Formulation of a Family of Sure-Success Quantum Search Algorithms

Sure-Success Quantum Algorithms on Weight Decision Problem

Conditions on sure-success decidability of weights of Boolean functions are presented for a given number of generalized Grover iterations. It is shown that the decidability problem reduces to a system of algebraic equations of a single variable. For problems that require a large number of iterations, it is observed that the iteration number of sure-success quantum algorithms scale as the square root of the iteration number of the corresponding classical probabilistic algorithms. It is also demonstrated that for a few iterations, quantum algorithms can be more efficient than this.

Sure success partial search

Partial search has been proposed recently for finding the target block containing a target element with fewer queries than the full Grover search algorithm which can locate the target precisely. Since such partial searches will likely be used as subroutines for larger algorithms their success rate is important. We propose a partial search algorithm which achieves success with unit probability.

A family of sure-success quantum algorithms for solving a generalized Grover search problem

This work considers a generalization of Grover's search problem, viz., to find any one element in a set of acceptable choices which constitute a fraction f of the total number of choices in an unsorted data base. An infinite family of sure-success quantum algorithms are introduced here to solve this problem, each member for a different range of f. The nth member of this family involves n queries of the data base, and so the lowest few members of this family should be very convenient algorithms within their ranges of validity. The even member {A}_{2n} of the family covers ever larger range of f…

一种具有确定成功概率的优化量子最小值搜索算法及其基于 Cirq 的实验模拟

An optimized quantum minimum searching algorithm with sure-success probability and its experiment simulation with Cirq

针对量子最小值搜索问题，提出一种结合 Grover-Long 精确搜索、动态迭代策略与 oracle 电路优化的确定成功概率算法，并通过 Cirq 模拟验证其可行性。

Geometric formulation of quantum mechanics

Quantum mechanics is among the most important and successful mathematical model for describing our physical reality. The traditional formulation of quantum mechanics is linear and algebraic. In contrast classical mechanics is a geometrical and non-linear theory that is defined on a symplectic manifold. However, after invention of general relativity, we are convinced that geometry is physical and effect us in all scale. Hence the geometric formulation of quantum mechanics sought to give a unified picture of physical systems based on its underling geometrical structures, e.g., now, the states ar…

Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer

We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may…