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1. Block mapping class groups and their finiteness properties

   https://doi.org/10.1007/s10711-025-00985-9  

   Aramayona, J; Aroca, J; Cumplido, M; Skipper, R; Wu, X (April 2025, Geometriae Dedicata)

   Abstract Given$$g \in \mathbb N \cup \{0, \infty \}$$ $g\in N\cup \{0,\infty \}$, let$$\Sigma _g$$ ${\Sigma }_g$denote the closed surface of genusgwith a Cantor set removed, if$$g<\infty $$ $g<\infty$; or the blooming Cantor tree, when$$g= \infty $$ $g=\infty$. We construct a family$$\mathfrak B(H)$$ $B\left(H\right)$of subgroups of$${{\,\textrm{Map}\,}}(\Sigma _g)$$ $\text{Map}\left({\Sigma }_g\right)$whose elements preserve ablock decompositionof$$\Sigma _g$$ ${\Sigma }_g$, andeventually like actlike an element ofH, whereHis a prescribed subgroup of the mapping class group of the block. The group$$\mathfrak B(H)$$ $B\left(H\right)$surjects onto an appropriate symmetric Thompson group of Farley–Hughes; in particular, it answers positively. Our main result asserts that$$\mathfrak B(H)$$ $B\left(H\right)$is of type$$F_n$$ $F_n$if and only ifHis. As a consequence, for every$$g\in \mathbb N \cup \{0, \infty \}$$ $g\in N\cup \{0,\infty \}$and every$$n\ge 1$$ $n\ge 1$, we construct a subgroup$$G <{{\,\textrm{Map}\,}}(\Sigma _g)$$ $G<\text{Map}\left({\Sigma }_g\right)$that is of type$$F_n$$ $F_n$but not of type$$F_{n+1}$$ $F_{n+1}$, and which contains the mapping class group of every compact surface of genus$$\le g$$ $\le g$and with non-empty boundary. 

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2. Measurement of multijet azimuthal correlations and determination of the strong coupling in proton-proton collisions at $$\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V} $$

   https://doi.org/10.1140/epjc/s10052-024-13116-7  

   Hayrapetyan, A; Tumasyan, A; Adam, W; Andrejkovic, J W; Bergauer, T; Chatterjee, S; Damanakis, K; Dragicevic, M; Hussain, P S; Jeitler, M; et al (August 2024, The European Physical Journal C)

   Abstract A measurement is presented of a ratio observable that provides a measure of the azimuthal correlations among jets with large transverse momentum$$p_{\textrm{T}}$$ $p_{\text{T}}$. This observable is measured in multijet events over the range of$$p_{\textrm{T}} = 360$$ $p_{\text{T}}=360$–$$3170\,\text {Ge}\hspace{-.08em}\text {V} $$ $3170\text{Ge}\text{V}$based on data collected by the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13$$\,\text {Te}\hspace{-.08em}\text {V}$$ $\text{Te}\text{V}$, corresponding to an integrated luminosity of 134$$\,\text {fb}^{-1}$$ ${\text{fb}}^{-1}$. The results are compared with predictions from Monte Carlo parton-shower event generator simulations, as well as with fixed-order perturbative quantum chromodynamics (pQCD) predictions at next-to-leading-order (NLO) accuracy obtained with different parton distribution functions (PDFs) and corrected for nonperturbative and electroweak effects. Data and theory agree within uncertainties. From the comparison of the measured observable with the pQCD prediction obtained with the NNPDF3.1 NLO PDFs, the strong coupling at the Z boson mass scale is$$\alpha _\textrm{S} (m_{{\textrm{Z}}}) =0.1177 \pm 0.0013\, \text {(exp)} _{-0.0073}^{+0.0116} \,\text {(theo)} = 0.1177_{-0.0074}^{+0.0117}$$ ${\alpha }_{\text{S}}\left(m_{\text{Z}}\right)=0.1177\pm 0.0013{\text{(exp)}}_{-0.0073}^{+0.0116}\text{(theo)}=0.{1177}_{-0.0074}^{+0.0117}$, where the total uncertainty is dominated by the scale dependence of the fixed-order predictions. A test of the running of$$\alpha _\textrm{S}$$ ${\alpha }_{\text{S}}$in the$$\,\text {Te}\hspace{-.08em}\text {V}$$ $\text{Te}\text{V}$region shows no deviation from the expected NLO pQCD behaviour. 

