Publications related to Phenomenological theory of variational quantum ground-state preparation

Understanding generalization and robustness in modern deep learning

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Scalable Quantum Algorithms for Noisy Quantum Computers

Julien Sebastian Gacon

Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. Access to a computational space that incorporates quantum effects, such as superposition ...

EPFL2024

Quantum Protocols for ML, Physics, and Finance

Grzegorz Adam Gluch

In this thesis, we give new protocols that offer a quantum advantage for problems in ML, Physics, and Finance.Quantum mechanics gives predictions that are inconsistent with local realism.The experiment proving this fact (Bell, 1964) gives a quantum protoco ...

EPFL2024

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Friederike Metz

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems an ...

NATURE PORTFOLIO2023

Optimizing Quantum Compilers: Efficient and Effective Algorithms

Fereshte Mozafari Ghoraba

Quantum computing has made significant progress in recent years, with Google and IBM releasing quantum computers with 72 and 50 qubits, respectively. Google has also achieved quantum supremacy with its 54-qubit device, and IBM has announced the release of ...

EPFL2023

Quantum process tomography with unsupervised learning and tensor networks

Giuseppe Carleo

The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, r ...

NATURE PORTFOLIO2023

Scalable multi-chip quantum architectures enabled by cryogenic hybrid wireless/quantum-coherent network-in-package

Edoardo Charbon, Fabio Sebastiano

The grand challenge of scaling up quantum computers requires a full-stack architectural standpoint. In this position paper, we will present the vision of a new generation of scalable quantum computing architectures featuring distributed quantum cores (Qcor ...

IEEE2023

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

Variational dynamics as a ground-state problem on a quantum computer

Giuseppe Carleo, Stefano Barison, Filippo Vicentini

We propose a variational quantum algorithm to study the real-time dynamics of quantum systems as a ground -state problem. The method is based on the original proposal of Feynman and Kitaev to encode time into a register of auxiliary qubits. We prepare the ...

AMER PHYSICAL SOC2022

Phenomenological theory of variational quantum ground-state preparation

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Understanding generalization and robustness in modern deep learning

Scalable Quantum Algorithms for Noisy Quantum Computers

Quantum Protocols for ML, Physics, and Finance

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Optimizing Quantum Compilers: Efficient and Effective Algorithms

Quantum process tomography with unsupervised learning and tensor networks

Scalable multi-chip quantum architectures enabled by cryogenic hybrid wireless/quantum-coherent network-in-package

Deep Learning Generalization with Limited and Noisy Labels

Variational dynamics as a ground-state problem on a quantum computer

Variational dynamics as a ground-state problem on a quantum computer

Deep Learning Generalization with Limited and Noisy Labels

Quantum process tomography with unsupervised learning and tensor networks

Optimizing Quantum Compilers: Efficient and Effective Algorithms

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Quantum Protocols for ML, Physics, and Finance

Scalable Quantum Algorithms for Noisy Quantum Computers

Understanding generalization and robustness in modern deep learning

Scalable multi-chip quantum architectures enabled by cryogenic hybrid wireless/quantum-coherent network-in-package