Towards algorithm-free physical equilibrium model of computing
- URL: http://arxiv.org/abs/2112.00006v1
- Date: Tue, 30 Nov 2021 09:48:39 GMT
- Title: Towards algorithm-free physical equilibrium model of computing
- Authors: Seyed Mousavi
- Abstract summary: A new model of computing is proposed to replace the sequential paradigm of algorithms with inherent parallelism of physical processes.
We construct physical systems whose equilibrium states correspond to the desired solutions and let them evolve to search for the solutions.
The main requirements of the model are identified and quantum circuits are proposed for its potential implementation.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Our computers today, from sophisticated servers to small smartphones, operate
based on the same computing model, which requires running a sequence of
discrete instructions, specified as an algorithm. This sequential computing
paradigm has not yet led to a fast algorithm for an NP-complete problem despite
numerous attempts over the past half a century. Unfortunately, even after the
introduction of quantum mechanics to the world of computing, we still followed
a similar sequential paradigm, which has not yet helped us obtain such an
algorithm either. Here a completely different model of computing is proposed to
replace the sequential paradigm of algorithms with inherent parallelism of
physical processes. Using the proposed model, instead of writing algorithms to
solve NP-complete problems, we construct physical systems whose equilibrium
states correspond to the desired solutions and let them evolve to search for
the solutions. The main requirements of the model are identified and quantum
circuits are proposed for its potential implementation.
Related papers
- Solving the Independent Domination Problem by Quantum Approximate Optimization Algorithm [0.5919433278490629]
Independent Domination Problem (IDP) has practical implications in various real-world scenarios.
Existing classical algorithms for IDP are plagued by high computational complexity.
This paper introduces a Quantum Approximate Optimization (QAOA)-based approach to address the IDP.
arXiv Detail & Related papers (2024-10-22T17:49:00Z) - Deep Equilibrium Algorithmic Reasoning [18.651333116786084]
We study neurally solving algorithms from a different perspective.
Since the algorithm's solution is often an equilibrium, it is possible to find the solution directly by solving an equilibrium equation.
Our approach requires no information on the ground-truth number of steps of the algorithm, both during train and test time.
arXiv Detail & Related papers (2024-10-19T10:40:55Z) - The Deep Equilibrium Algorithmic Reasoner [20.375241527453447]
We show that graph neural networks (GNNs) can learn to execute classical algorithms.
We conjecture and empirically validate that one can train a network to solve algorithmic problems by directly finding the equilibrium.
arXiv Detail & Related papers (2024-02-09T14:46:50Z) - Addressing Quantum's "Fine Print": State Preparation and Information
Extraction for Quantum Algorithms and Geologic Fracture Networks [0.0]
This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms.
Our approach to addressing each is consistent with an overall exponential speed-up.
arXiv Detail & Related papers (2023-10-03T23:02:54Z) - The Clock and the Pizza: Two Stories in Mechanistic Explanation of
Neural Networks [59.26515696183751]
We show that algorithm discovery in neural networks is sometimes more complex.
We show that even simple learning problems can admit a surprising diversity of solutions.
arXiv Detail & Related papers (2023-06-30T17:59:13Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - Implementable Hybrid Quantum Ant Colony Optimization Algorithm [0.0]
We propose a new hybrid quantum algorithm to produce approximate solutions for NP-hard problems.
We develop an improved algorithm that can be truly implemented on near-term quantum computers.
The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.
arXiv Detail & Related papers (2021-07-08T13:50:51Z) - 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) - Space-efficient binary optimization for variational computing [68.8204255655161]
We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem.
We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
arXiv Detail & Related papers (2020-09-15T18:17:27Z)
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.