We present, to our knowledge, the first adaptation of Pauli Correlation Encoding (PCE) to quantum topological data analysis, reformulating Betti number estimation as a depth-efficient variational optimization over a compressed qubit register. From a Takens embedding and Vietoris--Rips filtration of S&P~500 returns, we extract combinatorial Laplacians and recast null-space counting as a continuous-PCE Rayleigh-quotient minimization with variational deflation, encoding $n_k$ simplex indices into $O(n_k^{1/\kappa})$ qubits with shallow, ancilla-free circuits. Because the resulting loss is rational rather than bilinear in the correlators, the barren-plateau bound of~\cite{Sciorilli25} does not transfer; empirically the gradient variance decays only polynomially, with no exponential barren plateau, over $n=4$--$12$ qubits. The classical stage matches ripser~\cite{bauer2021ripser} on all 190 sliding windows (2007-2009). On the real market Laplacians ($\beta_1=1$--$22$), warm-starting from a classical null-space surrogate allows PCE-VQE to recover $\beta_1$ exactly at every scale, placing the obstacle in the optimisation landscape rather than the encoding. Chronologically split classification gives in-regime ROC AUC $0.818$, but out-of-distribution evaluation on the 2020 COVID shock and 2022 rate cycle (AUC $0.009$, $0.515$) shows the calibration does not generalize across crisis regimes.
Deep parameterized quantum circuits may remain sensitive to a parameter change while the observables retained by a learning model barely respond. We study this separation for a fixed computational-basis measurement. For a pure-state tangent, we compare the quantum Fisher information $F_Q$, the Fisher information $F_{\rm full}$ in the complete bitstring distribution, and the largest variance-normalized response $\mathcal I_{\mathcal A}$ available to a diagonal readout space $\mathcal A$. If the joint state--tangent frame is Haar random, we prove that the two successive information fractions are independent Beta variables whose means are $1/2$ and $r/(2^n-1)$, where $r$ is the centered dimension of the readout. Consequently, even the joint span of all computational-basis Pauli strings through any fixed weight $k$ retain only $O(n^k2^{-n})$ of the full-record information. Exact-statevector experiments across six circuit families show increasing finite-size agreement with this hierarchy in five nonconserving ensembles as the circuit depth grows. A number-conserving family departs strongly from the isotropic prediction even after correcting the support and readout rank, showing that rank alone is insufficient without tangent isotropy.
In odd local dimension $D$, complete Wigner positivity yields stochastic phase-space dynamics on Wigner-nonnegative states but does not control signed inputs, entanglement across channel uses, or collective decoding. Using a subsystem-resolved Weyl decomposition, we characterize the equality conditions of the tensor-stable output-purity bound. Cyclic difference sets are precisely the uniform shift supports saturating the universal Parseval lower bound on the worst nontrivial collision mode. For factorized shift--phase noise with shift support $R$, $|R|=r$, and phase distribution $h$ of no larger collision radius, we obtain $S_{\alpha,\min}(\Phi_{R,h}^{\otimes n})=n\log_2 r$ for all $n\ge1$ and $0\le\alpha\le2$, the unrestricted capacity $C=\log_2(D/r)$, and a finite-blocklength strong converse. For a balanced bi-difference-set interpolation $h_\varepsilon=(1-\varepsilon)q_H+\varepsilon u_D$, the unassisted capacity is constant for $0\le\varepsilon\le1$, while the Choi state is NPT for every $\varepsilon<1$ and becomes entanglement breaking at $\varepsilon=1$. At $\varepsilon=0$, all tensor-power minimum-output states are products with local factors in one of two mutually unbiased Weyl bases; for $\varepsilon>0$, only the computational basis remains. For Singer parameters $D=q^2+q+1$ and $r=q+1$, $C_{\mathrm E}/C\to2-\varepsilon$. Finally, for an identity--dephasing profile we determine the exact tensor-power collision-entropy phase diagram, derive rigorous capacity bounds, and isolate a distinct von Neumann crossover, with a tensor-power R'enyi conjecture supported by numerics. Thus complete Wigner positivity can coexist with persistent channel entanglement and a scalable entanglement-assisted advantage.
