Measurement-based quantum computation (MBQC) is a paradigm for quantum computation where computation is driven by local measurements on a suitably entangled resource state.
Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms.
This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm.
In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models.
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems.
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods.
The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices.
Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest.
We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.