5 papers
Random dimension reduction and learning symmetric properties of quantum states
Angus Lowe, Xinyu Tan
We introduce a procedure called random dimension reduction that simultaneously reduces the dimensions of many, potentially distinct quantum states while preserving properties invar…
Nearly tight bounds for testing tree tensor network states
Benjamin Lovitz, Angus Lowe
Tree tensor network states (TTNS) generalize the notion of having low Schmidt-rank to multipartite quantum states, through a parameter known as the bond dimension. This leads to su…
Randomized truncation of quantum states
Aram W. Harrow, Angus Lowe, Freek Witteveen
A fundamental task in quantum information is to approximate a pure quantum state in terms of sparse states or, for a bipartite system, states of bounded Schmidt rank. The optimal d…
Lower Bounds for Learning Quantum States with Single-Copy Measurements
Angus Lowe, Ashwin Nayak
We study the problems of quantum tomography and shadow tomography using measurements performed on individual, identical copies of an unknown -dimensional state. We first revisit…
Optimal quantum circuit cuts with application to clustered Hamiltonian simulation
Aram W. Harrow, Angus Lowe
We study methods to replace entangling operations with random local operations in a quantum computation, at the cost of increasing the number of required executions. First, we cons…