QTML 2023: 7TH INTERNATIONAL CONFERENCE ON QUANTUM TECHINQUES IN MACHINE LEARNING

This page contains an index consisting of author-provided keywords.

A
Adaptive agents	Quantum adaptive agents with efficient long-term memories
Adversarial Learning	Quantum machine learning with enhanced adversarial robustness
Adversarial robustness	Training robust quantum classifiers based on Lipschitz bounds
Agnostic PAC Learning of Boolean functions	Classical Verification of Quantum Learning
anomaly detection	Quantum anomaly detection in the latent space of proton collision events at the LHC
B
Backpropagation	On quantum backpropagation, information reuse, and cheating measurement collapse
barren plateau	QResNet: a variational entanglement skipping algorithm
Barren Plateaus	The landscape of QAOA Max-Cut Lie algebras On the Absence of Barren Plateaus in Quantum Generative Adversarial Networks
Bayesian Inference	Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems
Betti curve	A Topological Features Based Quantum Kernel
Betti number	A Topological Features Based Quantum Kernel
Binary neural networks	Quantum optimization of Binary Neural Networks
Block encoding	Next Generation Quantum Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
C
Chaotic Systems	Hybrid quantum-classical reservoir computing for solving chaotic systems
circuit cutting	Simulating dynamics of large quantum systems on small quantum devices using circuit knitting
circuit knitting	Simulating dynamics of large quantum systems on small quantum devices using circuit knitting
Circuit Optimization	Applying Genetic Algorithms to Optimize the Generalization Ability of Variational Quantum Circuits
classical shadow	Efficient information recovery from Pauli noise via classical shadow
Classical shadows	Post-Variational Quantum Neural Networks Neural–Shadow Quantum State Tomography The power and limitations of learning quantum dynamics incoherently Shadows of quantum machine learning
Classical simulation	Classical simulations of noisy variational quantum circuits
classification	Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform
Clifford circuits	Learning t-doped stabilizer states
Combinatorial Optimization	On The Performance And Limitation Of Quantum Approximate Optimization
Combinatorial Optimization Problems	Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants
Complex systems	Quantum adaptive agents with efficient long-term memories
Complexity theory	Exponential separations between classical and quantum learners
computational fluid mechanics	Complete quantum-inspired framework for simulations of flows past immersed bodies
Computational learning theory	Exponential separations between classical and quantum learners
constant-depth circuits	Constant-depth circuits for Uniformly Controlled Gates and Boolean functions with application to quantum memory circuits
continuous variable quantum information	Quantum Similarity Testing with Convolutional Neural Networks
D
Data encoding and processing in quantum systems	Constant-depth circuits for Uniformly Controlled Gates and Boolean functions with application to quantum memory circuits
Data re-uploading	Training robust quantum classifiers based on Lipschitz bounds
Decentralized computing	Non-IID Quantum Federated Learning with One-shot Communication Complexity
Dimension Reduction	Dimension reduction in quantum stochastic modelling
Dropout	A General Approach for Dropout in Quantum Neural Networks
E
efficient tomography	Learning t-doped stabilizer states
Efficient-circuit design	Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
Entangled data	Transition role of entangled data in quantum machine learning
entanglement	QResNet: a variational entanglement skipping algorithm
Entanglement characteristics	Explainable Representation Learning of Small Quantum States
epsilon graphs	Quantum Distance Calculation for ε-Graph Construction
Equivariance	Equivariant Quantum Models
error mitigation	Efficient information recovery from Pauli noise via classical shadow
Evolutionary search	Hierarchical quantum circuit representations for neural architecture search
Experiment	Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor
Explainable AI	Explainable Representation Learning of Small Quantum States
Exponential quantum advantage	Exponential separations between classical and quantum learners
Expressivity	Expressivity and Generalization Ability of Trace-induced Quantum Kernels
F
feature encoding	Let Quantum Neural Networks Choose Their Own Frequencies
feature map	Let Quantum Neural Networks Choose Their Own Frequencies
Federated Learning	Non-IID Quantum Federated Learning with One-shot Communication Complexity
Fokker--Planck equation	Gibbs Sampling of Periodic Potentials on a Quantum Computer
Forecasting quantum dynamics	Next Generation Quantum Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
Fourier analysis of Boolean functions	Classical Verification of Quantum Learning
