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Artificial intelligence and machine learning for quantum technologies
In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …
of science and technology significantly. In the present perspective article, we explore how …
Deep reinforcement learning in chemistry: A review
Reinforcement learning (RL) has been applied to various domains in computational
chemistry and has found wide‐spread success. In this review, we first motivate the …
chemistry and has found wide‐spread success. In this review, we first motivate the …
Filtering variational quantum algorithms for combinatorial optimization
Current gate-based quantum computers have the potential to provide a computational
advantage if algorithms use quantum hardware efficiently. To make combinatorial …
advantage if algorithms use quantum hardware efficiently. To make combinatorial …
KANQAS: Kolmogorov-Arnold network for quantum architecture search
Quantum architecture Search (QAS) is a promising direction for optimization and automated
design of quantum circuits towards quantum advantage. Recent techniques in QAS …
design of quantum circuits towards quantum advantage. Recent techniques in QAS …
Quantum circuit synthesis with diffusion models
Quantum computing has recently emerged as a transformative technology. Yet, its promised
advantages rely on efficiently translating quantum operations into viable physical …
advantages rely on efficiently translating quantum operations into viable physical …
Quantum architecture search via deep reinforcement learning
Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …
realistic application for and using quantum computers. However, designing a suitable …
Quantumdarts: differentiable quantum architecture search for variational quantum algorithms
With the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era and the fast
development of machine learning, variational quantum algorithms (VQA) including …
development of machine learning, variational quantum algorithms (VQA) including …
Variational quantum pulse learning
Quantum computing is among the most promising emerging techniques to solve problems
that are computationally intractable on classical hardware. A large body of existing works …
that are computationally intractable on classical hardware. A large body of existing works …
Training-free quantum architecture search
Variational quantum algorithm (VQA) derives advantages from its error resilience and high
flexibility in quantum resource requirements, rendering it broadly applicable in the noisy …
flexibility in quantum resource requirements, rendering it broadly applicable in the noisy …
Physics-informed bayesian optimization of variational quantum circuits
In this paper, we propose a novel and powerful method to harness Bayesian optimization for
variational quantum eigensolvers (VQEs)-a hybrid quantum-classical protocol used to …
variational quantum eigensolvers (VQEs)-a hybrid quantum-classical protocol used to …