Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus

TL;DR

This paper introduces dissipative quantum algorithms that use nonunitary dynamics and active reset mechanisms to avoid barren plateaus, enhancing scalability and noise resilience in quantum optimization.

Contribution

It demonstrates that dissipative algorithms can circumvent barren plateaus and remain trainable under noise, offering a scalable alternative to traditional variational quantum algorithms.

Findings

Dissipative algorithms avoid both unitary and noise-induced barren plateaus.

Active reset of ancillary qubits maintains gradient magnitudes.

Numerical simulations confirm robustness and scalability.

Abstract

Variational quantum algorithms (VQAs) have enabled a wide range of applications on near-term quantum devices. However, their scalability is fundamentally limited by barren plateaus, where the probability of encountering large gradients vanishes exponentially with system size. In addition, noise induces barren plateaus, deterministically flattening the cost landscape. Dissipative quantum algorithms that leverage nonunitary dynamics to prepare quantum states via engineered cooling offer a complementary framework with remarkable robustness to noise. We demonstrate that dissipative quantum algorithms based on non-unital channels can avoid both unitary and noise-induced barren plateaus. Periodically resetting ancillary qubits actively extracts entropy from the system, maintaining gradient magnitudes and enabling scalable optimization. We provide analytic conditions ensuring they remain trainable even in the presence of noise. Numerical simulations confirm our predictions and illustrate scenarios where unitary algorithms fail but dissipative algorithms succeed. Our framework positions dissipative quantum algorithms as a scalable, noise-resilient alternative to traditional VQAs.