Skip to content
Preprint

Nullspace-guided Adaptive Bootstrap of Quantum Many-body Systems

Sep 2026 · 0 citations · 43 references
Physics

Abstract

We introduce a nullspace-guided adaptive (NGA) bootstrap method that improves the energy lower bounds of quantum many-body ground states by refining the bootstrap basis in a dynamic and incremental way. At each iteration, the optimized moment matrix reveals a nullspace of saturated positivity directions, which is intuitively interpreted as annihilators of the approximate ground-state subspace. The NGA bootstrap then prunes operators with small nullspace leverage and grows the basis along descendants of these null directions. By applying the NGA bootstrap to the transverse-field Ising chain, we obtain nearly exact energy lower bounds because the algorithm automatically discovers the eigenoperator structure in terms of Jordan-Wigner fermions from a minimal local bootstrap basis. For the Hubbard chain, it improves upon state-of-the-art energy lower bounds by up to two orders of magnitude, reaching errors ranging from $10^{-3}$ down to $10^{-5}$ in the strongly correlated regimes. We further show that the NGA framework can be used to improve the certified two-sided bounds on general observables. In addition, the bootstrap error decreases approximately as a power law with increasing computational resources. These results suggest that our method provides a practical and scalable route toward accurate bootstrap of general quantum many-body systems.

View source

Similar papers

Preprint Oct 2026

Efficient Sampling for Many-Body Fermionic Non-Gaussianity

Fermionic non-Gaussianity is a key resource for universal quantum computation, and its behavior in quantum many-body systems has attracted growing interest. Recently, a convolution-based measure, which we call the magic R\'enyi entropy (MRE), has been proposed as a resource measure of non-Gaussianity, but its evaluatio...

Ryota Matsuda, Masahiro Hoshino, Y. Ashida · 0 citations
Preprint Oct 2026

A Basis-Aware Approach to Quantum Sampling of the Fermi-Hubbard Ladder

Sample-based quantum diagonalization can reduce the classical cost of many-body calculations by restricting diagonalization to configurations identified through quantum sampling, but its efficiency depends on how compactly the target state is represented in the sampled basis. We investigate this dependence for sample-b...

Adam Leicester, Zi-Xu Wang, Jack Mandell et al. · 0 citations
Preprint Oct 2026

Convergence Without Finite Exactness in the Eigenstate Quantum Bootstrap

The eigenstate quantum-mechanical bootstrap can converge to the physical moment set while remaining nonexact at every finite order. We prove this for a one-dimensional quasi-exactly solvable sextic oscillator at explicitly known eigenvalues, including excited levels. At every finite order, the lower and upper bounds on...

Ye-Qing Zhou, Daniel Keren · 1 citation
Preprint Aug 2026

Efficient search for excitable zero-modes in constrained systems

Kinetically constrained systems, such as those representing Rydberg atom arrays in the blockade regime, have gathered considerable attention due to the presence of atypical eigenstates in their spectrum. The latter manifests itself through the presence of quantum many-body scars as well as unusually large zero-mode (ZM...

Jean-Yves Desaules · 0 citations
Preprint Oct 2026

The magic cost of fermionic randomness

Sampling-based quantum advantage raises a basic resource question: how much non-Gaussianity is needed to reproduce the random-matrix statistics often associated with quantum chaos? Passive fermionic linear optics preserves Gaussian structure, and even Haar-random mode mixing fails to anticoncentrate for Slater inputs....

Chen-Feng Cao, Yi-Fan Tang, J. Eisert · 0 citations
Preprint Oct 2026

Post-Optimization Subspace Corrections to Quantum Ans\"atze

Identity-augmented q-sc-EOM improves molecular potential-energy curves obtained from truncated ADAPT-VQE ans\"atze with limited expressivity and recovers accuracy from states produced by sampled operator ranking without additional gradient-based operator selection, providing an advantage when small pool gradients make...

P. F. Kwao, Bharath Sambasivam, Jason Saroni et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.