Quantum Interference as a Proposal Mechanism for Combinatorial Optimization
- URL: http://arxiv.org/abs/2607.27509v1
- Date: Wed, 29 Jul 2026 22:54:13 GMT
- Title: Quantum Interference as a Proposal Mechanism for Combinatorial Optimization
- Abstract summary: Quantum Proposal Interference Search (QIPS) uses seed-conditioned quantum circuits to generate localized interference patterns.<n>QIPS is used to update an elite frontier of low-energy solutions.<n>Performance is assessed using top-$K$ coverage, hit rate, multiplicity, Hilbert-space coverage and dyadic-rank metrics.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum Interference Proposal Search (QIPS) uses seed-conditioned quantum circuits to generate localized interference patterns as finite-shot proposal distributions for QUBO/Ising optimization. Candidate $n_b$-bit strings are sampled from these distributions, scored classically and used to update an elite frontier of low-energy solutions. QIPS uses a fixed two-layer gate-based circuit architecture with 100 shots per circuit while the Hilbert-space dimension grows as $2^{n_b}$. Across six benchmark families with $18 \le n_b \le 29$, QIPS maintains competitive progress relative to a matched classical control that preserves the same search loop, frontier update rule and proposal budget, with total proposals proportional to $n_b$. Performance is assessed using top-$K$ coverage, hit rate, multiplicity, Hilbert-space coverage and dyadic-rank metrics. The results identify localized quantum interference as a resource-efficient proposal mechanism for computational quantum optimization.
Related papers
- A Scalable Distributed Quantum Optimization Framework via Factor Graph Paradigm [46.08923284345648]
We introduce a structure-aware framework for distributed quantum optimization.<n>We find that for a search space of size $N$, our framework achieves $O(sqrtN)$ query complexity up to processors and separator dependent factors.<n>We show that structure-aware decomposition delivers a practical path to scalable distributed quantum optimization on quantum networks.
arXiv Detail & Related papers (2026-03-08T15:15:52Z) - Large-scale portfolio optimization on a trapped-ion quantum computer [32.24411362086563]
We present an end-to-end pipeline for large-scale portfolio selection with cardinality constraints.<n>We experimentally demonstrate it on trapped-ion quantum processors using hardware-aware decomposition.
arXiv Detail & Related papers (2026-02-27T12:36:14Z) - Quantum approximate optimization of bosonic finite-state systems [0.0]
Formulating problems in nature inherently described by finite $D$-dimensional states requires mapping the qudit Hilbert space to that of multiqubit.<n>Here we propose to employ the Hamiltonian-based quantum approximate optimization algorithm (QAOA) through devising appropriate mixing Hamiltonians.<n>We apply this framework to quantum approximate thermalization and find the ground state of the repulsive Bose-Hubbard model in the strong and weak interaction regimes.
arXiv Detail & Related papers (2025-10-07T04:44:13Z) - Hybrid Quantum-Classical Branch-and-Price Method for the Vertex Coloring Problem [0.11242503819703255]
Quantum Classical Branch-and-Price (QCBP) is a hybrid quantum-classical algorithm for the Vertex Coloring problem on neutral-atom Quantum Processing Units (QPUs)
arXiv Detail & Related papers (2025-08-26T10:03:56Z) - A Multilevel Approach For Solving Large-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization [3.3493770627144004]
We experimentally test how existing quantum processing units (QPUs) perform as subsolvers within a multilevel strategy.
We find approximate solutions to $10$ instances of fully connected $82$-qubit Sherrington-Kirkpatrick graphs.
We observe that quantum optimization results are competitive regarding the quality of solutions compared to classicals.
arXiv Detail & Related papers (2024-08-14T20:06:32Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement
Distribution in FSO Quantum Networks [62.87033427172205]
Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments where establishing an optical fiber infrastructure is challenging and costly.
A reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual line-of-sight between users for entanglement distribution.
arXiv Detail & Related papers (2024-01-19T17:16:40Z) - Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$.
We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
arXiv Detail & Related papers (2024-01-17T18:59:38Z) - Efficient DCQO Algorithm within the Impulse Regime for Portfolio
Optimization [41.94295877935867]
We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm.
Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors.
We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer.
arXiv Detail & Related papers (2023-08-29T17:53:08Z) - The QAOA with Few Measurements [4.713817702376467]
The Approximate Quantum Optimization Algorithm (QAOA) was originally developed to solve optimization problems.
Fully descriptive benchmarking techniques are often expensive for large numbers of qubits.
Some experimental quantum computing platforms such as neutral atom quantum computers have slow repetition rates.
arXiv Detail & Related papers (2022-05-13T18:42:20Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.