Quantum Power Iteration Unified Using Generalized Quantum Signal Processing
- URL: http://arxiv.org/abs/2507.11142v1
- Date: Tue, 15 Jul 2025 09:47:43 GMT
- Title: Quantum Power Iteration Unified Using Generalized Quantum Signal Processing
- Authors: Viktor Khinevich, Yasunori Lee, Nobuyuki Yoshioka, Wataru Mizukami,
- Abstract summary: We present a unifying framework for quantum power-method-based algorithms.<n>We apply GQSP to realize quantum analogues of classical power iteration, power Lanczos, inverse iteration, and folded spectrum methods.<n>Our results indicate that GQSP-based implementations of power methods combine scalability, flexibility, and robust convergence.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a unifying framework for quantum power-method-based algorithms through the lens of generalized quantum signal processing (GQSP): we apply GQSP to realize quantum analogues of classical power iteration, power Lanczos, inverse iteration, and folded spectrum methods, all within a single coherent framework. Our approach is efficient in terms of the number of queries to the block encoding of a Hamiltonian. Also, our approach can avoid Suzuki-Trotter decomposition. We constructed quantum circuits for GQSP-based quantum power methods, estimated the number of queries, and numerically verified that this framework works. We additionally benchmark various quantum power methods with molecular Hamiltonians and demonstrate that Quantum Power Lanczos converges faster and more reliably than standard Quantum Power Iteration, while Quantum Inverse Iteration outperforms existing inverse iteration variants based on time-evolution operators. We also show that the Quantum Folded Spectrum Method can obtain excited states without variational optimization. Overall, our results indicate that GQSP-based implementations of power methods combine scalability, flexibility, and robust convergence, paving the way for practical initial state preparations on fault-tolerant quantum devices.
Related papers
- Advancing Quantum State Preparation Using Decision Diagram with Local Invertible Maps [5.328178128965817]
We propose a family of efficient Quantum State Preparation (QSP) algorithms tailored to different numbers of available ancilla qubits.<n>Our approach exploits the power of Local Invertible Map Decision Diagrams (LimTDDs) to reduce quantum circuit complexity.
arXiv Detail & Related papers (2025-07-23T03:34:44Z) - VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - Neural-network-assisted Monte Carlo sampling trained by Quantum Approximate Optimization Algorithm [0.8009842832476994]
We propose a hybrid quantum-classical MCMC framework that combines a quantum circuit with a generative neural sampler (GNS)<n>GNS acts as a classical surrogate to efficiently emulate quantum outputs, thereby lifting circuit constraints.<n>These results establish the method as a viable sampling-based quantum algorithm for NISQ devices.
arXiv Detail & Related papers (2025-06-02T05:26:26Z) - Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
arXiv Detail & Related papers (2025-05-24T02:51:34Z) - Variational Quantum Subspace Construction via Symmetry-Preserving Cost Functions [39.58317527488534]
We propose a variational strategy based on symmetry-preserving cost functions to iteratively construct a reduced subspace for extraction of low-lying energy states.<n>As a proof of concept, we test the proposed algorithms on H4 chain and ring, targeting both the ground-state energy and the charge gap.
arXiv Detail & Related papers (2024-11-25T20:33:47Z) - Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation [41.01256771536732]
This study evaluates the performance of quantum and quantum-inspired methods compared to classical models for crack segmentation.
Our results indicate that quantum-inspired and quantum methods offer a promising alternative for image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.
arXiv Detail & Related papers (2024-10-14T16:51:59Z) - Efficient Quantum Pseudorandomness from Hamiltonian Phase States [41.94295877935867]
We introduce a quantum hardness assumption called the Hamiltonian Phase State (HPS) problem.<n>We show that our assumption is plausibly fully quantum; meaning, it cannot be used to construct one-way functions.<n>We show that our assumption and its variants allow us to efficiently construct many pseudorandom quantum primitives.
arXiv Detail & Related papers (2024-10-10T16:10:10Z) - Benchmarking Variational Quantum Eigensolvers for Entanglement Detection in Many-Body Hamiltonian Ground States [37.69303106863453]
Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain quantum advantage.
We use a specific class of VQA named variational quantum eigensolvers (VQEs) to benchmark them at entanglement witnessing and entangled ground state detection.
Quantum circuits whose structure is inspired by the Hamiltonian interactions presented better results on cost function estimation than problem-agnostic circuits.
arXiv Detail & Related papers (2024-07-05T12:06:40Z) - Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
We present a model for parallelizing simulation of quantum circuit executions.
The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
arXiv Detail & Related papers (2024-06-05T17:16:07Z) - Quantum Phase Processing and its Applications in Estimating Phase and
Entropies [10.8525801756287]
"quantum phase processing" can directly apply arbitrary trigonometric transformations to eigenphases of a unitary operator.
Quantum phase processing can extract the eigen-information of quantum systems by simply measuring the ancilla qubit.
We propose a new quantum phase estimation algorithm without quantum Fourier transform, which requires the fewest ancilla qubits and matches the best performance so far.
arXiv Detail & Related papers (2022-09-28T17:41:19Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - A Grand Unification of Quantum Algorithms [0.0]
A number of quantum algorithms were recently tied together by a technique known as the quantum singular value transformation.
This paper provides a tutorial through these developments, first illustrating how quantum signal processing may be generalized to the quantum eigenvalue transform.
We then employ QSVT to construct intuitive quantum algorithms for search, phase estimation, and Hamiltonian simulation.
arXiv Detail & Related papers (2021-05-06T17:46:33Z)
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.