Fourier Neural Operator	Quantum Fourier Networks for Solving Parametric PDEs
G
generalization	Understanding quantum machine learning also requires rethinking generalization Understanding generalization with quantum geometry Expressivity and Generalization Ability of Trace-induced Quantum Kernels
generalization bounds	Understanding quantum machine learning also requires rethinking generalization
Generative modeling	Trainability barriers and opportunities in quantum generative modeling
Generative modelling	Neural–Shadow Quantum State Tomography On the Sample Complexity of Quantum Boltzmann Machine Learning Gibbs Sampling of Periodic Potentials on a Quantum Computer
Genetic Algorithms	Applying Genetic Algorithms to Optimize the Generalization Ability of Variational Quantum Circuits
Geometric quantum machine learning	Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images Homogenous space expressibility of parametrized quantum circuits Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data Symmetry-invariant quantum machine learning force fields
Gibbs sampling	Gibbs Sampling of Periodic Potentials on a Quantum Computer
gibbs state preparation	Variational Quantum Time Evolution without the Quantum Geometric Tensor
Gibbs states	On the Sample Complexity of Quantum Boltzmann Machine Learning
Graph machine learning	Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor
Graph neural networks	Extending Graph Transformers with Quantum Computed Aggregation
Gravitational wave	Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration
H
Haar integration	Homogenous space expressibility of parametrized quantum circuits
high energy physics	Quantum anomaly detection in the latent space of proton collision events at the LHC
high-energy physics	Quantum data learning for quantum simulations in high-energy physics
hybrid algorithm	Simulating dynamics of large quantum systems on small quantum devices using circuit knitting
Hybrid Quantum Computing	Post-Variational Quantum Neural Networks
hypothesis testing	Learning bounds and guarantees for testing (quantum) hypotheses
I
Image Analysis	Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images
Incoherent learning	The power and limitations of learning quantum dynamics incoherently
Inductive bias	Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
information recovery	Efficient information recovery from Pauli noise via classical shadow
information theory	Learning bounds and guarantees for testing (quantum) hypotheses
Interactive verification of Machine Learning	Classical Verification of Quantum Learning
Interpretable models	Explainable Representation Learning of Small Quantum States
K
Kalman filter	Machine learning continuously monitored systems: estimating parameters
L
landscape analysis	Analyzing variational quantum landscapes with information content
Langevin dynamics	Gibbs Sampling of Periodic Potentials on a Quantum Computer
lattice gauge theory	Quantum data learning for quantum simulations in high-energy physics
learning	Learning t-doped stabilizer states
Learning separations	Shadows of quantum machine learning
learning theory	Learning bounds and guarantees for testing (quantum) hypotheses
Lie algebras	The landscape of QAOA Max-Cut Lie algebras Here comes the SU(N) multivariate quantum gates and gradients
M
Machine Learning	Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems Dimension reduction in quantum stochastic modelling Quantum Similarity Testing with Convolutional Neural Networks Quantum Fourier Networks for Solving Parametric PDEs
Markov chain Monte Carlo method	Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration
Material simulation	Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding
Matrix Multiplicative Weight Updates	Quadratic Speedup in Quantum Zero-Sum Games via Single-Call Mirror-Prox Matrix Methods
matrix product states	Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks Complete quantum-inspired framework for simulations of flows past immersed bodies Quantum optimization of Binary Neural Networks
maxcut	The landscape of QAOA Max-Cut Lie algebras
measurement protocols	Time-series quantum reservoir computing with weak and projective measurements
minimax bounds	Learning bounds and guarantees for testing (quantum) hypotheses
Mirror Prox	Quadratic Speedup in Quantum Zero-Sum Games via Single-Call Mirror-Prox Matrix Methods
Model Training and Scaling	On the Absence of Barren Plateaus in Quantum Generative Adversarial Networks
molecular dynamics	Symmetry-invariant quantum machine learning force fields
Multi-Class Classification	A Multi-Class Quantum Kernel-Based Classifier
Multiqubit noise deconvolution	Channel tomography for quantum noise characterization and mitigation
N
Nash Equilibrium	Quadratic Speedup in Quantum Zero-Sum Games via Single-Call Mirror-Prox Matrix Methods
Neural architecture search (NAS)	Hierarchical quantum circuit representations for neural architecture search