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3. Measurement of multidifferential cross sections for dijet production in proton–proton collisions at $$\sqrt{s} = 13\,\text {Te}\hspace{-.08em}\text {V} $$

   https://doi.org/10.1140/epjc/s10052-024-13606-8  

   Hayrapetyan, A; Tumasyan, A; Adam, W; Andrejkovic, J W; Bergauer, T; Chatterjee, S; Damanakis, K; Dragicevic, M; Valle, A_Escalante Del; Hussain, P S; et al (January 2025, The European Physical Journal C)

   Abstract A measurement of the dijet production cross section is reported based on proton–proton collision data collected in 2016 at$$\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V} $$ $\sqrt{s}=13\mathrm{Te}V$by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of up to 36.3$$\,\text {fb}^{-1}$$ ${\mathrm{fb}}^{-1}$. Jets are reconstructed with the anti-$$k_{\textrm{T}} $$ $k_{\text{T}}$algorithm for distance parameters of$$R=0.4$$ $R=0.4$and 0.8. Cross sections are measured double-differentially (2D) as a function of the largest absolute rapidity$$|y |_{\text {max}} $$ ${\left|y\right|}_{\max }$of the two jets with the highest transverse momenta$$p_{\textrm{T}}$$ $p_{\text{T}}$and their invariant mass$$m_{1,2} $$ $m_{1,2}$, and triple-differentially (3D) as a function of the rapidity separation$$y^{*} $$ $y^{\ast }$, the total boost$$y_{\text {b}} $$ $y_b$, and either$$m_{1,2} $$ $m_{1,2}$or the average$$p_{\textrm{T}}$$ $p_{\text{T}}$of the two jets. The cross sections are unfolded to correct for detector effects and are compared with fixed-order calculations derived at next-to-next-to-leading order in perturbative quantum chromodynamics. The impact of the measurements on the parton distribution functions and the strong coupling constant at the mass of the$${\text {Z}} $$ $Z$boson is investigated, yielding a value of$$\alpha _\textrm{S} (m_{{\text {Z}}}) =0.1179\pm 0.0019$$ ${\alpha }_{\text{S}}\left(m_Z\right)=0.1179\pm 0.0019$. 

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4. Characterizing Schwarz maps by tracial inequalities

   https://doi.org/10.1007/s11005-023-01636-4  

   Carlen, Eric; Müller-Hermes, Alexander (February 2023, Letters in Mathematical Physics)

   Abstract Let$$\phi $$ $\varphi$be a positive map from the$$n\times n$$ $n\times n$matrices$$\mathcal {M}_n$$ $M_n$to the$$m\times m$$ $m\times m$matrices$$\mathcal {M}_m$$ $M_m$. It is known that$$\phi $$ $\varphi$is 2-positive if and only if for all$$K\in \mathcal {M}_n$$ $K\in M_n$and all strictly positive$$X\in \mathcal {M}_n$$ $X\in M_n$,$$\phi (K^*X^{-1}K) \geqslant \phi (K)^*\phi (X)^{-1}\phi (K)$$ $\varphi \left(K^{\ast }{X}^{-1}K\right)⩾\varphi {\left(K\right)}^{\ast }\varphi {\left(X\right)}^{-1}\varphi \left(K\right)$. This inequality is not generally true if$$\phi $$ $\varphi$is merely a Schwarz map. We show that the corresponding tracial inequality$${{\,\textrm{Tr}\,}}[\phi (K^*X^{-1}K)] \geqslant {{\,\textrm{Tr}\,}}[\phi (K)^*\phi (X)^{-1}\phi (K)]$$ $\text{Tr}\left[\varphi \left(K^{\ast }{X}^{-1}K\right)\right]⩾\text{Tr}\left[\varphi {\left(K\right)}^{\ast }\varphi {\left(X\right)}^{-1}\varphi \left(K\right)\right]$holds for a wider class of positive maps that is specified here. We also comment on the connections of this inequality with various monotonicity statements that have found wide use in mathematical physics, and apply it, and a close relative, to obtain some new, definitive results. 

