Simulating Quantum Computations with Tutte Polynomials
- URL: http://arxiv.org/abs/2101.00211v2
- Date: Sat, 25 Sep 2021 10:47:33 GMT
- Title: Simulating Quantum Computations with Tutte Polynomials
- Authors: Ryan L. Mann
- Abstract summary: We establish a classical algorithm for exactly computing quantum probability amplitudes.
Our algorithm is based on mapping output probability amplitudes of quantum circuits to evaluations of the Tutte of graphic matroids.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We establish a classical heuristic algorithm for exactly computing quantum
probability amplitudes. Our algorithm is based on mapping output probability
amplitudes of quantum circuits to evaluations of the Tutte polynomial of
graphic matroids. The algorithm evaluates the Tutte polynomial recursively
using the deletion-contraction property while attempting to exploit structural
properties of the matroid. We consider several variations of our algorithm and
present experimental results comparing their performance on two classes of
random quantum circuits. Further, we obtain an explicit form for Clifford
circuit amplitudes in terms of matroid invariants and an alternative efficient
classical algorithm for computing the output probability amplitudes of Clifford
circuits.
Related papers
- 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) - Sub-universal variational circuits for combinatorial optimization
problems [0.0]
This work introduces a novel class of classical probabilistic circuits designed for generating quantum approximate solutions to optimization problems constructed using two-bit matrices.
Through a numerical study, we investigate the performance of our proposed variational circuits in solving the Max-Cut problem on various graphs of increasing sizes.
Our findings suggest that evaluating the performance of quantum variational circuits against variational circuits with sub-universal gate sets is a valuable benchmark for identifying areas where quantum variational circuits can excel.
arXiv Detail & Related papers (2023-08-29T02:16:48Z) - A Universal Quantum Algorithm for Weighted Maximum Cut and Ising
Problems [0.0]
We propose a hybrid quantum-classical algorithm to compute approximate solutions of binary problems.
We employ a shallow-depth quantum circuit to implement a unitary and Hermitian operator that block-encodes the weighted maximum cut or the Ising Hamiltonian.
Measuring the expectation of this operator on a variational quantum state yields the variational energy of the quantum system.
arXiv Detail & Related papers (2023-06-10T23:28:13Z) - Predicting RNA Secondary Structure on Universal Quantum Computer [2.277461161767121]
It is the first step for understanding how RNA structure folds from base sequences that to know how its secondary structure is formed.
Traditional energy-based algorithms are short of precision, particularly for non-nested sequences.
Gate model algorithms for universal quantum computing are not available.
arXiv Detail & Related papers (2023-05-16T15:57:38Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Approximating outcome probabilities of linear optical circuits [0.0]
We propose classical algorithms for approximating outcome probabilities of a linear optical circuit.
Our scheme renders an efficient estimation of outcome probabilities with precision depending on the classicality of the circuit.
Our study sheds light on the power of linear optics, providing plenty of quantum-inspired algorithms for problems in computational complexity.
arXiv Detail & Related papers (2022-11-14T08:21:51Z) - A single $T$-gate makes distribution learning hard [56.045224655472865]
This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits.
We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
arXiv Detail & Related papers (2022-07-07T08:04:15Z) - 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) - Polynomial unconstrained binary optimisation inspired by optical
simulation [52.11703556419582]
We propose an algorithm inspired by optical coherent Ising machines to solve the problem of unconstrained binary optimization.
We benchmark the proposed algorithm against existing PUBO algorithms, and observe its superior performance.
The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approxing the electronic structure problem by a PUBO problem.
arXiv Detail & Related papers (2021-06-24T16:39:31Z) - 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) - Quantum Algorithms for Estimating Physical Quantities using
Block-Encodings [0.30458514384586405]
We present quantum algorithms for the estimation of n-time correlation functions, the local and non-local density of states, and dynamical linear response functions.
All algorithms are based on block-encodings - a technique for the manipulation of arbitrary non-unitary combinations on a quantum computer.
arXiv Detail & Related papers (2020-04-14T23:15:39Z)
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.