A Factorization Identity for Twisted Multinomial Coefficients with Application to Pilot States in Hamiltonian Decoded Quantum Interferometry
- URL: http://arxiv.org/abs/2604.01022v1
- Date: Wed, 01 Apr 2026 15:27:23 GMT
- Title: A Factorization Identity for Twisted Multinomial Coefficients with Application to Pilot States in Hamiltonian Decoded Quantum Interferometry
- Abstract summary: We introduce the twisted multinomial coefficient, in which each inversion between letters $i$ and $j$ carries a pair-dependent weight.<n>We show that the factorization yields an exact matrix product state (MPS) of bond dimension $k+1$ for the expansion coefficients of $hk$ in a twisted algebra.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The $q$-multinomial coefficient, a classical object in enumerative combinatorics, counts permutations of multisets weighted by the number of inversions, with a single deformation parameter $q$. We introduce the twisted multinomial coefficient, in which each inversion between letters $i$ and $j$ carries a pair-dependent weight $ω_{ij}$ determined by a skew-symmetric matrix $Ω$. In general, no closed-form evaluation is known. Our main result is that under a natural structural condition on $Ω$ - predecessor-uniformity ($ω_{ij} = q_j$ for all $i<j$) - the twisted multinomial factorizes as a product of Gaussian ($q$-deformed) binomials with site-dependent parameters: $\binom{k}{k_1,\ldots,k_m}_Ω= \prod_j\binom{\ell_j}{k_j}_{q_j}$. This extends the standard product formula for the $q$-multinomial from a single parameter $q$ to $m-1$ independent parameters. The identity is purely combinatorial: it holds for arbitrary $q_j \in \mathbb{C}\setminus\{0\}$ without any algebraic constraints. We were led to this identity by studying pilot state preparation in Hamiltonian Decoded Quantum Interferometry (HDQI), a recently proposed quantum algorithm for preparing Gibbs and ground states. As an application, we show that the factorization yields an exact matrix product state (MPS) of bond dimension $k+1$ for the expansion coefficients of $h^k$ in a twisted algebra. We note that this addresses only one component of the HDQI pipeline (pilot state preparation); the full protocol additionally requires efficient decoding of the associated Hamiltonian code, and both components must work in conjunction for Hamiltonians of physical interest. Identifying such Hamiltonians remains an important open problem.
Related papers
- Exact quantum splitting and the structure of finite algebras [1.7277199466514768]
Berlekamp's algorithm factors a squarefree $finmathbbF_q[x]$ by deterministic linear algebra.<n>We give an unconditional exact quantum implementation in a circuit model permitting single-qubit rotations through efficiently computable angles.
arXiv Detail & Related papers (2026-08-31T06:56:14Z) - Research Report on Noise-Shaped One-Bit Coefficients in Discrete Polynomial Fourier Extension [0.0]
This report studies noise-shaped one-bit coefficients in normalized discrete Fourier extension.<n>For first-order Sigma- quantization, the error is written as $e_k=u_k-q_k=v_k$ with a uniformly bounded state.<n>For the parabolic $_x,t()=x+t2$, the bound is expressed through $J(x,t)=int_01 |x+2t|d$, and the uniform $N-1$ rate is shown to be sharp.
arXiv Detail & Related papers (2026-07-26T21:51:41Z) - Two-Tower Quantum Matrix Chain Multiplication: Trading Qubits for Depth [73.08853228981701]
Matrix chain multiplication is a problem in scientific computing, machine learning, and graph analysis.<n>We present emphTwo-Tower Matrix, a quantum subroutine that encodes the product $mathcalW$ of the $K$ matrices into a quantum state in circuit depth.
arXiv Detail & Related papers (2026-07-14T18:42:40Z) - Simulation of Non-Hermitian Hamiltonians with Bivariate Quantum Signal Processing [0.0]
We achieve query-optimal quantum simulations of non-Hermitian Hamiltonians $H_mathrmeff = H_R + iH_I$, where $H_R$ is Hermitian and $H_I succeq 0$.
arXiv Detail & Related papers (2026-05-12T17:40:56Z) - Quantum analog-encoding for correlated Gaussian vectors and their exponentiation with application to rough volatility [0.0]
We propose quantum algorithms for the exact simulation of a normalised correlated Gaussian random vector.<n>Motivated by financial applications, we provide an end-to-end resource analysis.
