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arXiv:1504.04787 · Physical Review A

Postprocessing for speeding up general quantum search algorithms

Avatar Tulsi

Published Aug 27, 2015• 4 citations
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Abstract

A general quantum search algorithm aims to evolve a quantum system from a known source state $|s\ensuremath{\rangle}$ to an unknown target state $|t\ensuremath{\rangle}$. It uses a diffusion operator ${D}_{s}$ that has the source state as one of its eigenstates and ${I}_{t}$, where ${I}_{\ensuremath{\psi}}$ denotes the selective phase inversion of the $|\ensuremath{\psi}\ensuremath{\rangle}$ state. It evolves $|s\ensuremath{\rangle}$ to a particular state $|w\ensuremath{\rangle}$, which we will call the $w$ state, in $O(B/\ensuremath{\alpha})$ time steps, where $\ensuremath{\alpha}$ is $|\ensuremath{\langle}t|s\ensuremath{\rangle}|$ and $B$ is a characteristic of the diffusion operator. Measuring the $w$ state gives the target state with a success probability of $O(1/{B}^{2})$, and $O({B}^{2})$ applications of the algorithm can boost it from $O(1/{B}^{2})$ to $O(1)$, making the total time complexity $O({B}^{3}/\ensuremath{\alpha})$. In the special case of Grover's algorithm, ${D}_{s}$ is ${I}_{s}$ and $B$ is very close to 1. A more efficient way to boost the success probability is quantum amplitude amplification provided we can efficiently implement ${I}_{w}$. Such an efficient implementation is not known so far. In this paper, we present an efficient algorithm to approximate selective phase inversions of the unknown eigenstates of an operator using a phase estimation algorithm. This algorithm is used to efficiently approximate ${I}_{w}$, which reduces the time complexity of general algorithm to $O(B/\ensuremath{\alpha})$. Although $O(B/\ensuremath{\alpha})$ algorithms are known to exist, our algorithm offers physical implementation advantages.

