Verifying random matrix product states with autoregressive local measurements
- URL: http://arxiv.org/abs/2604.16578v1
- Date: Fri, 17 Apr 2026 13:12:27 GMT
- Title: Verifying random matrix product states with autoregressive local measurements
- Abstract summary: Matrix product states (MPS) are a central language for one-dimensional quantum matter.<n>We introduce an emphautoregressive importance sampler that draws Pauli strings sequentially from efficiently computable conditional distributions.<n>We also develop a grouped extension that constructs qubit-wise commuting measurement settings via a emphsorting string.
- Score: 7.085044605508094
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Matrix product states (MPS) are a central language for one-dimensional quantum matter and a practical target for near-term quantum simulators and variational algorithms. Yet, while substantial effort has focused on preparing MPS with shallow circuits, scalable methods to \emph{verify} that a many-body device has actually produced the intended state remain underdeveloped. Direct fidelity estimation (DFE) relies only on local Pauli measurements, but in many-body settings it suffers an exponential classical overhead from the preprocessing needed to sample Pauli strings. We eliminate this obstacle by introducing an \emph{autoregressive} importance sampler that draws Pauli strings sequentially from efficiently computable conditional distributions, reducing the per-shot classical overhead to linear scaling in the number of qubits. We further develop a grouped extension that constructs qubit-wise commuting measurement settings via a \emph{sorting string} and simultaneously estimates the entire commuting group from a single setting, significantly reducing estimator variance while preserving efficient postprocessing. Our approach extends naturally to matrix product operators (MPO), enabling scalable verification of tensor-network states and observables in long one-dimensional quantum systems. We utilize random MPS as a natural benchmark for generic 1D entangled states.
Related papers
- Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation [48.887627688666974]
Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies.<n>Here, we propose a scalable noise-learning framework for Lindblad dissipation rates that combines a simulation method, the Jump Method (TJM)<n>We demonstrate the approach on two noise models in the Ising model, in which independent dissipation rates are learned for each site up to $N_mathrmsite=16$.<n>This combination of scalable numerics and rigorous theoretical guarantees positions TJM-based noise learning as a practical foundation for characterizing dissipation in large quantum devices.
arXiv Detail & Related papers (2026-08-25T15:07:11Z) - Basis-Adaptive Sparse-State Simulation of Quantum Circuits [0.0]
We introduce an algorithm called Basis-Adaptive Sparse-State Simulation (BASS)<n>BASS updates each qubit's local representation basis during execution rather than locking the computational basis for the entire circuit.<n>On structured brickwork circuits, BASS achieves substantially higher fidelity than the fixed-basis approach.
arXiv Detail & Related papers (2026-05-26T17:02:39Z) - Quantum-inspired classical simulation through randomized time evolution [0.0]
We build on recently introduced randomized quantum algorithms for time evolution (TE-PAI)<n>We adapt them to the classical simulation context with the purpose of enabling massive parallelisation.<n>We numerically observe that MPS TE-PAI is substantially more robust against severe bond-dimension truncation than product formulas.
arXiv Detail & Related papers (2026-04-14T15:09:29Z) - CAWN: Continuous Acoustic Wave Networks for Autoregressive Language Modeling [46.16066322190728]
We introduce the Continuous Acoustic Wave Network (CAWN), a fully continuous sequence-mixing architecture.<n>Instead of discrete matrix-based attention, CAWN projects hidden states into multi-headed complex-domain phasors.<n>To prevent signal degradation over ultra-long contexts, we introduce a dual-gated Selective Phase Resonance mechanism.
arXiv Detail & Related papers (2026-04-05T20:13:22Z) - Sample- and Hardware-Efficient Fidelity Estimation by Stripping Phase-Dominated Magic [0.2796197251957245]
This work proposes a sample- and gate-efficient fidelity estimation algorithm that is affordable within feasible quantum devices.<n>We show that the fidelity estimation with pure states close to the structure of phase states, for which sample-efficient DFE is limited by their strong entanglement and magic, can be done by using $mathcalO(mathrmpoly(n))$ sampling copies, with a single $n$-qubit fan-out gate.
arXiv Detail & Related papers (2026-02-10T12:13:25Z) - Self-Rewarding Sequential Monte Carlo for Masked Diffusion Language Models [58.946955321428845]
This work presents self-rewarding sequential Monte Carlo (SMC)<n>Our algorithm stems from the observation that most existing MDLMs rely on a confidence-based sampling strategy.<n>We introduce the trajectory-level confidence as a self-rewarding signal for assigning particle importance weights.
arXiv Detail & Related papers (2026-02-02T09:21:45Z) - Score-Based Turbo Message Passing for Plug-and-Play Compressive Imaging [45.77515171733844]
We devise an algorithm that integrates a score-based minimum mean-squared error (MMSE) denoiser for compressive image recovery.<n>The resulting algorithm, named score-based turbo message passing (STMP), combines the fast convergence of message passing with the power of score-based generative priors.
arXiv Detail & Related papers (2025-12-16T14:24:12Z) - WUSH: Near-Optimal Adaptive Transforms for LLM Quantization [52.77441224845925]
Quantization to low bitwidth is a standard approach for deploying large language models.<n>A few extreme weights and activations stretch the dynamic range and reduce the effective resolution of the quantizer.<n>We derive, for the first time, closed-form optimal linear blockwise transforms for joint weight-activation quantization.
arXiv Detail & Related papers (2025-11-30T16:17:34Z) - Operator-aware shadow importance sampling for accurate fidelity estimation [8.212934913387384]
Grouping-based DFE achieves strong accuracy for small systems but suffers from exponential scaling.<n>Our algorithm improves upon the grouping-based algorithms for Haar-random states.<n>For structured states such as the GHZ and W states, our algorithm also eliminates the exponential memory requirements of previous grouping-based methods.
arXiv Detail & Related papers (2025-11-03T14:09:31Z) - Quantum circuit simulation with a local time-dependent variational principle [2.2627671295262215]
We develop a new state-of-the-art circuit simulation method for quantum computing, condensed matter, and beyond.<n>We benchmark the approach on five 49-qubit circuits: three Hamiltonian circuits (1D open and periodic Heisenberg, 2D 7x7 Ising), and two algorithmic ones (quantum approximate optimization, hardware-efficient ansatz)
arXiv Detail & Related papers (2025-08-13T18:00:02Z) - Randomized semi-quantum matrix processing [0.0]
We present a hybrid quantum-classical framework for simulating generic matrix functions.
The method is based on randomization over the Chebyshev approximation of the target function.
We prove advantages on average depths, including quadratic speed-ups on costly parameters.
arXiv Detail & Related papers (2023-07-21T18:00:28Z) - You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli
Sampling [38.34914626128062]
We show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH) decreases the quadratic complexity of such models to linear.
We evaluate our algorithm on the GLUE benchmark with standard 512 sequence length where we see favorable performance relative to a standard pretrained Transformer.
arXiv Detail & Related papers (2021-11-18T14:24:34Z) - Robust preparation of Wigner-negative states with optimized
SNAP-displacement sequences [41.42601188771239]
We create non-classical states of light in three-dimensional microwave cavities.
These states are useful for quantum computation.
We show that this way of creating non-classical states is robust to fluctuations of the system parameters.
arXiv Detail & Related papers (2021-11-15T18:20:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.