806 citations · 5.4k across the 44 of their papers we have counts for
45 papers
Quantum Gaussian processes for prediction of channel observations
Jonas Jäger, Yaroslav Khmelnitskiy, Paolo Braccia +4
Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited numb…
Particle-preserving fermionic shadows with mode-independent sample complexity
Maxwell West, M. Cerezo, Martin Larocca
We consider the problem of learning expectation values of particle-preserving operators with respect to an unknown -particle -mode fermionic state via classical shadows. Our…
Exponentially many initializations to avoid barren plateaus
Ankit Kulshrestha, Ricard Puig, Diego García-Martín +4
Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure fo…
Provable and scalable quantum Gaussian processes for quantum learning
Jonas Jäger, Paolo Braccia, Pablo Bermejo +3
Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning framewor…
Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce -count
Daniele Lizzio Bosco, Lukasz Cincio, Giuseppe Serra +1
Compiling quantum circuits into Clifford+ gates is a central task for fault-tolerant quantum computing using stabilizer codes. In the near term, gates will dominate the cost…
Analyzing the quantum approximate optimization algorithm: ansätze, symmetries, and Lie algebras
Sujay Kazi, Martín Larocca, Marco Farinati +3
The Quantum Approximate Optimization Algorithm (QAOA) has been proposed as a method to obtain approximate solutions for combinatorial optimization tasks. In this work, we study the…
More buck-per-shot: Why learning trumps mitigation in noisy quantum sensing
Aroosa Ijaz, C. Huerta Alderete, Frédéric Sauvage +3
Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a c…
Quantum Convolutional Neural Networks are Effectively Classically Simulable
Pablo Bermejo, Paolo Braccia, Manuel S. Rudolph +3
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts.…
Exact spectral gaps of random one-dimensional quantum circuits
Andrew E. Deneris, Pablo Bermejo, Paolo Braccia +2
The spectral gap of local random quantum circuits is a fundamental property that determines how close the moments of the circuit's unitaries match those of a Haar random distributi…
Barren Plateaus in Variational Quantum Computing
Martin Larocca, Supanut Thanasilp, Samson Wang +7
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Platea…
Computing exact moments of local random quantum circuits via tensor networks
Paolo Braccia, Pablo Bermejo, Lukasz Cincio +1
A basic primitive in quantum information is the computation of the moments . These describe the distribution of expectation values obtained…
Symmetric derivatives of parametrized quantum circuits
David Wierichs, Richard D. P. East, Martín Larocca +2
Symmetries are crucial for tailoring parametrized quantum circuits to applications, due to their capability to capture the essence of physical systems. In this work, we shift the f…
Does provable absence of barren plateaus imply classical simulability?
M. Cerezo, Martin Larocca, Diego García-Martín +9
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and…
Showcasing a Barren Plateau Theory Beyond the Dynamical Lie Algebra
N. L. Diaz, Diego García-Martín, Sujay Kazi +2
Barren plateaus have emerged as a pivotal challenge for variational quantum computing. Our understanding of this phenomenon underwent a transformative shift with the recent introdu…
A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
Michael Ragone, Bojko N. Bakalov, Frédéric Sauvage +4
Variational quantum computing schemes train a loss function by sending an initial state through a parametrized quantum circuit, and measuring the expectation value of some operator…
Parallel-in-time quantum simulation via Page and Wootters quantum time
N. L. Diaz, Paolo Braccia, Martin Larocca +3
In the past few decades, researchers have created a veritable zoo of quantum algorithms by drawing inspiration from classical computing, information theory, and even from physical…
Lie-algebraic classical simulations for quantum computing
Matthew L. Goh, Martin Larocca, Lukasz Cincio +2
The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity, and in the development of quantum technologies. Efficient technique…
Quantum neural networks form Gaussian processes
Diego García-Martín, Martin Larocca, M. Cerezo
It is well known that artificial neural networks initialized from independent and identically distributed priors converge to Gaussian processes in the limit of a large number of ne…
Challenges and Opportunities in Quantum Machine Learning
M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang +2
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with app…
Effects of noise on the overparametrization of quantum neural networks
Diego García-Martín, Martin Larocca, M. Cerezo
Overparametrization is one of the most surprising and notorious phenomena in machine learning. Recently, there have been several efforts to study if, and how, Quantum Neural Networ…
Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
Louis Schatzki, Martin Larocca, Quynh T. Nguyen +2
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on qu…
Theory for Equivariant Quantum Neural Networks
Quynh T. Nguyen, Louis Schatzki, Paolo Braccia +5
Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakth…
Representation Theory for Geometric Quantum Machine Learning
Michael Ragone, Paolo Braccia, Quynh T. Nguyen +5
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importatio…
Building spatial symmetries into parameterized quantum circuits for faster training
Frederic Sauvage, Martin Larocca, Patrick J. Coles +1
Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates emplo…