Ground-State Preparation by Projection onto the Maximal Decoherence-Free Subspace: Operator-Algebraic Derivation and Constant-Depth Execution on 156-Qubit Processors
Abstract
Abstract We present a framework for preparing quantum ground states by structural projection rather than by deep-circuit Trotterized dynamical simulation. The method is grounded in a rigorous operator-algebraic formalization spanning 297 parts (the IQS/EQS corpus), from which we extract the complete derivation chain 𝐻_boundary → 𝐴_𝜇 → 𝑃_𝐻 → 𝐴_𝐻. We prove that the Host projector 𝑃_𝐻 = 𝑃_DFS^max acts as a native state-preparation oracle — a single projective step applied once, not a query oracle in the phase-kickback sense: assuming appropriate overlap, it projects any initial state |𝜓₀⟩ into the prescribed ground-state subspace with an 𝒪(1) execution depth on the quantum device.The body of this paper is confined to the framework and its theorems; the complete experimental record is given in the appendix. We validate the framework through 55 independent QPU executions on three IBM Heron-class processors (156 qubits, 𝑟2 revision): ibm_marrakesh, ibm_kingston and ibm_fez. Twenty-one of these form the Phase 3–6 programme, utilizing up to 76 actively measured qubits and achieving highly non-uniform output distributions in which the most probable outcome exceeds its uniform-distribution probability by factors of 3.44 × 10⁵ to 5.50 × 10⁶ at 50,000 shots; the remaining twenty-four test derived theorems directly against predictions computed and published before submission, including the oracle projection at 𝑁 = 6 to 12, where up to 82.1% of shots return an exact maximum…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.