Publication

Overhead-constrained circuit knitting for variational quantum dynamics

Related publications (31)

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

Amer Chemical Soc2024

Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution

Giuseppe Carleo, Filippo Vicentini, Clemens Giuliani, Alessandro Sinibaldi

We analyze the accuracy and sample complexity of variational Monte Carlo approaches to simulate the dynamics of many-body quantum systems classically. By systematically studying the relevant stochastic estimators, we are able to: (i) prove that the most us ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2023

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

Emergence of Classical Magnetic Order from Anderson Towers: Quantum Darwinism in Action

Frédéric Mila

Environment is assumed to play a negative role in quantum mechanics, destroying the coherence in a quantum system and, thus, randomly changing its state. However, for a quantum system that is initially in a degenerate ground state, the situation could be d ...

American Physical Society2023

A squeezed mechanical oscillator with millisecond quantum decoherence

Tobias Kippenberg, Amir Youssefi, Mahdi Chegnizadeh, Shingo Kono

An enduring challenge in constructing mechanical-oscillator-based hybrid quantum systems is to ensure engineered coupling to an auxiliary degree of freedom and maintain good mechanical isolation from the environment, that is, low quantum decoherence, consi ...

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

Time-resolved imaging with SPAD detectors

Jiuxuan Zhao

With the capabilities such as single-photon detection, time stamping and high-speed acquisition, time-resolved imaging based on single-photon avalanche diode (SPAD) detectors has found significant applications across diverse domains, including but not limi ...

EPFL2023

Erbium and thulium on MgO(100)/Ag(100) as candidates for single atom qubits

Harald Brune, François Patthey, Stefano Rusponi, Marina Pivetta, Edgar Fernandes, Fabio Donati, Aparajita Singha

Lanthanide atoms on surfaces are an exceptional platform for atomic-scale magnetic information storage. However, their potential as qubits remains unexplored due to the limited number of experimental setups that can coherently drive the spins of single ada ...

AMER PHYSICAL SOC2023

Nanomechanical resonators with low dissipation for quantum optomechanics

Alberto Beccari

Fields of technology as diverse as microwave filter construction, characterization of material interfaces with atomic precision, and detection of gravitational waves from astronomical sources employ mechanical resonators at their core. The utility of mecha ...

EPFL2023

Subtleties in the trainability of quantum machine learning models

A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, it ...

SPRINGERNATURE2023