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Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections
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Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections

--> Quantum Physics arXiv:2609.30283 (quant-ph) [Submitted on 3 Sep 2026] Title:Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections Authors:Ting Li, Zhiming Xiao, Qibiao Tang View a PDF of the paper titled Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections, by Ting Li and 2 other authors View PDF HTML (experimental) Abstract:Equivariant quantum networks encode symmetry as an inductive bias, which can improve generalization and may also favor optimization convergence. Equivariance alone, however, does not constrain the noncommuting closure of trainable generators, and this closure can still grow rapidly in symmetry-preserving variational circuits. We introduce a hierarchical ancilla-controlled architecture that addresses this Lie-algebra-growth mechanism. Commuting invariant-sector projectors on the data register select parameterized operations on a shared ancilla register, where the noncommuting trainable dynamics is confined. The trainable circuit decomposes into compatible joint sectors, giving a sector-probability-weighted ancilla response and an explicit view of parameter sharing across hierarchical paths. For an ancilla dimension $d_A=2^m$ and $K_\ell$ retained layer-wise control modes, we prove the group-independent bound $\dim(\mathfrak g)\le (d_A^2-1)\prod_{\ell=1}^{L}(K_\ell+1)$. The bound is polynomial in the number of data qubits when $m$ and $K_\ell$ remain constant along a logarithmic-depth hierarchy. Particle-number and parity projectors illustrate the general construction, while a fixed Clebsch--Gordan coupling tree supplies a concrete $SU(2)$ realization with rotation-invariant scalar outputs. Finite-size state-vector simulations exhibit slower gradient-variance decay and larger initialization gradients than generic and conventional rotationally equivariant circuits over the studied system sizes.

Sep 28, 2026

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IonQ Just Announced a Major Breakthrough. Should Investors Buy the Stock Now?
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IonQ Just Announced a Major Breakthrough. Should Investors Buy the Stock Now?

Quantum computers have a real problem. Small errors slip into calculations all the time, and catching and fixing them fast enough has been a real issue for the industry. On Tuesday, Sept. 22, IonQ (IONQ +1.11%) said it may have found a solution to this problem. ExpandNYSE: IONQIonQPremium FeatureMoneyball Superscore62/100Today's Change(1.11%) $0.50Current Price$45.48Key Data Points*:nth-last-child(-n+2)]:border-b-0">Market Cap$18BMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary.Day's Range$44.12 - $46.5552wk Range$25.89 - $84.64Volume27.6MAvg Vol21.3MGross Margin-3317.96% IonQ's statement said it has developed the industry's first end-to-end, real-time quantum error-correction decoder. Even better, this breakthrough runs on a single standard CPU. This gives IonQ a real edge in this highly competitive race. The day after the announcement, IonQ also said its Superion 256 will become the first on-premises quantum processor at Nvidia's (NVDA +0.22%) Accelerated Quantum Research Center. Image source: The Motley Fool. The stock price rose more than 11% on Wednesday, in line with the good news. IonQ remains a high-risk, speculative investment. In particular, the decoder was validated on simulated data rather than in a live system. How commercially viable quantum computing will be in the coming years remains to be seen. IonQ's valuation is also quite rich. It's deeply unprofitable as well. Investors interested in this space should recognize the sector's longer time horizon and volatility. Shares of IonQ have actually fallen more than 40% in the past year. For investors willing to stay invested for the next several years as this nascent sector finds its footing and use cases, this IonQ breakthrough is significant enough to consider buying the stock, in my opinion. Read NextSep 25, 2026 •By Parkev Tatevosian, CFAGreat News for IonQ Stock Investors!Sep 25, 2026

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Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Modelquantum-computing

Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model

--> Quantum Physics arXiv:2609.30496 (quant-ph) [Submitted on 24 Sep 2026] Title:Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model Authors:Karthikeya Machiraju, Krishna Sujith, Kaustav Bhowmick View a PDF of the paper titled Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model, by Karthikeya Machiraju and 2 other authors View PDF HTML (experimental) Abstract:Existing quantum simulations of the two-flavor Schwinger model have run at a single lattice size, and it is not known how far the variational approach can be pushed or which weakness stops it first. Following the model from N = 2 to 6 staggered lattice sites, we find that the binding constraint at reachable sizes is hardware noise rather than circuit expressibility or trainability, and identify N = 3 as the immediately viable extension of existing trapped-ion experiments. The energy error of a charge-conserving ansatz collapses onto one function of p/d, the ratio of variational parameters to physical-sector dimension, and falls by more than two orders of magnitude as p/d rises through order unity, giving the expressibility condition L(4N - 1) >= binom(2N,N) for L circuit layers. The condition is local in chemical potential: at N = 3 the layer count sufficient at zero chemical potential leaves a 74.38% error near the first-order boundary, while one further layer reaches 0.08%. Charge conservation also protects trainability and prevents charge-sector leakage: as the qubit count doubles from 4 to 8, the normalized gradient variance falls to 1/3.56 of its starting value for the constrained ansatz, versus 1/13.57 for an unconstrained circuit. Comparing a global contraction with per-gate local noise, a fixed-parameter control shows that the noise model, not whether the optimizer runs inside the noisy loop, sets how strongly noise degrades the fi

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Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networksquantum-computing

Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks

--> Quantum Physics arXiv:2609.30581 (quant-ph) [Submitted on 24 Sep 2026] Title:Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks Authors:Marcel Mordarski, Nathan Mani, Arshad Patel, William Knottenbelt, Roberto Bondesan View a PDF of the paper titled Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks, by Marcel Mordarski and 4 other authors View PDF HTML (experimental) Abstract:Encrypted training relies on keeping server-side updates low-degree. This constraint traditionally excludes models whose weights inhabit a compact Lie group (notably variational quantum circuits, where every trainable weight is an $\mathrm{SU(2)}$ rotation). Expressed in Euler angles or discrete alphabets, these updates appear transcendental, historically demanding prohibitive costs: one client--server round per gate, or upwards of $25{,}000$ operations per weight. This penalty is strictly an artefact of coordinates. In the unit-quaternion (spin) chart, group composition is exactly bilinear (degree two, with coefficients in $\{-1,0,+1\}$). Consequently, encrypted rotation updates cost one multiplicative level and federated averaging costs zero in any levelled homomorphic scheme, completely eliminating bootstrapping. This implementation-independent algebraic property is confirmed across two cryptographic backends, introducing only $0.0$ and $-2.0\times10^{-12}$ rad of aggregation error. Leveraging this reduction yields a non-interactive protocol for encrypted federated training of hybrid quantum--classical networks. It includes correctness proofs for aggregation and sign handling, plus a compilation lemma proving parameterised entanglers add only constant-factor overhead without altering the depth class. Empirically, a paired five-seed study confirms zero measurable utility tax ($\Delta=+9\times10^{-6}$ MSE, $p=0.92$), and a noise-budget ablation falsifies the hypothesis that encr

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