Efficient state estimation on quantum processors
Victor Gonzalez Avella, Abraham Vega Vargas, Tomas Merlo Vergara, Kevin de la Ossa Doria, Jakub Czartowski, Dougal Main, Gabriel Araneda, Aldo Delgado, Dardo Goyeneche
TL;DR
This work addresses the challenge of scalable quantum-state tomography for multi-qubit processors by introducing two entanglement-free, post-processing-free approaches based on polarization identities. The first builds a five-basis scheme compatible with LOCC to yield an informationally complete reconstruction, while the second constructs a minimal set of $2n+1$ tensor-product bases requiring only local gates for measurement. Both provide explicit analytic reconstructions without solving large inverse problems, and their scalability is validated through IBM QPU experiments up to $n=12$ qubits and remote entangled trapped-ion experiments. The results demonstrate high fidelities and reveal trade-offs between shot resources and entangling-gate usage, offering practical tomography options for benchmarking large quantum processors and distributed quantum computing.
Abstract
We present two scalable and entanglement-free methods for estimating the collective state of an n-qubit quantum computer. The first method consists of a fixed set of five quantum circuits-regardless of the number of qubits-that avoid the use of entanglement as a measurement resource, relying instead on classical communication between selected pairs of qubits. The second method requires only 2n+1 circuits, each of which applies a single local gate to one of the n qubits during the measurement stage. Unlike traditional estimation methods, our approaches do not require any costly post-processing procedure to estimate a quantum state, enabling scalability to relatively large system sizes. We experimentally compare both methods on freely available IBM quantum processors, and observe how the state estimation varies with increasing number of qubits and shots. We further validated our results by estimating the 4-qubit entangled state of two remote ion-trap quantum processors, demonstrating that the optimized 2n+1 tomographic scheme achieves estimates consistent with standard methods while using exponentially fewer measurements.
