4 citations · 6 across the 8 of their papers we have counts for
9 papers
Improved bounds on stabilizer extent and Clifford rank
Pulkit Sinha, Benjamin Lovitz
We prove that every pure state of stabilizer rank at most has stabilizer extent at most , and establish the analogous bound for the squared Clifford c…
Enhanced quantum capacity thresholds from symmetry
Avantika Agarwal, Amolak Ratan Kalra, Sungjai Lee +4
The quantum capacity captures the value of a quantum channel for transmitting quantum information, establishing the fundamental limits on quantum communication. In spite of its cen…
Nearly optimal algorithms to learn sparse quantum Hamiltonians in physically motivated distances
Amira Abbas, Nunzia Cerrato, Francisco Escudero Gutiérrez +3
We study the problem of learning Hamiltonians that are -sparse in the Pauli basis, given access to their time evolution. Although Hamiltonian learning has been extensively i…
Stabilizer Ranks, Barnes Wall Lattices and Magic Monotones
Amolak Ratan Kalra, Pulkit Sinha
In 2024, Kliuchnikov and Schönnenbeck showed a connection between the Barnes Wall lattices, stabilizer states and Clifford operations. In this work, we study their results and rela…
Dimension Independent and Computationally Efficient Shadow Tomography
Pulkit Sinha
We describe a new shadow tomography algorithm that uses samples, for measurements and additive error , which is independent of the dimension of the…
NP-hardness of testing equivalence to sparse polynomials and to constant-support polynomials
Omkar Baraskar, Agrim Dewan, Chandan Saha +1
An -sparse polynomial has at most monomials with nonzero coefficients. The Equivalence Testing problem for sparse polynomials (ETsparse) asks to decide if a given polynomial…
Phase error rate estimation in QKD with imperfect detectors
Devashish Tupkary, Shlok Nahar, Pulkit Sinha +1
We present a finite-size security proof of the decoy-state BB84 QKD protocol against coherent attacks, using entropic uncertainty relations, for imperfect detectors. We apply this…
Proper vs Improper Quantum PAC learning
Ashwin Nayak, Pulkit Sinha
A basic question in the PAC model of learning is whether proper learning is harder than improper learning. In the classical case, there are examples of concept classes with VC dime…
Optimal lower bounds for Quantum Learning via Information Theory
Shima Bab Hadiashar, Ashwin Nayak, Pulkit Sinha
Although a concept class may be learnt more efficiently using quantum samples as compared with classical samples in certain scenarios, Arunachalam and de Wolf (JMLR, 2018) proved t…