arXiv Detail & Related papers (2026-04-24T11:29:09Z) - Power-Law Spectrum of the Random Feature Model [11.318593165494724]
We characterize the eigenvalues of the population random-feature covariance $mathbbE_x [frac1df(Wtop x )otimes 2]$.<n>For all $1 leq j leq d log-(p+1)(d)$, the $j$-th eigenvalue is of order $left(logp-1(j+1)/jright)$.
arXiv Detail & Related papers (2026-03-15T19:54:34Z) - Diffusion Computation versus Quantum Computation: A Comparative Model for Order Finding and Factoring [0.0]
We study a hybrid computational model for integer factorization in which the only non-classical resource is access to an emphiterated diffusion process on a finite graph.<n>Our comparison with Shor's algorithm is emphconceptual and model-based.<n>We report complexity in two cost measures: digital steps and diffusion steps.
arXiv Detail & Related papers (2026-01-05T19:45:38Z) - Hamiltonian Decoded Quantum Interferometry [69.7049555871155]
We introduce Hamiltonian Decoded Quantum Interferometry (HDQI)<n>HDQI utilizes coherent measurements and the symplectic representation of the Pauli group to reduce Gibbs sampling and Hamiltonian Bellians.<n>We show that HDQI efficiently prepares Gibbs states at arbitrary temperatures for a class of physically motivated commuting Hamiltonians.
arXiv Detail & Related papers (2025-10-09T08:06:15Z) - Efficient unitary designs and pseudorandom unitaries from permutations [35.66857288673615]
We show that products exponentiated sums of $S(N)$ permutations with random phases match the first $2Omega(n)$ moments of the Haar measure.
The heart of our proof is a conceptual connection between the large dimension (large-$N$) expansion in random matrix theory and the method.
arXiv Detail & Related papers (2024-04-25T17:08:34Z) - Average-Case Complexity of Tensor Decomposition for Low-Degree
Polynomials [93.59919600451487]
"Statistical-computational gaps" occur in many statistical inference tasks.
We consider a model for random order-3 decomposition where one component is slightly larger in norm than the rest.
We show that tensor entries can accurately estimate the largest component when $ll n3/2$ but fail to do so when $rgg n3/2$.
arXiv Detail & Related papers (2022-11-10T00:40:37Z) - Near-optimal fitting of ellipsoids to random points [68.12685213894112]
A basic problem of fitting an ellipsoid to random points has connections to low-rank matrix decompositions, independent component analysis, and principal component analysis.
We resolve this conjecture up to logarithmic factors by constructing a fitting ellipsoid for some $n = Omega(, d2/mathrmpolylog(d),)$.
Our proof demonstrates feasibility of the least squares construction of Saunderson et al. using a convenient decomposition of a certain non-standard random matrix.
arXiv Detail & Related papers (2022-08-19T18:00:34Z) - Towards Antisymmetric Neural Ansatz Separation [48.80300074254758]
We study separations between two fundamental models of antisymmetric functions, that is, functions $f$ of the form $f(x_sigma(1), ldots, x_sigma(N))
These arise in the context of quantum chemistry, and are the basic modeling tool for wavefunctions of Fermionic systems.
arXiv Detail & Related papers (2022-08-05T16:35:24Z) - Stochastic behavior of outcome of Schur-Weyl duality measurement [45.41082277680607]
We focus on the measurement defined by the decomposition based on Schur-Weyl duality on $n$ qubits.
We derive various types of distribution including a kind of central limit when $n$ goes to infinity.
arXiv Detail & Related papers (2021-04-26T15:03:08Z) - Robustly Learning any Clusterable Mixture of Gaussians [55.41573600814391]
We study the efficient learnability of high-dimensional Gaussian mixtures in the adversarial-robust setting.
We provide an algorithm that learns the components of an $epsilon$-corrupted $k$-mixture within information theoretically near-optimal error proofs of $tildeO(epsilon)$.
Our main technical contribution is a new robust identifiability proof clusters from a Gaussian mixture, which can be captured by the constant-degree Sum of Squares proof system.
arXiv Detail & Related papers (2020-05-13T16:44:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.