Roger G. Melko
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View article: RydbergGPT
RydbergGPT Open
We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hami…
View article: Neural network enhanced cross entropy benchmark for monitored circuits
Neural network enhanced cross entropy benchmark for monitored circuits Open
We explore the interplay of quantum computing and machine learning to advance experimental protocols for observing measurement-induced phase transitions (MIPTs) in quantum devices. In particular, we focus on trapped ion monitored circuits …
View article: Autoregressive Typical Thermal States
Autoregressive Typical Thermal States Open
A variety of generative neural networks recently adopted from machine learning have provided promising strategies for studying quantum matter. In particular, the success of autoregressive models in natural language processing has motivated…
View article: Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking
Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking Open
Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationships among these models remain largely unexplored, a gap that …
View article: Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice Open
Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations. A key direction has be…
View article: Probing Defects with Quantum Simulator Snapshots
Probing Defects with Quantum Simulator Snapshots Open
Snapshots, i.e. projective measurements of local degrees of freedom, are the most standard data taken in experiments on quantum simulators. Snapshots are usually used to probe local physics. In this work we propose a simple protocol to exp…
View article: Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions Open
View article: Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the triangular lattice
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the triangular lattice Open
Variational Monte Carlo simulations have been crucial for understanding quantum many-body systems, especially when the Hamiltonian is frustrated and the ground-state wavefunction has a non-trivial sign structure. In this paper, we use recu…
View article: Exploring the Energy Landscape of RBMs: Reciprocal Space Insights into Bosons, Hierarchical Learning and Symmetry Breaking
Exploring the Energy Landscape of RBMs: Reciprocal Space Insights into Bosons, Hierarchical Learning and Symmetry Breaking Open
Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationships among these models remain largely unexplored, a gap that …
View article: Beyond-classical computation in quantum simulation
Beyond-classical computation in quantum simulation Open
Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here,…
View article: Experimental Online Quantum Dots Charge Autotuning Using Neural Networks
Experimental Online Quantum Dots Charge Autotuning Using Neural Networks Open
Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a conv…
View article: Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the square lattice
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the square lattice Open
Machine-learning-based variational Monte Carlo simulations are a promising approach for targeting quantum many-body ground states, especially in two dimensions and in cases where the ground state is known to have a non-trivial sign structu…
View article: Autoregressive neural quantum states of Fermi Hubbard models
Autoregressive neural quantum states of Fermi Hubbard models Open
Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to th…
View article: The Economics of an Open-Source Quantum Computer
The Economics of an Open-Source Quantum Computer Open
Open-source projects that aim to make their offerings public have competed against for-profit, proprietary companies in a number of domains. These open-source projects often arise in response to the offerings of proprietary companies in ma…
View article: Neural network enhanced cross entropy benchmark for monitored circuits
Neural network enhanced cross entropy benchmark for monitored circuits Open
We explore the interplay of quantum computing and machine learning to advance experimental protocols for observing measurement-induced phase transitions (MIPT) in quantum devices. In particular, we focus on trapped ion monitored circuits a…
View article: Quantum-Assisted Generative AI for Simulation of the Calorimeter Response
Quantum-Assisted Generative AI for Simulation of the Calorimeter Response Open
As CERN approaches the launch of the High Luminosity Large Hadron Collider (HL-LHC) by the decade’s end, the computational demands of traditional simulations have become untenably high. Projections show millions of CPU-years required to cr…
View article: Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions Open
With the approach of the High Luminosity Large Hadron Collider (HL-LHC) era set to begin particle collisions by the end of this decade, it is evident that the computational demands of traditional collision simulation methods are becoming i…
View article: CaloQVAE: Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
CaloQVAE: Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models Open
View article: End-to-end variational quantum sensing
End-to-end variational quantum sensing Open
Harnessing quantum correlations can enable sensing beyond classical precision limits, with the realization of such sensors poised for transformative impacts across science and engineering. Real devices, however, face the accumulated impact…
View article: Artificial Intelligence for Quantum Computing
Artificial Intelligence for Quantum Computing Open
Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, i…
View article: Autoregressive neural quantum states of Fermi Hubbard models
Autoregressive neural quantum states of Fermi Hubbard models Open
Neural quantum states (NQS) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly-correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the…
View article: Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions Open
Particle collisions at accelerators such as the Large Hadron Collider, recorded and analyzed by experiments such as ATLAS and CMS, enable exquisite measurements of the Standard Model and searches for new phenomena. Simulations of collision…
View article: Robust quantum dots charge autotuning using neural network uncertainty
Robust quantum dots charge autotuning using neural network uncertainty Open
This study presents a machine learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This me…
View article: Experimental online quantum dots charge autotuning using neural networks
Experimental online quantum dots charge autotuning using neural networks Open
Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a conv…
View article: Autoregressive model path dependence near Ising criticality
Autoregressive model path dependence near Ising criticality Open
Autoregressive models are a class of generative model that probabilistically predict the next output of a sequence based on previous inputs. The autoregressive sequence is by definition one-dimensional (1D), which is natural for language t…
View article: GraphiQ: Quantum circuit design for photonic graph states
GraphiQ: Quantum circuit design for photonic graph states Open
is a versatile open-source framework for designing photonic graph state generation schemes, with a particular emphasis on photon-emitter hybrid circuits. Built in Python, GraphiQ consists of a suite of design tools, including multiple sim…
View article: Robust quantum dots charge autotuning using neural network uncertainty
Robust quantum dots charge autotuning using neural network uncertainty Open
This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This me…
View article: RydbergGPT
RydbergGPT Open
We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hami…
View article: Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice Open
Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations. A key direction has be…
View article: Stochastic series expansion quantum Monte Carlo for Rydberg arrays
Stochastic series expansion quantum Monte Carlo for Rydberg arrays Open
Arrays of Rydberg atoms are a powerful platform to realize strongly-interacting quantum many-body systems. A common Rydberg Hamiltonian is free of the sign problem, meaning that its equilibrium properties are amenable to efficient simulati…