[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 …
[HTML][HTML] Quantum computing for near-term applications in generative chemistry and drug discovery
Highlights•Drug discovery is time consuming, expensive and experiences increasing
challenges.•Generation of new drug candidates is one of the major challenges.•Quantum …
challenges.•Generation of new drug candidates is one of the major challenges.•Quantum …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
The state of quantum computing applications in health and medicine
FF Flöther - Research Directions: Quantum Technologies, 2023 - cambridge.org
Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-
related activities and experiments in the last few years (although biology and quantum …
related activities and experiments in the last few years (although biology and quantum …
Efficient quantum computation of molecular forces and other energy gradients
While most work on the quantum simulation of chemistry has focused on computing energy
surfaces, a similarly important application requiring subtly different algorithms is the …
surfaces, a similarly important application requiring subtly different algorithms is the …
Leveraging small-scale quantum computers with unitarily downfolded hamiltonians
In this work, we propose a quantum unitary downfolding formalism based on the driven
similarity renormalization group (QDSRG) that may be combined with quantum algorithms …
similarity renormalization group (QDSRG) that may be combined with quantum algorithms …
[PDF][PDF] Hyperparameter optimization of hybrid quantum neural networks for car classification
Image recognition is one of the primary applications of machine learning algorithms.
Nevertheless, machine learning models used in modern image recognition systems consist …
Nevertheless, machine learning models used in modern image recognition systems consist …
Quantum computing algorithms: getting closer to critical problems in computational biology
The recent biotechnological progress has allowed life scientists and physicians to access an
unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular …
unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular …
Error-mitigated fermionic classical shadows on noisy quantum devices
Efficiently estimating fermionic Hamiltonian expectation values is vital for simulating various
physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number …
physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number …
Complexity of life sciences in quantum and AI era
A Pyrkov, A Aliper, D Bezrukov… - Wiley …, 2024 - Wiley Online Library
Having made significant advancements in understanding living organisms at various levels
such as genes, cells, molecules, tissues, and pathways, the field of life sciences is now …
such as genes, cells, molecules, tissues, and pathways, the field of life sciences is now …