Measurements of Quantum Hamiltonians with Locally-Biased Classical
Shadows
- URL: http://arxiv.org/abs/2006.15788v1
- Date: Mon, 29 Jun 2020 03:08:03 GMT
- Title: Measurements of Quantum Hamiltonians with Locally-Biased Classical
Shadows
- Authors: Charles Hadfield, Sergey Bravyi, Rudy Raymond, Antonio Mezzacapo
- Abstract summary: We consider the problem of estimating expectation values of molecular Hamiltonians, obtained on states prepared on a quantum computer.
We propose a novel estimator for this task, which is locally optimised with knowledge of the Hamiltonian and a classical approximation to the underlying quantum state.
- Score: 6.434709790375755
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Obtaining precise estimates of quantum observables is a crucial step of
variational quantum algorithms. We consider the problem of estimating
expectation values of molecular Hamiltonians, obtained on states prepared on a
quantum computer. We propose a novel estimator for this task, which is locally
optimised with knowledge of the Hamiltonian and a classical approximation to
the underlying quantum state. Our estimator is based on the concept of
classical shadows of a quantum state, and has the important property of not
adding to the circuit depth for the state preparation. We test its performance
numerically for molecular Hamiltonians of increasing size, finding a sizable
reduction in variance with respect to current measurement protocols that do not
increase circuit depths.
Related papers
- Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers [37.69303106863453]
We propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers.
Our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average.
arXiv Detail & Related papers (2024-06-17T14:10:10Z) - Truncation technique for variational quantum eigensolver for Molecular
Hamiltonians [0.0]
variational quantum eigensolver (VQE) is one of the most promising quantum algorithms for noisy quantum devices.
We propose a physically intuitive truncation technique that starts the optimization procedure with a truncated Hamiltonian.
This strategy allows us to reduce the required number of evaluations for the expectation value of Hamiltonian on a quantum computer.
arXiv Detail & Related papers (2024-02-02T18:45:12Z) - Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes [32.07657827173262]
We introduce an innovative quantum framework for the agent's engagement with an unknown MDP.
We show that the quantum advantage in mean estimation leads to exponential advancements in regret guarantees for infinite horizon Reinforcement Learning.
arXiv Detail & Related papers (2023-10-18T03:17:51Z) - 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) - Enhanced estimation of quantum properties with common randomized
measurements [0.0]
We present a technique for enhancing the estimation of quantum state properties by incorporating approximate prior knowledge about the quantum state of interest.
This method involves performing randomized measurements on a quantum processor and comparing the results with those obtained from a classical computer that stores an approximation of the quantum state.
arXiv Detail & Related papers (2023-04-24T17:44:31Z) - Approximation of the Nearest Classical-Classical State to a Quantum
State [0.0]
A revolutionary step in computation is driven by quantumness or quantum correlations, which are permanent in entanglements but often in separable states.
The exact quantification of quantumness is an NP-hard problem; thus, we consider alternative approaches to approximate it.
We show that the objective value decreases along the flow by proofs and numerical results.
arXiv Detail & Related papers (2023-01-23T08:26:17Z) - 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) - 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) - 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 expectation-value estimation by computational basis sampling [0.0]
A practical obstacle is the necessity of a large number of measurements for statistical convergence.
We propose an algorithm to estimate the expectation value based on its approximate expression as a weighted sum of classically-tractable matrix elements.
arXiv Detail & Related papers (2021-12-14T14:08:56Z)
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.