Quantum State Discrimination With Stabilizer Circuits
Benjamin Stratton, Santiago Zamora, Rafael Chaves
Abstract
The task of quantum state discrimination provides an operational characterization of distinguishability and plays a central role in quantum information science. Since the achievable success probability in quantum state discrimination depends both on the states being discriminated and on the allowed measurements, it is natural to study discrimination under physically motivated measurement constraints. Here, we investigate minimum-error quantum state discrimination under measurements implementable by both fixed and adaptive stabilizer circuits. Given arbitrary stabilizer-state ancillas, we show that fixed stabilizer circuits provide no additional discrimination power, whereas adaptive circuits do. Then, focusing on single-qubit systems, we derive analytical expressions for the success probability across both circuit classes when supplied with arbitrary (non-stabilizer) ancillas, showing that the performance gap between fixed and adaptive circuits persists. We further consider discrimination with non-stabilizerness incorporated directly into the measurement operators. Here, the optimization is formulated as a semidefinite program, and analytical bounds interpolating between the stabilizer and Helstrom limits for qubits are derived. Finally, we illustrate applications in quantum random access codes and bounds on unitary synthesis fidelity with a finite number of magic states.
Create a lesson
Related papers
Trading Circuit Depth for Pulse Sparsity in Chromatic Dynamical Decoupling
Amy F. Brown, Daniel A. Lidar
Optimal spectrum estimation
Ainesh Bakshi, Apoorv Vikram Singh, Xinyu Tan
Non-Abelian sheaf quantum LDPC codes: good and magical
Zimu Li, Fuchuan Wei, Zhengyi Han et al.
Learning and interpreting policies for simultaneous entanglement requests in quantum networks
Leon Rode, Sumeet Khatri, Supartha Podder
Sharp universal death of entanglement threshold for Pauli Hamiltonians
Bobak T. Kiani
Proper Agnostic Learning of Matrix Product States and Tree Tensor Networks
Constantin Cedillo Vayson de Pradenne, Jordan Cotler