Qubit-efficient quantum relaxations compress classical decision variables into expectation values on substantially fewer qubits. We ask what resource tradeoffs this compression entails for quantum optimization. For the complete quadratic-Majorana encoding on $n$ qubits, pairwise correlators can represent $m=\Theta(n^2)$ binary variables. We define the universal margin as the smallest correlator magnitude that can be guaranteed with prescribed signs for every target sign assignment. We show that it is exactly $\Delta_{\rm Maj}(n)=\tan\!\left(\frac{\pi}{4n}\right)=\Theta(1/n)$, whereas uniformly random sign assignments retain $\Theta(1/\sqrt n)$ target-specific margins. The stronger $1/n$ worst-case scaling is Majorana-specific. Moreover, arbitrary density operators and fermionic Gaussian states generate the same quadratic-Majorana covariance body, so non-Gaussian state resources cannot enlarge this two-point relaxation. Beyond Majoranas, standard quantum random access code bounds provide general information-theoretic baselines. For any fixed family of $m$ designated binary observables on $n$ qubits, the universal margin is at most $\sqrt{(2\ln2\;n/m)}$, while arbitrary random access decoding from $N$ copies with constant success probability above $1/2$ requires $nN=\Omega(m)$. For a fixed Pauli correlation encoding required to work uniformly over all targets, maintaining a fixed nonzero decoded magnitude under smooth sign decoding therefore requires a rescaling parameter that grows as the available margin shrinks. Thus, while providing substantial qubit savings, compression can shift cost into restricted expectation value geometry, smaller expectation value magnitudes, or more demanding information recovery rather than eliminate it.
This work reveals and exploits this underexplored robustness property: how much non-Clifford and variational expressivity can be removed from the sampling circuit before SQD accuracy degrades, and answers through two complementary compression techniques: gradient-based operator pruning, which discards low-impact excitation operators, and Clifford rounding, which snaps remaining parameters to the nearest Clifford angle.
Kangyu Zheng, Yidong Zhou, Jinglei Cheng et al.· 0 citations
Simulating real-time dynamics of quantum systems is often limited to short times by entanglement growth. Finite-pole reconstructions such as linear prediction and related machineries extrapolate such data reliably when the spectrum is a finite set of excitations, but at criticality the low-energy spectrum is a power-law continuum $A(\omega)\sim|\omega|^{\alpha-1}$ -- a branch cut whose real-time tail $G(t)\sim t^{-\alpha}$ finitely many poles cannot represent. Here we develop a fractional-calculus-motivated envelope extrapolation for such data. Its structure is motivated by a fractional form of Schwinger--Dyson (fSD) equation, in which the Laplace symbol $s^{\alpha}$ carries the branch cut analytically while the residual self-energy remains meromorphic. On real data we employ the corresponding operational alternative -- the exponent $\alpha$ is selected blindly inside the fit window, the signal is detrended by $t^{\alpha}$, the residual is fitted by a stabilized finite-pole model, and the algebraic envelope is restored. On the critical XXZ chain this blind fractional-envelope method (fSD for short) extrapolates short-time data typically several-fold more accurately than finite-pole methods, with the exponent $\alpha$ identified blindly from the fit window alone and bracketing the closed-form Luttinger value at weak coupling. The same blind search finds the $z=2$ dilute-magnon exponent $\alpha=1/2$ at the $\Delta=1$ saturation transition, and the advantage persists in the gapped free-magnon phase with its sharp band edges. On noncritical dynamical mean-field spectra, whose low-frequency response is regular, fSD by contrast fails to select any stable fractional envelope and reduces to the standard pole result rather than manufacturing a spurious power law, making it an efficient and accurate route to quantum critical dynamics when only short simulation times are accessible.
Hyun-Jeong Kim, Hyun-Yong Lee, Heung-Sik Kim· 0 citations
The dynamical Lie algebra (DLA) governs the controllability, expressibility, and simulation complexity of a quantum system. Explicitly computing it has been a major computational bottleneck: brute-force Lie closure scales exponentially in the number of qubits $n$. Many applications, however, consult only the isomorphism type of the DLA. We introduce PauLie, an open-source framework that decides this isomorphism type for DLAs generated by arbitrary Pauli strings, building on the anticommutation-graph reduction of Aguilar et al. PauLie runs in $O(n|\mathcal{G}|\max(n,|\mathcal{G}|))$ time, where $|\mathcal{G}|$ is the number of generators, turning DLA classification into a routine preprocessing step. We demonstrate its use as a structural oracle for routing Lie-algebraic simulation and Cartan decomposition, diagnosing barren plateaus in variational quantum algorithms, and engineering universal Pauli string generator sets with optimal generation rate.
Oxana Shaya, Konstantin Golovkin, Mainak Roy et al.· 0 citations