DOI
10.1103/physreva.92.022353

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A general quantum search algorithm aims to evolve a quantum system from a known source state ∣s\ensuremath⟩|s\ensuremath{\rangle}∣s\ensuremath⟩ to an unknown target state ∣t\ensuremath⟩|t\ensuremath{\rangle}∣t\ensuremath⟩. It uses a diffusion operator Ds{D}_{s}Ds​ that has the source state as one of its eigenstates and It{I}_{t}It​, where I\ensuremathψ{I}_{\ensuremath{\psi}}I\ensuremathψ​ denotes the selective phase inversion of the ∣\ensuremathψ\ensuremath⟩|\ensuremath{\psi}\ensuremath{\rangle}∣\ensuremathψ\ensuremath⟩ state. It evolves ∣s\ensuremath⟩|s\ensuremath{\rangle}∣s\ensuremath⟩ to a particular state ∣w\ensuremath⟩|w\ensuremath{\rangle}∣w\ensuremath⟩, which we will call the www state, in O(B/\ensuremathα)O(B/\ensuremath{\alpha})O(B/\ensuremathα) time steps, where \ensuremathα\ensuremath{\alpha}\ensuremathα is ∣\ensuremath⟨t∣s\ensuremath⟩∣|\ensuremath{\langle}t|s\ensuremath{\rangle}|∣\ensuremath⟨t∣s\ensuremath⟩∣ and BBB is a characteristic of the diffusion operator. Measuring the www state gives the target state with a success probability of O(1/B2)O(1/{B}^{2})O(1/B2), and O(B2)O({B}^{2})O(B2) applications of the algorithm can boost it from O(1/B2)O(1/{B}^{2})O(1/B2) to O(1)O(1)O(1), making the total time complexity O(B3/\ensuremathα)O({B}^{3}/\ensuremath{\alpha})O(B3/\ensuremathα). In the special case of Grover's algorithm, Ds{D}_{s}Ds​ is Is{I}_{s}Is​ and BBB is very close to 1. A more efficient way to boost the success probability is quantum amplitude amplification provided we can efficiently implement Iw{I}_{w}Iw​. Such an efficient implementation is not known so far. In this paper, we present an efficient algorithm to approximate selective phase inversions of the unknown eigenstates of an operator using a phase estimation algorithm. This algorithm is used to efficiently approximate Iw{I}_{w}Iw​, which reduces the time complexity of general algorithm to O(B/\ensuremathα)O(B/\ensuremath{\alpha})O(B/\ensuremathα). Although O(B/\ensuremathα)O(B/\ensuremath{\alpha})O(B/\ensuremathα) algorithms are known to exist, our algorithm offers physical implementation advantages.
Grover's search algorithm drives a quantum system from an initial state $|s\ensuremath{\rangle}$ to a desired final state $|t\ensuremath{\rangle}$ by using selective phase inversions of these two states. Earlier, we studied a generalization of Grover's algorithm that relaxes the assumption of the efficient implementation of Is{I}_{s}Is​, the selective phase inversion of the initial state, also known as a diffusion operator. This assumption is known to become a serious handicap in cases of physical interest. Our general search algorithm works with almost any diffusion operator Ds{D}_{s}Ds​ with the only restriction of having $|s\ensuremath{\rangle}$ as one of its eigenstates. The price that we pay for using any operator is an increase in the number of oracle queries by a factor of O(B)O(B)O(B), where BBB is a characteristic of the eigenspectrum of Ds{D}_{s}Ds​ and can be large in some situations. Here we show that by using a quantum Fourier transform, we can regain the optimal query complexity of Grover's algorithm without losing the freedom of using any diffusion operator for quantum searching. However, the total number of operators required by the algorithm is still O(B)O(B)O(B) times more than that of Grover's algorithm. So our algorithm offers an advantage only if the oracle operator is computationally more expensive than the diffusion operator, which is true in most search problems.
Grover's quantum search algorithm evolves a quantum system from a known source state ∣s⟩|s\rangle∣s⟩ to an unknown target state ∣t⟩|t\rangle∣t⟩ using the selective phase inversions, IsI_{s}Is​ and ItI_{t}It​, of these two states. In one of the generalizations of Grover's algorithm, IsI_{s}Is​ is replaced by a general diffusion operator DsD_{s}Ds​ having ∣s⟩|s\rangle∣s⟩ as an eigenstate and ItI_{t}It​ is replaced by a general selective phase rotation ItϕI_{t}^ϕItϕ​. A fast quantum search is possible as long as the operator DsD_{s}Ds​ and the angle ϕϕϕ satisfies certain conditions. These conditions are very restrictive in nature. Specifically, suppose ∣ℓ⟩|\ell\rangle∣ℓ⟩ denote the eigenstates of DsD_{s}Ds​ corresponding to the eigenphases θℓθ_{\ell}θℓ​. Then the sum of the terms ∣⟨ℓ∣t⟩∣2cot⁡(θℓ/2)|\langle \ell|t\rangle|^{2}\cot(θ_{\ell}/2)∣⟨ℓ∣t⟩∣2cot(θℓ​/2) over all ℓ≠s\ell \neq sℓ=s has to be almost equal to cot⁡(ϕ/2)\cot(ϕ/2)cot(ϕ/2) for a fast quantum search. In this paper, we show that this condition can be significantly relaxed by introducing appropriate modifications of the algorithm. This allows access to a more general class of diffusion operators for fast quantum search.
Grover's quantum search algorithm is an important landmark discovery in the area of quantum computing that drives a quantum computer from a known initial state(source state) to an unknown final state(target state) by using selective phase inversions of these states. The algorithm is simply a successive iteration of the Grover's search operator, which is a product of selective inversion operators of source and target states. In its original form, Grover's operator performs the selective inversions only on a unique source state and a unique target state. In the generalized version, we make our search operator to perform selective inversions on more than one source and target states. First, we find that the case of selective inversions of two source and two target states can be easily analysed somewhat similar to the case of Grover's algorithm. But unlike Grover's operator, the selective inversions of three source and three target states is not so easy to study analytically and hence we perform numerical calculations on some specific cases. We find that these generalized search operators also give us a successful quantum search as expected.
