The Dicke-state-initialized Grover’s algorithm is used for the hitting set problem (DSIGA-HSP) to efficiently solve the NP-Complete HSP. By initializing the working qubits in the Dicke state Dkn , the algorithm restricts the search domain to subsets of fixed size k, thereby avoiding the use of quantum counters and reducing the search space from 2n to n k . A Dicke-state-based diffuser and quantum-flag oracle are designed to achieve selective amplitude amplification of valid hitting sets. Experimental results based on the IBM Qiskit Aer Simulator confirm that the quantum circuit generated by DSIGA-HSP can successfully solve the HSP.
The Dicke-state-initialized Grover’s algorithm is used for the hitting set problem (DSIGA-HSP) to efficiently solve the NP-Complete HSP. By initializing the working qubits in the Dicke state Dkn , the algorithm restricts the search domain to subsets of fixed size k, thereby avoiding the use of quantum counters and reducing the search space from 2n to n k . A Dicke-state-based diffuser and quantum-flag oracle are designed to achieve selective amplitude amplification of valid hitting sets. Experimental results based on the IBM Qiskit Aer Simulator confirm that the quantum circuit generated by DSIGA-HSP can successfully solve the HSP.
An algorithm called Dicke state quantum search for the hitting set problem (DSQS-HSP), generates quantum circuits to solve the k-hitting set problem (k-HSP), by initializing the working qubits in an n-qubit Dicke state Dkn of exactly k qubits in 1. The quantum circuit reduces the search space size from 2n to D = nk, the number of symmetric superposition states in Dkn. A quantum-flag oracle checks the hitting condition, and a mirror-readout mechanism projects valid solutions to the output register. The circuit yields two outcome types: the all-zero string with probability (D−M)/D and solution strings, each with probability 1/D, where M is the number of solutions. The resource growth of qubits, gates, circuit depth, and circuit execution repetitions is O(nk), which remains polynomial in the best case for min(k, n − k) ≪ n/2. Experimental results using IBM Qiskit Aer Simulator confirm that the DSQS-HSP can produce quantum circuits to successfully solve the k-HSP.
This paper proposes a quantum algorithm, named Dicke state quantum search (DSQS), to set qubits in the Dicke state |Dkn⟩ of D states in superposition to locate the target inputs or solutions of specific patterns among 2n unstructured input instances, where n is the number of input qubits and D=nk=O(nk) for min(k,n−k)≪n/2. Compared to Grover’s algorithm, a famous quantum search algorithm that calls an oracle and a diffuser O(2n) times, DSQS requires no diffuser and calls an oracle only once. Furthermore, DSQS does not need to know the number of solutions in advance. We prove the correctness of DSQS with unitary transformations, and show that each solution can be found by DSQS with an error probability less than 1/3 through O(nk) repetitions, as long as min(k,n−k)≪n/2. Additionally, this paper proposes a classical algorithm, named DSQS-VCP, to generate quantum circuits based on DSQS for solving the k-vertex cover problem (k-VCP), a well-known NP-complete (NPC) problem. Complexity analysis demonstrates that DSQS-VCP operates in polynomial time and that the quantum circuit generated by DSQS-VCP has a polynomial qubit count, gate count, and circuit depth as long as min(k,n−k)≪n/2. We thus conclude that the k-VCP can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3 under the condition of min(k,n−k)≪n/2. Since the k-VCP is NP-complete, NP and NPC problems can be polynomially reduced to the k-VCP. If the reduced k-VCP instance satisfies min(k,n−k)≪n/2, then both the instance and the original NP/NPC problem instance to which it corresponds can be solved by the DSQS-VCP quantum circuit in polynomial time with an error probability less than 1/3. All statements of polynomial algorithm execution time in this paper apply only to VCP instances and similar instances of other problems, where min(k,n−k)≪n/2. Thus, they imply neither NP ⊆ BQP nor P = NP. In this restricted regime of min(k,n−k)≪n/2, the Dicke state subspace has a polynomial size of D=nk=O(nk), and our DSQS algorithm samples from it without asymptotic superiority over exhaustive enumeration. Nevertheless, DSQS may be combined with other quantum algorithms to better exploit the strengths of quantum computation in practice. Experimental results using IBM Qiskit packages show that the DSQS-VCP quantum circuit can solve the k-VCP successfully.
