Jens Eisert

Cellular-automaton decoders for topological quantum memories

2015 · npj Quantum Information

We introduce a new framework for constructing topological quantum memories, by recasting error recovery as a dynamical process on a field generating cellular automaton. We envisage quantum systems controlled by a classical hardware composed of …

Reliable quantum certification of photonic state preparations

2015 · Nature Communications

Quantum technologies promise a variety of exciting applications. Even though impressive progress has been achieved recently, a major bottleneck currently is the lack of practical certification techniques. The challenge consists of ensuring that classically intractable …

An efficient quantum algorithm for spectral estimation

2017 · DSpace@MIT (Massachusetts Institute of Technology)

We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method, which determines the frequencies and damping factors of signals consisting of finite sums of exponentially …

Towards provably efficient quantum algorithms for large-scale machine-learning models

2023 · arXiv (Cornell University)

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum …

Shallow Shadows: Expectation Estimation Using Low-Depth Random Clifford Circuits

2024 · Physical Review Letters

We provide practical and powerful schemes for learning properties of a quantum state using a small number of measurements. Specifically, we present a randomized measurement scheme modulated by the depth of a random quantum circuit …

Stochastic noise can be helpful for variational quantum algorithms

2025 · Physical Review A

Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided, for example, by means of stochastic gradient descent methods. In this work, we provide evidence …