[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 …
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 …
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 …
Unbiasing fermionic quantum Monte Carlo with a quantum computer
Interacting many-electron problems pose some of the greatest computational challenges in
science, with essential applications across many fields. The solutions to these problems will …
science, with essential applications across many fields. The solutions to these problems will …
Avoiding barren plateaus using classical shadows
Variational quantum algorithms are promising algorithms for achieving quantum advantage
on near-term devices. The quantum hardware is used to implement a variational wave …
on near-term devices. The quantum hardware is used to implement a variational wave …
Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …
of novel materials. Computationally hard tasks in materials science stretch the limits of …
Shallow shadows: Expectation estimation using low-depth random Clifford circuits
We provide practical and powerful schemes for learning properties of a quantum state using
a small number of measurements. Specifically, we present a randomized measurement …
a small number of measurements. Specifically, we present a randomized measurement …
Exponential separations between learning with and without quantum memory
We study the power of quantum memory for learning properties of quantum systems and
dynamics, which is of great importance in physics and chemistry. Many state-of-the-art …
dynamics, which is of great importance in physics and chemistry. Many state-of-the-art …
Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation
Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …
and industries including computational science, mathematics, finance, pharmaceutical …