We study the sample complexity of shadow tomography in the high-precision regime under realistic measurement constraints. Given an unknown $d$-dimensional quantum state $\rho$ and a known set of observables $\{O_i\}_{i=1}^m$, the goal is to estimate expectation values $\{\mathrm{tr}(O_i\rho)\}_{i=1}^m$ to accuracy $\ep...
Senrui Chen, W. Gong, Sisi Zhou· Annual Conference Computatio...· 10 citations
This work proposes ScoreNF, a score-based learning framework built on the Normalizing Flow architecture, integrated with an Independent Metropolis-Hastings (IMH) module, enabling efficient and unbiased sampling from unnormalized target distributions, and shows that ScoreNF maintains high performance even with small tra...
V. Kanaujia, Vipul Arora· IEEE International Conferenc...· 3 citations
Simulation-based quantum system inference is introduced, a unified, likelihood-free framework that learns parameter posteriors directly from classical simulation data to reduce data-acquisition requirements in quantum experiments and accelerates parameter inference, providing a practical route to characterizing and imp...
Hang Zou, A. F. Kockum, Martin Rahm et al.· 0 citations
This work introduces a fully autonomous system combining differentiable programming and unsupervised learning for quantum phase discovery, and establishes autonomous, gradient-driven exploration of Hamiltonian space as a practical route to discovering quantum phases without phase labels or a prescribed target phase.
Shi-Yu Zhou, Yuxuan Zhang, S. Wetzel et al.· 0 citations
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It is confirmed that an ideal sequential and terminal measurement schedule delivers the same complete bit string distribution under matched causal operations, while normalized postselection can amplify perturbations inversely with prefix acceptance.
Bhvain Makwana, Kashyap Patel, Manjunath Joshi et al.· 0 citations
In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rendering post-deletion prediction error an ambiguous metric for machine unlearning. To resolve this ambiguity, we define the deletion floor as the expected target loss und...
Can Polat, Mustafa Kurban, Erchin Serpedin et al.· 0 citations
How many directions in weight space does training need? The intrinsic dimension answers this with the smallest number of random directions in which training still reaches a target accuracy, and small values have motivated parameter-efficient methods such as LoRA. We measure it for variational Monte Carlo (VMC), which t...
Lu Wei, Yu-Feng Wang, Chen-Feng Cao et al.· 0 citations
This work studies language-model synthesis of Clifford circuits, which prepare the stabilizer states used in quantum error correction and admit exact classical verification, in a target-conditioned framework.
Lu Wei, Yu-Feng Wang, Chen-Feng Cao et al.· 0 citations
This work introduces the Universal Quantum Transformer (UQT), a novel, quantum-native computing architecture that uses the physical properties of multi-qubit systems as a universal inductive bias for exact algebraic and compositional reasoning.
This work introduces QC-Stark, a benchmark for evaluating large language models (LLMs) on 11 quantum computing (QC) tasks, spanning circuit construction, debugging, compilation, error correction, and simulation, and finds that overall rankings mask substantial per-task variation.
Recent advances in quantum computing are opening new opportunities for computationally intensive decision problems. This paper studies how quantum computing can improve Monte Carlo tree search (MCTS) in the fixed-confidence setting, where the goal is to identify a near-optimal move in a given game tree with high probab...
Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise. Addressing this limitation requires hardware-facing capabilities beyond gate sequences: mid-circuit measurement and classical feedback for quantum error correction (QEC), precise timing for dynamical dec...
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
