32 citations · 32 across the 7 of their papers we have counts for
8 papers
On Certifying Source Sampling Hardness in Quantum Generative Modeling
Chen-Yu Liu, Leonardo Placidi, Enrico Rinaldi
Quantum generative models are often motivated by circuit families whose output distributions are believed to be classically hard to sample from. When such models are trained on ord…
Generative IQP Circuit Learning with Physics-Informed Latent Initialization
Chen-Yu Liu, Leonardo Placidi, Marco Ballarin +1
Quantum generative learning based on instantaneous quantum polynomial-time (IQP) circuits can benefit from efficient classical training strategies. A recent latent adaptation frame…
Learning to Prepare Molecular Ground States with Transformer Models
Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit +14
Quantum state preparation is a key component of many quantum algorithms. Performing this step efficiently is essential for realizing practical quantum advantage in quantum chemistr…
Efficiently Simulable Pauli Correlation Encoding
Daniele Lizzio Bosco, Gabriel Matos, Chen-Yu Liu +4
Pauli Correlation Encoding (PCE) is a heuristic framework for binary optimisation that encodes classical variables into many-body Pauli observables. While PCE requires fewer qubits…
The Impact of Qubit Connectivity on Quantum Advantage in Noisy IQP Circuits
Leonardo Placidi, Enrico Rinaldi, Keisuke Fujii +1
Instantaneous Quantum Polynomial-time (IQP) circuits are a candidate for demonstrating near-term quantum advantage, as their sampling task is believed to be classically hard in the…
Toward Generative Quantum Utility via Correlation-Complexity Map
Chen-Yu Liu, Leonardo Placidi, Eric Brunner +1
We study a practical question in generative quantum machine learning: given a classical dataset, can we determine, before training, whether it is well suited to a quantum generativ…
End-to-End Quantum Algorithms for the Jones Polynomial
Tuomas Laakkonen, Enrico Rinaldi, Chris N. Self +6
We present an end-to-end algorithmic pipeline where a noisy digital quantum computer is used to approximate the value of the Jones polynomial at the fifth root of unity for any inp…
The computational power of random quantum circuits in arbitrary geometries
Matthew DeCross, Reza Haghshenas, Minzhao Liu +49
Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimens…