• QAR-Lab LMU
• INnsbruck University
• PlanQK - Platform and ecosystem for Quantum-inspired Artificial Intelligence. The goal is to develop an open platform for Quantum-inspired AI to create and promote an ecosystem of AI & Quantum Computing (QC) specialists, developers of QC applications as well as users, service providers and consultants.
• QUTech TU Delft
  • https://qutech.nl/elkouss-group/publications/
  • https://qutech.nl/stephanie-wehner-group/
• RWTH AACHEN

• Simon Benjamin - Oxford https://qtechtheory.org/
• Steven Flammia - UoS https://www.sydney.edu.au/science/about/our-people/academic-staff/steven-flammia.html#collapseprofileresearchinterest
• Stephen Bartlett - UoS https://www.sydney.edu.au/science/about/our-people/academic-staff/stephen-bartlett.html
• Aram W. Harrow - MIT http://web.mit.edu/aram/www/
• Marcello Benedetti - UCL https://scholar.google.co.uk/citations?user=Pj-XjgkAAAAJ&hl=en
• Maria Schuld - University of KwaZulu-Natal https://quantum.ukzn.ac.za/ms-m-schuld/

• The Holy Grail of Quantum Artificial Intelligence:Major Challenges in Accelerating the Machine Learning Pipeline
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Quantum Computing in the NISQ era and beyond
• The Engineering Challenges in Quantum Computing
• Machine learning & artificial intelligence in the quantum domain
• Parameterized quantum circuits as machine learning models
• Quantum Machine Learning
• The bitter truth about gate-based quantum algorithms in the NISQ era

• A high threshold code for modular hardware with asymmetric noise
• Fault-Tolerant Logical Gates in the IBM Quantum Experience
• Multiqubit randomized benchmarking using few samples
• Transforming graph states using single-qubit operations
• QVECTOR: an algorithm for device-tailored quantum error correction

• Variational-State Quantum Metrology
• Variational quantum simulation of general processes
• Theory of variational quantum simulation

• Quantum and classical probability distributions for arbitrary Hamiltonians
• Probability Representation of Quantum Mechanics Where System States Are Identified with Probability Distributions

• Quantum compilation and circuit optimisation via energy dissipation
• Quantum-assisted Quantum Compiling

• Simple proof of confidentiality for private quantum channels in noisy environments

• Quantum internet: A vision for the road ahead
• Towards Large-Scale Quantum Networks

• Small quantum computers and large classical data sets

• From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning
• Quantum Convolutional Neural Networks
• Quantum autoencoders for efficient compression of quantum data
• Classification with Quantum Neural Networks on Near Term Processors
• Learning the quantum algorithm for state overlap
• Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
• Real time evolution with neural-network quantum states
• Variational Inference with Normalizing Flows
• Machine learning methods in quantum computing theory
• Small quantum computers and large classical data set
• Machine learning quantum states in the NISQ era
• Quantum machine learning in feature Hilbert spaces
• Sample-efficient learning of quantum many-body systems
• Power of data in quantum machine learning
• Generative training of quantum Boltzmann machines with hidden units

• Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers
• Quanvolutional Neural Networks: Powering Image Recognition with Quantum Circuits
• Experimental Implementation of a Quantum Autoencoder via Quantum Adders

• Implementing a distance-based classifier with a quantum interference circuit
• Circuit-centric quantum classifiers
• Quantum classifier with tailored quantum kernel
• Quantum circuit structure learning
• Robust Implementation of Generative Modeling with Parametrized Quantum Circuits
• Towards Quantum Machine Learning with Tensor Networks

• Quantum autoencoders via quantum adders with genetic algorithms
• Training of Quantum Circuits on a Hybrid Quantum Computer
• Efficient Learning for Deep Quantum Neural Networks
• A Universal Training Algorithm for Quantum Deep Learning
• Training deep quantum neural networks
• Layerwise learning for quantum neural networks
• The quantum Wasserstein distance of order 1
• Quantum Natural Gradients

• Adversarial quantum circuit learning for pure state approximation
• Quantum Generative Adversarial Networks for Learning and Loading Random Distributions, other source
• Quantum generative adversarial learning
• Quantum generative adversarial networks
• Quantum generative adversarial network for generating discrete distribution
• Quantum Wasserstein GANs
• Online Kernel based Generative Adversarial Networks

• Quantum process tomography with unsupervised learning and tensor networks

• Evaluating analytic gradients on quantum hardware
• Solving machine learning optimization problems using quantum computers

• Superior memory efficiency of quantum devices for the simulation of continuous-time stochastic processes
• Quantum mechanics can reduce the complexity of classical models

• Quantum computing for finance: Overview and prospects
• Option Pricing using Quantum Computers

• Using Quantum Mechanics to Cluster Time Series
• Time series forecasting with qubit neural networks

• Methodology for replacing indirect measurements with direct measurements

• A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration
• Quantum Technologies in Space

• Continuous-variable quantum neural networks
• Continuous-variable quantum computing in the quantum optical frequency comb

• Quantum Sampling Algorithms for Near-Term Devices
• Quantum Risk Analysis
• Crossing a topological phase transition with a quantum computer
• Quantum data compression, quantum information generation, and the density-matrix
• Adding control to arbitrary unknown quantum operations
• Real- and imaginary-time evolution with compressed quantum circuits
• Equivalent Quantum Circuits
• Reversing Unknown Quantum Transformations: Universal Quantum Circuit for Inverting General Unitary Operations

• Quant GANs: Deep Generation of Financial Time Series
• A Two-Step Disentanglement Method
• Stock Market Prediction Based on Generative Adversarial Network
• Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo
• Classification with non-i.i.d. sampling
• Near-Optimal No-Regret Algorithms for Zero-Sum Games
• The Distribution of Stock Return Volatility
• Smart sampling and incremental function learning for very large high dimensional data
• High-Dimensional Statistics in Finance
• Encoding Time Series Data for Better Clustering Results
• Deep Learning and Time Series-to-Image Encoding for Financial Forecasting
• Time2Vec: Learning a Vector Representation of Time
• MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial Networks
• CONDITIONAL GAN FOR TIMESERIES GENERATION
• UNSUPERVISED AND SEMI-SUPERVISED LEARNING WITH CATEGORICAL GENERATIVE ADVERSARIAL NETWORKS
• JointGAN: Multi-Domain Joint Distribution Learning with
• Adversarially Learned Inference

Qiskit is an open-source quantum computing software development framework for leveraging today's quantum processors in research, education, and business.

A cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.d

Python framework for hybrid quantum-classical machine learning that is primarily focused on modeling quantum data. TFQ is an application framework developed to allow quantum algorithms researchers and machine learning applications researchers to explore computing workflows that leverage Google’s quantum computing offerings, all from within TensorFlow.

Cirq is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators.

Variational quantum circuits by construction depend only on a linear or polynomial number of parameters while the Hilbert-space dimension of the underlying quantum state increases exponentially in the number of qubits. This advantageous scaling allows one to tackle classically intractable problems. The general concept of variational quantum algorithms is to prepare a parametrised quantum state using a quantum processor and to vary its parameters externally until the optimum of a suitable cost function is reached. This cost function can be tailored to the particular problem. For example, one can search for the ground state of a molecule by setting the cost function to be the expectation value of the corresponding molecular Hamiltonian. This technique is usually referred to as the variational quantum eigensolver (VQE) [1, 2, 3, 4]. Quantum machine learning is another area where variational techniques may be valuable. One is then interested in optimising a cost function that quantifies how similar the output of the quantum circuit is to a fixed dataset [7, 8]. Moreover, it is also possible to recompile a quantum circuit into another by optimising a metric on related quantum states [9, 10]. Variational-State Quantum Metrology

Variational algorithms have been developed as a powerful classical tool for simulating manybody quantum systems. The core idea is based on the intuition that physical states with low energy generally belong to an exponentially small manifold of the whole Hilbert space

Variational simulation is a widely used technique in many-body physics

Purely quantum methods differ from classical stochastic optimization in that they are usually guaranteed to find the global optimum under ideal conditions. In real-world implementations, they, too, yield stochastic results.

Qubits also cannot be copied, and any attempt to do so can be detected. (https://science.sciencemag.org/content/sci/362/6412/eaam9288.full.pdf)

Recent work shows that quantum mechanics can provide more parsimonious models of stochastic processes than classical models, as quantified by an entropic measure of complexity. This suggests that quantum models hold the potential to substantially reduce the amount of other type of computational resources, e.g. memory, required to model a given dataset. (https://arxiv.org/pdf/1708.09757.pdf)

• Provide means to process (the essence of) large amounts of data on quantum computers.
• Provide standardized interfaces that allow for dynamic combination of QAI components and (by extension) for experts of different fields to collaborate on QAI algorithms.
• Focus on problems that are currently hard and intractable for the ML community, for example, generative models in unsupervised and semi-supervised (https://arxiv.org/pdf/1708.09757.pdf)
• Focus on datasets with potentially intrinsic quantum-like correlations, making quantum computers indispensable; these will provide the most compact and efficient model representation, with the potential of a significant quantum advantage even at the level of 50-100 qubit devices. (https://arxiv.org/pdf/1708.09757.pdf)
• Focus on hybrid algorithms where a quantum routine is executed in the intractable step of the classical ML algorithmic pipeline (https://arxiv.org/pdf/1708.09757.pdf)

• Research in this field has been focusing on tasks such as classification [14], regression [11, 15, 18], Gaussian models [16], vector quantization [13], principal component analysis [17] and other strategies that are routinely used by ML practitioners nowadays. We do not think these approaches would be of practical use in near-term quantum computers. The same reasons that make these techniques so popular, e.g., their scalability and algorithmic efficiency in tackling huge datasets, make them less appealing to become top candidates as killer applications in QAML with devices in the range of 100-1000 qubits. In other words, regardless of the claims about polynomial and even exponential algorithmic speedup, reaching interesting industrial scale applications would require millions or even billions of qubits. Such an advantage is then moot when dealing with real-world datasets and with the quantum devices to become available in the next years in the few thousands-of-qubits regime (source: https://arxiv.org/pdf/1708.09757.pdf)

• https://thequantumdaily.com/2019/11/18/the-worlds-top-12-quantum-computing-research-universities/
• Author of Quantum generative adversarial networks explains the concepts (video)