit Hamiltonians using the Variational Quantum Eigensolver (VQE) and quantum phase classification for Hamiltonians up to 12 qubits and uses these methods as a warm-start pretraining strategy for Hamiltonians with exponential-dimensional dynamical Lie algebras.
Rahul Bhowmick, Harsh Wadhwa, Avinash Singh et al.· 4 citations
A hybrid Benders solver with output-sensitive iteration bounds is developed, leveraging a Helly-based conflict compression theory to avoid generic exponential cut explosion and establishes an unconditionally polynomial Benders iteration complexity in network size.
Wen-Xin Li, Wen-Chao Liu, An-Zhe Jiang et al.· 0 citations
This work exposes the structural capabilities of PQKs by isolating and evaluating their native inductive bias through comparisons between untuned and tuned PQKs and proposes novel quantum feature maps that leverage two-qubit entanglement to capture multivariate correlations in IoT data.
Ravi Kumar Jha, Hsuan-Yin Lin, E. Rosnes· 0 citations
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We study Heisenberg antiferromagnets through a graph-theoretic representation of their Hamiltonians. In the computational basis of a fixed-magnetization sector, the Heisenberg Hamiltonian equals a constant minus the weighted Laplacian of a graph whose vertices are spin configurations and whose edges connect configurati...
Mahmud Ashraf Shamim, M. Raj, Mohamed Hibat-Allah et al.· 2 citations
It is shown that encoding the query can change the circuit's hypothesis class over the signal by modulating how its existing signal frequencies are combined, without introducing new sample-dependent information.
Felix Frohnert, Adrián Pérez-Salinas, Sebastian Lange et al.· 0 citations
This study shows that analytical composition can be sufficient in simple regimes, that simulation becomes necessary when stochastic memory effects shape the delivered fidelity distribution, and that lightweight learned surrogates can approximate simulator labels at much lower runtime cost for online control.
Anis Bekri, Amar Abane, V. Mai et al.· 0 citations
These results make the relationship between expressivity and data structure explicit for IQP kernels: expressive power translates into a practical benefit only when the accessible frequencies match with the structure of the data, which provides a criterion for selecting the bandwidth when the periodic or multimodal str...
Valeria Repetto, Stefano Melchionna, R. D'Aurizio· 0 citations
This work amortizes a major bottleneck in QAOA by an up-front data generation and training cost and can be amortized over time in settings where many similar problems are optimized on a regular basis, as is often the case in applications for industry.
Johannes Jakubik, Isabelle Wittmann, Zaheed Gaffoor et al.· 0 citations
This work proposes a variational framework for solving Lindblad master equations by minimizing a residual cost function using a compositional ansatz obtained by combining a classical neural network with a parameterized quantum circuit, and demonstrates a theoretical connection between expressivity, covering numbers, ef...
Rafael D'Iaz, Julio Guerrero García, Elvira Romera Gutiérrez· 0 citations
Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as an agronomic recommendation. Materials and methods: Ten classifiers were evaluated using five matched st...
Kwan Hong TAN· Academia AI and Applications· 0 citations
It is concluded that horizontally scalable, log-based messaging, combined with fault-tolerant, in-memory and micro-batch computation and durable, replicated storage constituted the technical foundation that made real-time, ML-driven enterprise analytics feasible within this period and that this layered architecture con...
Harsh Verma· European journal of computer...· 0 citations
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Microsoft Research Blog· microsoft.comOct 21, 2026
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
