Quantum computers have an exponential speed-up advantage over classical computers. One of the most prominent utilities of quantum computers is their ability to study complex quantum systems in various fields using quantum computational algorithms. Quantum computational algorithms can be used to study cosmological systems and how they behave with variations in the different parameters of the system. Here, we use the variational quantum eigensolver (VQE) algorithm to simulate the Hawking radiation phenomenon. VQE algorithm is a combination of quantum and classical computation methods used to obtain the minimum energy eigenvalue for a given Hamiltonian. Three different custom ansatzes are used in the VQE algorithm from which the results for the case with minimum errors are studied. We obtain the plots for temperature and power from the minimum energy eigenvalue recorded for different values of mass and distance from the center of the black hole. The final result is then analyzed and compared against already existing data on Hawking radiation.
Quantum computers have an exponential speed-up advantage over classical computers. One of the most prominent utilities of quantum computers is their ability to study complex quantum systems in various fields using quantum computational algorithms. Quantum computational algorithms can be used to study cosmological systems and how they behave with variations in the different parameters of the system. Here, we use the variational quantum eigensolver (VQE) algorithm to simulate the Hawking radiation phenomenon. VQE algorithm is a combination of quantum and classical computation methods used to obtain the minimum energy eigenvalue for a given Hamiltonian. Three different custom ansatzes are used in the VQE algorithm from which the results for the case with minimum errors are studied. We obtain the plots for temperature and power from the minimum energy eigenvalue recorded for different values of mass and distance from the center of the black hole. The final result is then analyzed and compared against already existing data on Hawking radiation.
Analyzing complex quantum systems using quantum computational algorithms is one of the most promising applications of quantum computers. This study focuses on evaluating the performance of a custom variational ansatz in the Variational Quantum Eigensolver (VQE) algorithm compared to predefined ansatzes. To achieve this, we employ the evaporating black hole model as a test bed for our analysis. Using the VQE approach, which integrates quantum and classical computing techniques, we aim to minimize the energy expectation value of the Hamiltonian. By training the circuit parameters of a trial wave function as a parameterized quantum circuit, we determine the upper bound for the ground state energy and assess the optimal variational form. We define a custom ansatz for the VQE protocol and compare its performance with other predefined ansatzes. Additionally, we test the performance of three different classical optimizers to further understand their impact on the VQE algorithm’s efficiency and accuracy.
For a complex quantum system, the dimensionality of the Hamiltonian grows exponentially with the size of the system, making it difficult to perform fast large-scale simulations and calculations on a classical computer. The first Variational Quantum Eigenvalue solver (VQE) proposed by Peruzzo et al. This gives the possibility of highly accurate molecular simulations and calculations in the field of quantum chemical computing. VQE is roughly based on the principle of using a quantum computer and set up a trial wave function |ψ(θ)⟩=U(θ)|ψ0⟩ to measure the energy expectation of the wave function |ψ(θ)⟩ at the corresponding initialization parameter, and then using a classical optimization algorithm to iterate θ0→θn over the energy to minimum until convergence. The significance of this paper is to verify the accuracy as well as the feasibility of the VQE algorithm for the calculation of LiH molecules in STO-3g basis, and to perform an error analysis and to verify the exponential acceleration of the classical algorithm implementation. These results shed light on guiding further exploration focusing on quantum computingt.
Abstract Material discovery is a phenomenon practiced since the evolution of the world. The discovery of materials has led to significant development in varied fields such as Science, Engineering and Technology. Computationally simulating molecules has been an area of interest in the industry as well as academia. However, simulating large molecules can be computationally expensive in terms of computing power and complexity. Quantum computing is a recent development that can improve the efficiency in predicting properties of atoms and molecules which will be useful for material design. The Variational Quantum Eigensolver (VQE) is one such quantum algorithm used to calculate the ground state energy of molecules or ions. In this study, we have done a comparative analysis of the parameters that constitute the VQE algorithm. This includes components such as basis, qubit mapping, ansatz, and optimizers used. We have also developed a database consisting of 79 single atoms and their variations of oxidation states and 33 molecules with the data of their Hamiltonian and ground state energy and dipole moment.
Abstract Quantum computing is a technology that utilizes the principles of quantum mechanics to perform complex computational processes. In this work, we use Qiskit Module from IBM to do quantum computational calculation using Variational Quantum Eigensolver (VQE) algorithm. VQE is a hybrid quantum-classical method that combines a quantum computer to measure energies and a classical computer to process the measurement results and update the parameters of the quantum computer. The purpose of VQE is to find the ground state energy of a chemical system. In the previous study, many of the VQE calculations have been done on simple molecules. So, in this study, we would like to use Hydrazine molecule as our object of VQE calculation. Furthermore, these results will be compared with the results from the classical calculation (MP2, CCSD(T), QCISD(T), and CASSCF) methods for testing the effectiveness of VQE using Unitary Coupled-Cluster Single and Double excitations (UCCSD) Ansatz. The quantum algorithm based on the UCCSD Ansatz led to a simplification of the algorithm by reducing the circuit depth. Then, the possibility to use active space approximation, can be used to reduce the quantum gates while trying to keep a good level of accuracy. In this study, we chose (2,2) and (4,4) active spaces. Based on the results, as we increase the size of the active space during the evaluation of the single-point energy, the estimated ground states obtained from the VQE algorithm yield nearly identical values. Conversely, in CASSCF calculations, expanding the active space introduces more energy corrections, thus making it more sensitive. Additionally, when examining potential energy surfaces, VQE demonstrates results that gradually align with CCSD(T) and QCISD(T) methods.
Current quantum computers (QCs) belong to the noisy intermediate-scale quantum (NISQ) class, characterized by noisy qubits, limited qubit capabilities, and limited circuit depth. These limitations have led to the development of hybrid quantum-classical algorithms that split the computational cost between classical and quantum hardware. Among the hybrid algorithms, the variational quantum eigensolver (VQE) is mentioned. The VQE is a variational quantum algorithm designed to estimate the eigenvalues and eigenvectors of a system on universal-gate quantum architectures. A canonical problem in electromagnetics is the computation of eigenmodes within waveguides. Following the finite difference method, the wave equation can be recast as an eigenvalue problem. This work exploits the quantum superposition and entanglement in quantum computing to solve the square waveguide mode problem. This algorithm is expected to demonstrate exponentially efficiency over classical computational techniques as the qubit count increases. The simulations were performed on IBM’s three-qubit quantum simulator, Qasm IBM Simulator. A shot-based simulation was performed considering computationally based measurements of the quantum hardware. The results of the probabilistic read-out, reported in terms of 2-D eigenmode field distributions, are close to ideal values with a few number of qubits, confirming the possibility to exploit the quantum advantage to formulate innovative eigensolvers.
The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the ground state of a Hamiltonian using variational methods. It has a wide range of potential applications, from quantum chemistry to lattice gauge theories in the Hamiltonian formulation. VQE relies on quantum computers to evaluate the energy of the system in terms of circuit parameters, and it minimizes this parametrized energy with a classical optimization routine. This work describes a Bayesian optimization (BO) algorithm specifically designed to minimize the parametrized energy obtained with a quantum computer. BO based on Gaussian process regression (GPR) is an algorithm for finding the global minimum of a black-box cost function, e.g. the energy, with a very low number of iterations even when using data affected by statistical noise. Furthermore, the GPR procedure developed for this work proved to be very versatile as we also used it to compute discrete integral transforms of noisy data. In particular, this procedure was used to reconstruct parton distribution functions from lattice QCD data.