This paper proposes using Dicke states to improve Grover’s quantum search algorithm for solving the k-vertex cover problem in a given graph with n vertices, a well-known NP-complete problem. A Dicke state $\left| {D_k^n} \right\rangle $ is a special entangled quantum state representing the symmetric superposition of all n-qubit states where exactly k qubits are in |1⟩. It is used to prepare the initial quantum state to reduce the quantum search space from 2nto C(n,k). Consequently, the operations required by Grover’s algorithm are reduced from O(2n) to O(C(n,k)), which is O(nk) for 1 ≤ k ≪ n/2 or n − k ≪ n/2.
Grover's algorithm is a well-known unstructured quantum search algorithm run on quantum computers. It constructs an oracle and calls the oracle O(N) times to locate specific data out of N unsorted data. This represents a quadratic speedup compared to the classical unstructured data sequential search algorithm, which requires to call the oracle O(N) times. We are currently in the noisy intermediate-scale quantum (NISQ) era in which quantum computers have a limited number of qubits, short decoherence time, and low gate fidelity. It is thus desirable to design quantum components with three good properties: (i) a reduced number of qubits, (ii) shorter quantum depth, and (iii) fewer gates. This paper utilizes novel quantum counters with the above-mentioned three good properties to construct the oracle of Grover's algorithm to efficiently solve the dominating set problem (DSP), as defined below. For a given graph G=(V, E), a dominating set (DS) D is a subset of the vertex set V, such that every vertex is in D or has an adjacent vertex in D. The DSP is to decide for a given graph G and an integer k whether there exists a DS with size k. Algorithms solving the DSP have many applications. For example, they can be applied to check whether k routers suffice to connect all computers in a computer network. The DSP is an NP-complete problem, indicating that no classical algorithm exists to solve the DSP with polynomial time complexity in the worst case. Therefore, using quantum algorithms, such as Grover's algorithm, to exploit the potent computational capabilities of quantum computers to solve the DSP is highly promising. We execute the whole quantum circuit of Grover's algorithm using novel quantum counters through the IBM Quantum Lab service to validate that the circuit can solve the DSP efficiently and correctly.
Using quantum algorithms to solve various problems has attracted widespread attention with the development of quantum computing. Researchers are particularly interested in using the acceleration properties of quantum algorithms to solve NP-complete problems. This paper focuses on the well-known NP-complete problem of finding the minimum dominating set in undirected graphs. To expedite the search process, a quantum algorithm employing Grover’s search is proposed. However, a challenge arises from the unknown number of solutions for the minimum dominating set, rendering direct usage of original Grover’s search impossible. Thus, a swap test method is introduced to ascertain the number of iterations required. The oracle, diffusion operators, and swap test are designed with achievable quantum gates. The query complexity is O(1.414 n ) and the space complexity is O(n). To validate the proposed approach, qiskit software package is employed to simulate the quantum circuit, yielding the anticipated results.
Grover's search algorithm provides a quadratic speedup over classical brute-force search in terms of query complexity and is widely used as a versatile subroutine in numerous quantum algorithms, including those for combinatorial problems with large search spaces. For such problems, it is natural to reduce the effective search space by incorporating problem constraints at the initialization step, which in Grover's algorithm can be achieved by preparing structured initial states that encode constraint information. In this work, we present a systematic framework with a simple preprocessing procedure for constraint-aware initialization in Grover's algorithm, focusing on problems with linear constraints. While such structured initial states can reduce the number of oracle queries required to obtain a solution, their preparation incurs additional circuit-level costs. We therefore offer a conservative circuit-level resource analysis, showing that the resulting constraint-aware initialization can improve resource efficiency in terms of gate counts and circuit depth. The validity of the framework is further demonstrated numerically using the exact-cover problem. Overall, our results indicate that this approach serves as a practical baseline for achieving more resource-efficient implementations of Grover's algorithm compared to the standard uniform initialization.