neural network	Quantum Similarity Testing with Convolutional Neural Networks
Neural network quantum states	Neural–Shadow Quantum State Tomography
Neural Networks	Quantum Fourier Networks for Solving Parametric PDEs
Neutral atoms	Extending Graph Transformers with Quantum Computed Aggregation Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor
NISQ	Simulating dynamics of large quantum systems on small quantum devices using circuit knitting Post-Variational Quantum Neural Networks
NISQ Algorithms	Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants
O
Online learning	Quantum algorithm for robust optimization via stochastic-gradient online learning
oped Clifford circuits	Learning t-doped stabilizer states
optimization	Analyzing variational quantum landscapes with information content
Overfitting	A General Approach for Dropout in Quantum Neural Networks
overparameterization	Understanding generalization with quantum geometry
P
Parameter Estimation	Machine learning continuously monitored systems: estimating parameters Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems
parameterized quantum circuits	Let Quantum Neural Networks Choose Their Own Frequencies
Parametrized quantum circuits	Training robust quantum classifiers based on Lipschitz bounds
partial differential equations	Let Quantum Neural Networks Choose Their Own Frequencies Gibbs Sampling of Periodic Potentials on a Quantum Computer Quantum Fourier Networks for Solving Parametric PDEs
Pauli transfer matrix reconstruction	Channel tomography for quantum noise characterization and mitigation
Performance metrics	Homogenous space expressibility of parametrized quantum circuits
photonic quantum computing	Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform
Photonic System	Dimension reduction in quantum stochastic modelling
Post-Variational Method	Post-Variational Quantum Neural Networks
precomputation	Quantum models and data through a precomputation lens
Pretraining	On the Sample Complexity of Quantum Boltzmann Machine Learning
Q
QAOA	The landscape of QAOA Max-Cut Lie algebras Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants
QGANss	Quadratic Speedup in Quantum Zero-Sum Games via Single-Call Mirror-Prox Matrix Methods
QN-SPSA	Quantum optimization of Binary Neural Networks
QPU Runs	Hybrid quantum-classical reservoir computing for solving chaotic systems
QRAM	Constant-depth circuits for Uniformly Controlled Gates and Boolean functions with application to quantum memory circuits
Quantum advantage	Quantum adaptive agents with efficient long-term memories
Quantum agents	Quantum adaptive agents with efficient long-term memories
Quantum algorithm	Quantum algorithm for robust optimization via stochastic-gradient online learning Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration
quantum algorithms	Quantum Distance Calculation for ε-Graph Construction Variational Quantum Time Evolution without the Quantum Geometric Tensor
Quantum Boltzmann Machines	On the Sample Complexity of Quantum Boltzmann Machine Learning
quantum channel learning	Efficient information recovery from Pauli noise via classical shadow
Quantum circuit generative models	Trainability barriers and opportunities in quantum generative modeling
Quantum circuits	Hybrid quantum-classical reservoir computing for solving chaotic systems Hierarchical quantum circuit representations for neural architecture search Trainability barriers and opportunities in quantum generative modeling
quantum computation	Quantum models and data through a precomputation lens
quantum computing	Variational Quantum Time Evolution without the Quantum Geometric Tensor Quantum Fourier Networks for Solving Parametric PDEs
quantum continuous monitoring	Machine learning continuously monitored systems: estimating parameters
quantum control	Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
quantum convolutional neural network	Detection of quantum phase transitions with quantum machine learning techniques Understanding quantum machine learning also requires rethinking generalization Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
Quantum convolutional neural network (QCNN)	Hierarchical quantum circuit representations for neural architecture search
Quantum Correlations	Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems
quantum data	Quantum models and data through a precomputation lens
Quantum Deep Learning	Quantum Fourier Networks for Solving Parametric PDEs
quantum differentiation	Let Quantum Neural Networks Choose Their Own Frequencies
quantum dynamics	Simulating dynamics of large quantum systems on small quantum devices using circuit knitting
Quantum Embedding theory	Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding
Quantum error mitigation	Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
Quantum feature mapping	Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
quantum Fisher information metric	Understanding generalization with quantum geometry
