Single-shot Quantum State Classification via Nonlinear Quantum Amplification

TL;DR

This paper demonstrates that nonlinear quantum amplification can improve single-shot quantum state classification by optimizing the entire measurement process for specific tasks, surpassing traditional linear approaches.

Contribution

It introduces a framework for using nonlinear quantum amplifiers in state discrimination, showing practical advantages and proposing a new qubit readout architecture optimized for this purpose.

Findings

Nonlinear amplification can enhance state discrimination fidelity.

Optimized end-to-end measurement improves classification performance.

Trade-offs between amplification regimes affect measurement accuracy.

Abstract

Quantum amplifiers are intrinsically nonlinear systems whose performance limits are set by quantum mechanics. In quantum measurement, amplifier operation is conventionally optimized in the linear regime by maximizing signal-to-noise ratio, an objective that is well-suited to parameter estimation but is typically insufficient for more general tasks such as arbitrary quantum state discrimination. Here we show that single-shot quantum state classification can benefit from operating a quantum amplifier outside the linear regime, when the measurement chain is optimized end-to-end for a task-specific cost function. We analyze a realistic superconducting readout architecture that includes state preparation, cryogenic nonlinear amplification, and room-temperature detection with finite noise. By introducing performance metrics tailored to state discrimination, we identify operating regimes in which nonlinear amplification provides a measurable advantage and clarify the trade-offs that ultimately limit classification fidelity. Building on these results, we propose a qubit readout architecture without cavity displacement that exploits nonlinear amplification to enhance single-shot state discrimination performance. Our results establish the practical value of nonlinear quantum amplifiers for quantum state discrimination and lay the foundation for a broader program to develop a general, end-to-end framework for resource-constrained optimization of nonlinear amplification in quantum information processing tasks.