Fast quantum algorithms for traversing paths of eigenstates
arXiv:1005.3034
Abstract
Consider a path of non-degenerate eigenstates of unitary operators or Hamiltonians with minimum eigenvalue gap G. The eigenpath traversal problem is to transform one or more copies of the initial to the final eigenstate. Solutions to this problem have applications ranging from quantum physics simulation to optimization. For Hamiltonians, the conventional way of doing this is by applying the adiabatic theorem. We give ``digital'' methods for performing the transformation that require no assumption on path continuity or differentiability other than the absence of large jumps. Given sufficient information about eigenvalues and overlaps between states on the path, the transformation can be accomplished with complexity O(L/G log(L/e)), where L is the angular length of the path and e is a specified bound on the error of the output state. We show that the required information can be obtained in a first set of transformations, whose complexity per state transformed has an additional factor that depends logarithmically on a maximum angular velocity along the path. This velocity is averaged over constant angular distances and does not require continuity. Our methods have substantially better behavior than conventional adiabatic algorithms, with fewer conditions on the path. They also improve on the previously best digital methods and demonstrate that path length and the gap are the primary parameters that determine the complexity of state transformation along a path.
36 pages, 1 figure
References in corpus (9)
- Quantum algorithm for solving linear systems of equations
- Ultracold atomic gases in optical lattices: mimicking condensed matter physics and beyond
- Simulated Quantum Computation of Molecular Energies
- Optimal Quantum Measurements of Expectation Values of Observables
- Quantum Simulations of Classical Annealing Processes
- Speed-up via Quantum Sampling
- An Elementary Proof of the Quantum Adiabatic Theorem
- Necessary Condition for the Quantum Adiabatic Approximation
- Adiabatic Markovian Dynamics
Cited by in corpus (7)
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- Fast Quantum Methods for Optimization
- Improved Bounds for Eigenpath Traversal
- Reflection-Based Adiabatic State Preparation