[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …
received significant attention from the research community in recent years. It uses the …
The randomized measurement toolbox
Programmable quantum simulators and quantum computers are opening unprecedented
opportunities for exploring and exploiting the properties of highly entangled complex …
opportunities for exploring and exploiting the properties of highly entangled complex …
Quantum advantage in learning from experiments
Quantum technology promises to revolutionize how we learn about the physical world. An
experiment that processes quantum data with a quantum computer could have substantial …
experiment that processes quantum data with a quantum computer could have substantial …
Power of data in quantum machine learning
The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …
applications of quantum technologies. However, machine learning tasks where data is …
Observing the quantum Mpemba effect in quantum simulations
The nonequilibrium physics of many-body quantum systems harbors various unconventional
phenomena. In this Letter, we experimentally investigate one of the most puzzling of these …
phenomena. In this Letter, we experimentally investigate one of the most puzzling of these …
Provably efficient machine learning for quantum many-body problems
Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …
challenging quantum many-body problems in physics and chemistry. However, the …
Predicting many properties of a quantum system from very few measurements
Predicting the properties of complex, large-scale quantum systems is essential for
developing quantum technologies. We present an efficient method for constructing an …
developing quantum technologies. We present an efficient method for constructing an …
Learning quantum systems
The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …
quantum systems of increasing complexity, with key applications in computation, simulation …
Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
Information-theoretic bounds on quantum advantage in machine learning
We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …
predicting outcomes of physical experiments. The experiments depend on an input …