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5. Approximate Unitary t-Designs by Short Random Quantum Circuits Using Nearest-Neighbor and Long-Range Gates

   https://doi.org/10.1007/s00220-023-04675-z  

   Harrow, Aram W.; Mehraban, Saeed (May 2023, Communications in Mathematical Physics)

   Abstract We prove that$${{\,\textrm{poly}\,}}(t) \cdot n^{1/D}$$ $\text{poly}\left(t\right)·n^{1/D}$-depth local random quantum circuits with two qudit nearest-neighbor gates on aD-dimensional lattice withnqudits are approximatet-designs in various measures. These include the “monomial” measure, meaning that the monomials of a random circuit from this family have expectation close to the value that would result from the Haar measure. Previously, the best bound was$${{\,\textrm{poly}\,}}(t)\cdot n$$ $\text{poly}\left(t\right)·n$due to Brandão–Harrow–Horodecki (Commun Math Phys 346(2):397–434, 2016) for$$D=1$$ $D=1$. We also improve the “scrambling” and “decoupling” bounds for spatially local random circuits due to Brown and Fawzi (Scrambling speed of random quantum circuits, 2012). One consequence of our result is that assuming the polynomial hierarchy ($${{\,\mathrm{\textsf{PH}}\,}}$$ $\mathrm{PH}$) is infinite and that certain counting problems are$$\#{\textsf{P}}$$ $\#P$-hard “on average”, sampling within total variation distance from these circuits is hard for classical computers. Previously, exact sampling from the outputs of even constant-depth quantum circuits was known to be hard for classical computers under these assumptions. However the standard strategy for extending this hardness result to approximate sampling requires the quantum circuits to have a property called “anti-concentration”, meaning roughly that the output has near-maximal entropy. Unitary 2-designs have the desired anti-concentration property. Our result improves the required depth for this level of anti-concentration from linear depth to a sub-linear value, depending on the geometry of the interactions. This is relevant to a recent experiment by the Google Quantum AI group to perform such a sampling task with 53 qubits on a two-dimensional lattice (Arute in Nature 574(7779):505–510, 2019; Boixo et al. in Nate Phys 14(6):595–600, 2018) (and related experiments by USTC), and confirms their conjecture that$$O(\sqrt{n})$$ $O\left(\sqrt{n}\right)$depth suffices for anti-concentration. The proof is based on a previous construction oft-designs by Brandão et al. (2016), an analysis of how approximate designs behave under composition, and an extension of the quasi-orthogonality of permutation operators developed by Brandão et al. (2016). Different versions of the approximate design condition correspond to different norms, and part of our contribution is to introduce the norm corresponding to anti-concentration and to establish equivalence between these various norms for low-depth circuits. For random circuits with long-range gates, we use different methods to show that anti-concentration happens at circuit size$$O(n\ln ^2 n)$$ $O\left(n{ln}^{2}n\right)$corresponding to depth$$O(\ln ^3 n)$$ $O\left({ln}^{3}n\right)$. We also show a lower bound of$$\Omega (n \ln n)$$ $\Omega (nlnn)$for the size of such circuit in this case. We also prove that anti-concentration is possible in depth$$O(\ln n \ln \ln n)$$ $O(lnnlnlnn)$(size$$O(n \ln n \ln \ln n)$$ $O(nlnnlnlnn)$) using a different model. 

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