Exponential distillation of dominant eigenproperties
- URL: http://arxiv.org/abs/2506.04380v1
- Date: Wed, 04 Jun 2025 18:49:08 GMT
- Title: Exponential distillation of dominant eigenproperties
- Authors: Bence Bakó, Tenzan Araki, Bálint Koczor,
- Abstract summary: Estimating observable expectation values in eigenstates of quantum systems has a broad range of applications.<n>We develop a hybrid quantum-classical algorithm that enables the estimation of an arbitrary observable expectation value in an eigenstate.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Estimating observable expectation values in eigenstates of quantum systems has a broad range of applications and is an area where early fault-tolerant quantum computers may provide practical quantum advantage. We develop a hybrid quantum-classical algorithm that enables the estimation of an arbitrary observable expectation value in an eigenstate, given an initial state is supplied that has dominant overlap with the targeted eigenstate -- but may overlap with any other eigenstates. Our approach builds on, and is conceptually similar to purification-based error mitigation techniques; however, it achieves exponential suppression of algorithmic errors using only a single copy of the quantum state. The key innovation is that random time evolution is applied in the quantum computer to create an average mixed quantum state, which is then virtually purified with exponential efficacy. We prove rigorous performance guarantees and conclude that the complexity of our approach depends directly on the energy gap in the problem Hamiltonian, and is thus comparable to state-of-the-art hybrid techniques. We demonstrate in a broad range of numerical simulations the applicability of our framework in near-term and early fault-tolerant settings. Furthermore, we demonstrate in a 100-qubit example that direct classical simulation of our approach enables the prediction of ground and excited state properties of quantum systems using tensor network techniques, which we recognize as a quantum-inspired classical approach.
Related papers
- Bounds on a Wavefunction Overlap with Hamiltonian Eigen-states: Performance Guarantees for the Quantum Phase Estimation Algorithm [0.0]
Estimating the overlap between an approximate wavefunction and a target eigenstate of the system Hamiltonian is essential for the efficiency of quantum phase estimation.<n>We derive upper and lower bounds on this overlap using expectation values of Hamiltonian powers and bounds on target eigenenergies.
arXiv Detail & Related papers (2025-03-15T18:24:12Z) - Efficient quantum-enhanced classical simulation for patches of quantum landscapes [0.0]
We show that it is always possible to generate a classical surrogate of a sub-region of an expectation landscape produced by a parameterized quantum circuit.<n>We provide a quantum-enhanced classical algorithm which, after simple measurements on a quantum device, allows one to classically simulate approximate expectation values of a subregion of a landscape.
arXiv Detail & Related papers (2024-11-29T18:00:07Z) - Enhancing Scalability of Quantum Eigenvalue Transformation of Unitary Matrices for Ground State Preparation through Adaptive Finer Filtering [0.13108652488669736]
Hamiltonian simulation is a domain where quantum computers have the potential to outperform classical counterparts.<n>One of the main challenges of such quantum algorithms is increasing the system size.<n>We present an approach to improve the scalability of eigenspace filtering for the ground state preparation of a given Hamiltonian.
arXiv Detail & Related papers (2024-01-17T09:52:24Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Fighting noise with noise: a stochastic projective quantum eigensolver [0.0]
We present a novel approach to estimating physical observables which leads to a two order of magnitude reduction in the required sampling of the quantum state.
The method can be applied to excited-state calculations and simulation for general chemistry on quantum devices.
arXiv Detail & Related papers (2023-06-26T09:22:06Z) - Quantum Thermal State Preparation [39.91303506884272]
We introduce simple continuous-time quantum Gibbs samplers for simulating quantum master equations.
We construct the first provably accurate and efficient algorithm for preparing certain purified Gibbs states.
Our algorithms' costs have a provable dependence on temperature, accuracy, and the mixing time.
arXiv Detail & Related papers (2023-03-31T17:29:56Z) - Universality of critical dynamics with finite entanglement [68.8204255655161]
We study how low-energy dynamics of quantum systems near criticality are modified by finite entanglement.
Our result establishes the precise role played by entanglement in time-dependent critical phenomena.
arXiv Detail & Related papers (2023-01-23T19:23:54Z) - Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
We present an approach where the quantum computation is supplemented by a classical result.
Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative.
In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
arXiv Detail & Related papers (2022-09-12T15:47:44Z) - Variational Approach to Quantum State Tomography based on Maximal
Entropy Formalism [3.6344381605841187]
We employ the maximal entropy formalism to construct the least biased mixed quantum state that is consistent with the given set of expectation values.
We employ a parameterized quantum circuit and a hybrid quantum-classical variational algorithm to obtain such a target state making our recipe easily implementable on a near-term quantum device.
arXiv Detail & Related papers (2022-06-06T01:16:22Z) - Theory of Quantum Generative Learning Models with Maximum Mean
Discrepancy [67.02951777522547]
We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs)
We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution.
Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
arXiv Detail & Related papers (2022-05-10T08:05:59Z) - Probabilistic imaginary-time evolution by using forward and backward
real-time evolution with a single ancilla: first-quantized eigensolver of
quantum chemistry for ground states [0.0]
Imaginary-time evolution (ITE) on a quantum computer is a promising formalism for obtaining the ground state of a quantum system.
We propose a new approach of PITE which requires only a single ancillary qubit.
We discuss the application of our approach to quantum chemistry by focusing on the scaling of computational cost.
arXiv Detail & Related papers (2021-11-24T12:54:27Z) - 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) - Quantum Causal Inference in the Presence of Hidden Common Causes: an
Entropic Approach [34.77250498401055]
We put forth a new theoretical framework for merging quantum information science and causal inference by exploiting entropic principles.
We apply our proposed framework to an experimentally relevant scenario of identifying message senders on quantum noisy links.
This approach can lay the foundations of identifying originators of malicious activity on future multi-node quantum networks.
arXiv Detail & Related papers (2021-04-24T22:45:50Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z)
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.