One iterative in Grover's original quantum search algorithm consists of two Hadamard–Walsh transformations, a selective amplitude inversion and a diffusion amplitude inversion. We concentrate on the relation among the probability of success of the algorithm, the phase shifts, the number of target items and the number of iterations via replacing the two amplitude inversions by phase shifts of an arbitrary ϕ = ϕ(0 ⩽ ϕ, ϕ ⩽ 2π). Then, according to the relation we find out the optimal phase shifts when the number of iterations is given. We present a new quantum search algorithm based on the optimal phase shifts of 1.018 after 0.5π/ iterations. The new algorithm can obtain either a single target item or multiple target items in the search space with the probability of success at least 93.43%.
Quantum search is among the most important algorithms in quantum computing. At its core is quantum amplitude amplification, a technique that achieves a quadratic speedup over classical search by combining two global reflections: the oracle, which marks the target, and the diffusion operator, which reflects about the initial state. We show that this speedup can be preserved when the oracle is the only global operator, with all other operations acting locally on non-overlapping partitions of the search register. We present a recursive construction that, when the initial and target states both decompose as tensor products over these chosen partitions, admits an exact closed-form solution for the algorithm's dynamics. This is enabled by an intriguing degeneracy in the principal angles between successive reflections, which collapse to just two distinct values governed by a single recursively defined angle. Applied to unstructured search, a problem that naturally satisfies the tensor decomposition, the approach retains the O(N)O(\sqrt{N})O(N​) oracle complexity of Grover search when each partition contains at least log⁡2(log⁡2N)\log_2(\log_2 N)log2​(log2​N) qubits. On an 18-qubit search problem, partitioning into two stages reduces the non-oracle circuit depth by as much as 51%-96% relative to Grover, requiring up to 9% additional oracle calls. For larger problem sizes this oracle overhead rapidly diminishes, and valuable depth reductions persist when the oracle circuit is substantially deeper than the diffusion operator. More broadly, these results show that a global diffusion operator is not necessary to achieve the quadratic speedup in quantum search, offering a new perspective on this foundational algorithm. Moreover, the scalar reduction at the heart of our analysis inspires and motivates new directions and innovations in quantum algorithm design and evaluation.
Abstract This guide is based on the IBM Quantum course Phase Estimation and Factoring and focuses on Grover’s algorithm, a quantum algorithm for unstructured search problems that provides a quadratic speedup over classical approaches. In an unstructured search problem, we are given a function (often represented as an oracle) that marks one or more “correct” solutions among NNN possible candidates. A classical algorithm requires on the order of O(N)O(N)O(N) evaluations in the worst case. In contrast, Grover’s algorithm finds a solution using only O(N)O(\sqrt{N})O(N) oracle queries. This quadratic improvement implies: The quantum algorithm requires roughly the square root of the number of operations needed classically. Any classical algorithm for unstructured search must have cost at least quadratic relative to Grover’s quantum procedure. Core Idea of the Algorithm Grover’s algorithm relies on: Preparation of a uniform superposition (typically using Hadamard gates). An oracle that marks valid solutions via phase inversion. The Grover diffusion operator, which amplifies the probability amplitude of the marked states through interference. By repeatedly applying the Grover operator, the amplitude of the desired solution grows while others diminish, allowing measurement to reveal the correct answer with high probability. Practical Considerations Despite its theoretical elegance and wide applicability, the quadratic speedup of Grover’s algorithm is unlikely to yield near-term practical advantage. Modern classical hardware operates at extremely high clock rates and is vastly more mature than current quantum devices. For problem sizes accessible to today’s quantum computers, classical systems remain dominant. However, history shows that even sub-quadratic classical improvements — such as: Fast Fourier Transform (FFT) Efficient sorting algorithms like quicksort and mergesort have had transformative practical impact. The crucial distinction is that Grover’s algorithm depends on fundamentally different hardware — quantum processors — which are still in early development. Long-Term Perspective As quantum hardware advances in qubit count, coherence time, and error correction, Grover’s quadratic advantage may become practically significant for: Cryptographic key search Optimization heuristics Combinatorial search problems Amplitude amplification subroutines in broader quantum algorithms Through hands-on experimentation on IBM Q, this lesson demonstrates how theoretical amplitude amplification translates into executable quantum circuits, reinforcing the relationship between interference, probability amplitudes, and computational complexity.