quantum gates	Here comes the SU(N) multivariate quantum gates and gradients
Quantum Generative Adversarial Networks	On the Absence of Barren Plateaus in Quantum Generative Adversarial Networks
Quantum information	Learning bounds and guarantees for testing (quantum) hypotheses Quantum adaptive agents with efficient long-term memories
Quantum kernel	A Topological Features Based Quantum Kernel
Quantum Kernel Methods	A Multi-Class Quantum Kernel-Based Classifier
Quantum Kernels	Expressivity and Generalization Ability of Trace-induced Quantum Kernels
Quantum learning theory	The power and limitations of learning quantum dynamics incoherently Transition role of entangled data in quantum machine learning Classical Verification of Quantum Learning Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
Quantum linear system algorithm	Gibbs Sampling of Periodic Potentials on a Quantum Computer
Quantum machine learning	Equivariant Quantum Models Quantum anomaly detection in the latent space of proton collision events at the LHC Quantum data learning for quantum simulations in high-energy physics Applying Genetic Algorithms to Optimize the Generalization Ability of Variational Quantum Circuits Understanding quantum machine learning also requires rethinking generalization Let Quantum Neural Networks Choose Their Own Frequencies A Topological Features Based Quantum Kernel Non-IID Quantum Federated Learning with One-shot Communication Complexity Exponential separations between classical and quantum learners Understanding generalization with quantum geometry Variational Quantum Time Evolution without the Quantum Geometric Tensor Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning Hybrid quantum-classical reservoir computing for solving chaotic systems Time-series quantum reservoir computing with weak and projective measurements Quantum machine learning with enhanced adversarial robustness Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform Quantum models and data through a precomputation lens Trainability barriers and opportunities in quantum generative modeling Deep quantum neural networks form Gaussian processes Shadows of quantum machine learning Transition role of entangled data in quantum machine learning On quantum backpropagation, information reuse, and cheating measurement collapse On the Absence of Barren Plateaus in Quantum Generative Adversarial Networks Training robust quantum classifiers based on Lipschitz bounds On the Sample Complexity of Quantum Boltzmann Machine Learning Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification Quantum Fourier Networks for Solving Parametric PDEs
Quantum memory	Quantum adaptive agents with efficient long-term memories Constant-depth circuits for Uniformly Controlled Gates and Boolean functions with application to quantum memory circuits
Quantum Metrology	Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems
quantum model	Let Quantum Neural Networks Choose Their Own Frequencies
Quantum Monte Carlo	Quantum Computing Quantum Monte Carlo
Quantum Neural Network	Let Quantum Neural Networks Choose Their Own Frequencies Quantum machine learning with enhanced adversarial robustness
Quantum Neural Networks	Post-Variational Quantum Neural Networks A General Approach for Dropout in Quantum Neural Networks Deep quantum neural networks form Gaussian processes Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
Quantum no-free-lunch theorem	Transition role of entangled data in quantum machine learning
Quantum noise	Classical simulations of noisy variational quantum circuits Efficient information recovery from Pauli noise via classical shadow
Quantum noise mitigation	Channel tomography for quantum noise characterization and mitigation
Quantum Optimization	Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants
Quantum phase transition	Detection of quantum phase transitions with quantum machine learning techniques
Quantum process tomography	Channel tomography for quantum noise characterization and mitigation
Quantum reservoir computing	Next Generation Quantum Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics Time-series quantum reservoir computing with weak and projective measurements
Quantum Robustness	Quantum machine learning with enhanced adversarial robustness
Quantum Simulation	Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding Quantum data learning for quantum simulations in high-energy physics On The Performance And Limitation Of Quantum Approximate Optimization Variational Quantum Time Evolution without the Quantum Geometric Tensor Neural–Shadow Quantum State Tomography Quantum Computing Quantum Monte Carlo
quantum simulations	Simulating dynamics of large quantum systems on small quantum devices using circuit knitting
quantum state characterization	Quantum Similarity Testing with Convolutional Neural Networks
quantum state preparation	Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
