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Quantum phase transitions materialize as level crossings in the ground-state energy when the parameters of the Hamiltonian are varied. The resulting ground-state phase diagrams are straightforward to determine by exact diagonalization on…

Quantum Physics · Physics 2021-12-10 Akhil Francis , Ephrata Zelleke , Ziyue Zhang , Alexander F. Kemper , J. K. Freericks

Adiabatic quantum computing (AQC) started as an approach to solving optimization problems, and has evolved into an important universal alternative to the standard circuit model of quantum computing, with deep connections to both classical…

Quantum Physics · Physics 2018-02-05 Tameem Albash , Daniel A. Lidar

We show that, during adiabatic evolution, any changes in entanglement can be attributed to a succession of avoided energy level crossings at which eigenvalues swap their eigenvectors. These swaps mediate the generation and redistribution of…

Quantum Physics · Physics 2025-08-14 Einar Gabbassov , Achim Kempf

Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the systems scale up due to the shrinkage of the excitation gap and…

Adiabatic quantum computation (AQC), which is particularly useful for combinatorial optimization, becomes more powerful by using excited states, instead of ground states. However, the excited-state AQC is prone to errors due to dissipation.…

Quantum Physics · Physics 2020-12-22 Hayato Goto , Taro Kanao

Quantum computing has the potential to transform simulations of quantum many-body problems at the heart of electronic structure theory. Efficient quantum algorithms to compute the eigenstates of fermionic Hamiltonians, such as quantum phase…

Quantum Physics · Physics 2026-05-29 Hugh G. A. Burton , Maria-Andreea Filip

Adiabatic quantum computation has recently attracted attention in the physics and computer science communities, but its computational power was unknown. We describe an efficient adiabatic simulation of any given quantum algorithm, which…

Quantum Physics · Physics 2007-11-22 Dorit Aharonov , Wim van Dam , Julia Kempe , Zeph Landau , Seth Lloyd , Oded Regev

We introduce a family of Hamiltonian systems for measurement-based quantum computation with continuous variables. The Hamiltonians (i) are quadratic, and therefore two body, (ii) are of short range, (iii) are frustration-free, and (iv)…

Quantum Physics · Physics 2011-03-01 Leandro Aolita , Augusto J. Roncaglia , Alessandro Ferraro , Antonio Acín

We elaborate the idea of quantum computation through measuring the correlation of a gapped ground state, while the bulk Hamiltonian is utilized to stabilize the resource. A simple computational primitive, by pulling out a single spin…

Quantum Physics · Physics 2010-07-29 Akimasa Miyake

We propose a novel non-Hermitian adiabatic quantum optimization algorithm. One of the new ideas is to use a non-Hermitian auxiliary "initial'' Hamiltonian that provides an effective level repulsion for the main Hamiltonian. This effect…

Quantum Physics · Physics 2012-11-15 Gennady P. Berman , Alexander I. Nesterov

Adiabatic quantum computing and optimization have garnered much attention recently as possible models for achieving a quantum advantage over classical approaches to optimization and other special purpose computations. Both techniques are…

Quantum Physics · Physics 2016-04-19 Lishan Zeng , Jun Zhang , Mohan Sarovar

Controllable adiabatic evolution of a multi-qubit system can be used for adiabatic quantum computation (AQC). This evolution ends at a configuration where the Hamiltonian of the system encodes the solution of the problem to be solved. As a…

Quantum control techniques are employed to perform adiabatic quantum computing in the presence of noise. First, we analyze the adiabatic entanglement protocol (AEP) for two qubits. In this case, we found that this protocol is very robust…

Quantum Physics · Physics 2023-06-13 Marllos E. F. Fernandes , Emanuel F. de Lima , Leonardo K. Castelano

Many artificial intelligence (AI) problems naturally map to NP-hard optimization problems. This has the interesting consequence that enabling human-level capability in machines often requires systems that can handle formally intractable…

Quantum Physics · Physics 2009-09-29 Hartmut Neven , Geordie Rose , William G. Macready

Linear system solvers are widely used in scientific computing, with the primary goal of solving linear system problems. Classical iterative algorithms typically rely on the conjugate gradient method. The rise of quantum computing has…

Quantum Physics · Physics 2024-12-10 Guojian Wu , Fang Gao , Qing Gao , Yu Pan

We show that the NP-hard quadratic unconstrained binary optimization (QUBO) problem on a graph $G$ can be solved using an adiabatic quantum computer that implements an Ising spin-1/2 Hamiltonian, by reduction through minor-embedding of $G$…

Quantum Physics · Physics 2009-07-16 Vicky Choi

The Hubbard model has occupied the minds of condensed matter physicists for most part of the last century. This model provides insight into a range of phenomena in correlated electron systems. We wish to examine the paradigm of quantum…

Quantum Physics · Physics 2026-05-12 Kunal Vyas , Fengping Jin , Hans De Raedt , Kristel Michielsen

We prove that adiabatic computation is equivalent to standard quantum computation even when the adiabatic quantum system is restricted to be a set of particles on a one-dimensional chain. We give a construction that uses a 2-local…

Quantum Physics · Physics 2008-02-19 Sandy Irani

Finding a local Hamiltonian $\hat{\mathcal{H}}$ having a given many-body wavefunction $|\psi\rangle$ as its ground state, i.e. a parent Hamiltonian, is a challenge of fundamental importance in quantum technologies. Here we introduce a…

We apply a quantum adiabatic evolution algorithm to a combinatorial optimization problem where the cost function depends entirely on the of the number of unit bits in a n-bit string (Hamming weight). The solution of the optimization problem…

Quantum Physics · Physics 2007-05-23 Alex Bulatov , Vadim Smelyanskiy
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