Quantum block Krylov subspace projector algorithm for computing low-lying eigenenergies
- URL: http://arxiv.org/abs/2506.09971v2
- Date: Tue, 15 Jul 2025 17:34:24 GMT
- Title: Quantum block Krylov subspace projector algorithm for computing low-lying eigenenergies
- Authors: Maria Gabriela Jordão Oliveira, Nina Glaser,
- Abstract summary: QBKSP is a multireference quantum Lanczos method designed to accurately compute low-lying eigenenergies, including degenerate ones, of quantum systems.<n>We present three compact quantum circuits tailored to different problem settings for evaluating the required expectation values.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computing eigenvalues is a computationally intensive task central to numerous applications in the natural sciences. Toward this end, we investigate the quantum block Krylov subspace projector (QBKSP) algorithm - a multireference quantum Lanczos method designed to accurately compute low-lying eigenenergies, including degenerate ones, of quantum systems. We present three compact quantum circuits tailored to different problem settings for evaluating the required expectation values. To assess the impact of the number and fidelity of initial reference states, as well as time evolution duration, we perform error-free and limited-precision numerical simulations and quantum circuit simulations. Our results show that using multiple reference states significantly enhances convergence, particularly in precision-limited scenarios and in cases where a single reference state fails to capture all target eigenvalues. Additionally, the QBKSP algorithm allows for the determination of degenerate eigenstates and their multiplicities through appropriate convergence conditions.
Related papers
- 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) - SRBB-Based Quantum State Preparation [1.3108652488669736]
A scalable algorithm for the approximate quantum state preparation problem is proposed.<n>The algorithm uses a variational quantum circuit based on the Standard Recursive Block Basis (SRBB)<n>The desired quantum state is then approximated by a scalable quantum neural network.
arXiv Detail & Related papers (2025-03-17T18:51:07Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Lower bound for simulation cost of open quantum systems: Lipschitz continuity approach [5.193557673127421]
We present a general framework to calculate the lower bound for simulating a broad class of quantum Markov semigroups.
Our framework can be applied to both unital and non-unital quantum dynamics.
arXiv Detail & Related papers (2024-07-22T03:57:41Z) - 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) - Improved Quantum Algorithms for Eigenvalues Finding and Gradient Descent [0.0]
Block encoding is a key ingredient in the recently developed quantum singular value transformation (QSVT) framework.<n>In this article, we affirm this perspective by leveraging block encoding to substantially enhance two previously proposed quantum algorithms.<n>Our findings demonstrate that even just elementary operations within the unitary block encoding framework can eliminate major scaling factors.
arXiv Detail & Related papers (2023-12-22T15:59:03Z) - Forward and Backward Constrained Bisimulations for Quantum Circuits using Decision Diagrams [3.788308836856851]
We develop efficient methods for the simulation of quantum circuits on classic computers.
In particular, we show that constrained bisimulation can boost decision-diagram-based quantum circuit simulation by several orders of magnitude.
arXiv Detail & Related papers (2023-08-18T12:40:47Z) - Simultaneous estimation of multiple eigenvalues with short-depth quantum
circuit on early fault-tolerant quantum computers [5.746732081406236]
We introduce a multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) method to simultaneously estimate multiple eigenvalues of a quantum Hamiltonian on early fault-tolerant quantum computers.
Our theoretical analysis demonstrates that the algorithm exhibits Heisenberg-limited scaling in terms of circuit depth and total cost.
arXiv Detail & Related papers (2023-03-10T05:42:26Z) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
We provide a complete quantum circuit-level description of the algorithm from problem input to problem output.
We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
arXiv Detail & Related papers (2022-11-22T18:54:48Z) - Analyzing Prospects for Quantum Advantage in Topological Data Analysis [35.423446067065576]
We analyze and optimize an improved quantum algorithm for topological data analysis.
We show that super-quadratic quantum speedups are only possible when targeting a multiplicative error approximation.
We argue that quantum circuits with tens of billions of Toffoli can solve seemingly classically intractable instances.
arXiv Detail & Related papers (2022-09-27T17:56:15Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Variational determination of arbitrarily many eigenpairs in one quantum
circuit [8.118991737495524]
A variational quantum eigensolver (VQE) was first introduced for computing ground states.
We propose a new algorithm to determine many low energy eigenstates simultaneously.
Our algorithm reduces significantly the complexity of circuits and the readout errors.
arXiv Detail & Related papers (2022-06-22T13:01:37Z) - Fundamental limitations on optimization in variational quantum
algorithms [7.165356904023871]
A leading paradigm to establish such near-term quantum applications is variational quantum algorithms (VQAs)
We prove that for a broad class of such random circuits, the variation range of the cost function vanishes exponentially in the number of qubits with a high probability.
This result can unify the restrictions on gradient-based and gradient-free optimizations in a natural manner and reveal extra harsh constraints on the training landscapes of VQAs.
arXiv Detail & Related papers (2022-05-10T17:14:57Z) - Improved Quantum Algorithms for Fidelity Estimation [77.34726150561087]
We develop new and efficient quantum algorithms for fidelity estimation with provable performance guarantees.
Our algorithms use advanced quantum linear algebra techniques, such as the quantum singular value transformation.
We prove that fidelity estimation to any non-trivial constant additive accuracy is hard in general.
arXiv Detail & Related papers (2022-03-30T02:02:16Z) - Numerical Simulations of Noisy Quantum Circuits for Computational
Chemistry [51.827942608832025]
Near-term quantum computers can calculate the ground-state properties of small molecules.
We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
arXiv Detail & Related papers (2021-12-31T16:33:10Z) - Quantum Krylov subspace algorithms for ground and excited state energy
estimation [0.0]
Quantum Krylov subspace diagonalization (QKSD) algorithms provide a low-cost alternative to the conventional quantum phase estimation algorithm.
We show that a wide class of Hamiltonians relevant to condensed matter physics and quantum chemistry contain symmetries that can be exploited to avoid the use of the Hadamard test.
We develop a unified theory of quantum Krylov subspace algorithms and present three new quantum-classical algorithms for the ground and excited-state energy estimation problems.
arXiv Detail & Related papers (2021-09-14T17:56:53Z) - Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
We present a constructive algorithm for generating quantum circuits with time-independent depth.
We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model.
In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
arXiv Detail & Related papers (2021-04-01T19:06:00Z)
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.