Quantum state tomography	Neural–Shadow Quantum State Tomography
Quantum states	Explainable Representation Learning of Small Quantum States
Quantum Stochastic Modelling	Dimension reduction in quantum stochastic modelling
quantum time evolution	Variational Quantum Time Evolution without the Quantum Geometric Tensor
Quantum topological data analysis	A Topological Features Based Quantum Kernel
quantum variational circuits	Homogenous space expressibility of parametrized quantum circuits Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform
Quantum Zero-Sum Game	Quadratic Speedup in Quantum Zero-Sum Games via Single-Call Mirror-Prox Matrix Methods
quantum-inspired algorithm	Complete quantum-inspired framework for simulations of flows past immersed bodies
quantum-inspired machine learning	Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
R
Random Quantum Circuits	Deep quantum neural networks form Gaussian processes
randomization test	Understanding quantum machine learning also requires rethinking generalization
recurrent networks	Machine learning continuously monitored systems: estimating parameters
reinforcement learning	Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
Representation Learning	Explainable Representation Learning of Small Quantum States
Representation Theory	Equivariant Quantum Models
Reservoir Computing	Hybrid quantum-classical reservoir computing for solving chaotic systems
residual network	QResNet: a variational entanglement skipping algorithm
Robust optimization	Quantum algorithm for robust optimization via stochastic-gradient online learning
S
Sample Complexity	On the Sample Complexity of Quantum Boltzmann Machine Learning
Sign problem	Quantum Computing Quantum Monte Carlo
single-photon-based quantum computing	Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform
skip-connection	QResNet: a variational entanglement skipping algorithm
Spin Glass Minimization	On The Performance And Limitation Of Quantum Approximate Optimization
spin systems	Detection of quantum phase transitions with quantum machine learning techniques Simulating dynamics of large quantum systems on small quantum devices using circuit knitting
stabilizer entropy	Learning t-doped stabilizer states
statistical learning theory	Understanding quantum machine learning also requires rethinking generalization
Supervised Learning	Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images
swap test	Quantum Distance Calculation for ε-Graph Construction
SWAP-Test	A Multi-Class Quantum Kernel-Based Classifier
Symmetry groups	Homogenous space expressibility of parametrized quantum circuits
T
Tensor Networks	Dimension reduction in quantum stochastic modelling Quantum optimization of Binary Neural Networks
time-series processing	Time-series quantum reservoir computing with weak and projective measurements
Topological kernel	A Topological Features Based Quantum Kernel
Trainability guarantees	Trainability barriers and opportunities in quantum generative modeling
trainable feature map	Let Quantum Neural Networks Choose Their Own Frequencies
trainable features	Let Quantum Neural Networks Choose Their Own Frequencies
Transformers	Extending Graph Transformers with Quantum Computed Aggregation
U
Unitary learning	The power and limitations of learning quantum dynamics incoherently
unsupervised learning	Quantum anomaly detection in the latent space of proton collision events at the LHC
Unsupervised machine learning	Explainable Representation Learning of Small Quantum States
V
Variational Algorithms	On The Performance And Limitation Of Quantum Approximate Optimization Symmetry-invariant quantum machine learning force fields
Variational models	On quantum backpropagation, information reuse, and cheating measurement collapse
variational quantum algorithm	QResNet: a variational entanglement skipping algorithm
Variational quantum algorithms	Classical simulations of noisy variational quantum circuits Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks Analyzing variational quantum landscapes with information content Quantum optimization of Binary Neural Networks Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform Quantum Computing Quantum Monte Carlo Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
Variational Quantum Circuit	Simulating dynamics of large quantum systems on small quantum devices using circuit knitting Quantum machine learning with enhanced adversarial robustness
Variational Quantum Circuits	Applying Genetic Algorithms to Optimize the Generalization Ability of Variational Quantum Circuits
variational quantum classifier	Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform
variational quantum computing	Here comes the SU(N) multivariate quantum gates and gradients
variational quantum eigensolver	Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform
W
weak measurements	Time-series quantum reservoir computing with weak and projective measurements

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