[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 …
Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
Theory of overparametrization in quantum neural networks
The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …
exciting. Understanding how QNN properties (for example, the number of parameters M) …
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 …
On quantum backpropagation, information reuse, and cheating measurement collapse
The success of modern deep learning hinges on the ability to train neural networks at scale.
Through clever reuse of intermediate information, backpropagation facilitates training …
Through clever reuse of intermediate information, backpropagation facilitates training …
Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware
Quantum computers may provide good solutions to combinatorial optimization problems by
leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often …
leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often …
Capacity and quantum geometry of parametrized quantum circuits
To harness the potential of noisy intermediate-scale quantum devices, it is paramount to find
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …
Quantum computing of the nucleus via ordered unitary coupled clusters
The variational quantum eigensolver (VQE) is an algorithm to compute ground and excited
state energy of quantum many-body systems. A key component of the algorithm and an …
state energy of quantum many-body systems. A key component of the algorithm and an …
Learnability of quantum neural networks
Quantum neural network (QNN), or equivalently, the parameterized quantum circuit (PQC)
with a gradient-based classical optimizer, has been broadly applied to many experimental …
with a gradient-based classical optimizer, has been broadly applied to many experimental …
Matrix-model simulations using quantum computing, deep learning, and lattice monte carlo
Matrix quantum mechanics plays various important roles in theoretical physics, such as a
holographic description of quantum black holes, and it underpins the only practical …
holographic description of quantum black holes, and it underpins the only practical …