scholarly journals Filtering variational quantum algorithms for combinatorial optimization

2021 ◽  
Author(s):  
David Amaro ◽  
Carlo Modica ◽  
Matthias Rosenkranz ◽  
Mattia Fiorentini ◽  
Marcello Benedetti ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Optimal Solution ◽  
Quantum Computers ◽  
Trapped Ion ◽  
Quantum Processor ◽  
Computational Advantage ◽  
Better Than

Abstract Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the Filtering Variational Quantum Eigensolver (F-VQE) which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the Quantum Approximate Optimization Algorithm (QAOA). We also demonstrate the experimental feasibility of our algorithms on a Honeywell trapped-ion quantum processor.

scholarly journals Application of Quantum Computing to Biochemical Systems: A Look to the Future

2020 ◽  
Vol 8 ◽  
Author(s):  
Hai-Ping Cheng ◽  
Erik Deumens ◽  
James K. Freericks ◽  
Chenglong Li ◽  
Beverly A. Sanders
Keyword(s):  
Quantum Computing ◽  
Physical System ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Active Regions ◽  
Quantum Computers ◽  
The Future ◽  
Biochemical Systems ◽  
Near Term ◽  
Different Levels

Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.

Hidden symmetry detection on a quantum computer

2007 ◽  
Vol 7 (1&2) ◽  
pp. 83-92
Author(s):  
R. Schutzhold ◽  
W.G. Unruh
Keyword(s):  
Quantum Computer ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Self Similarity ◽  
Hidden Subgroup Problem ◽  
Hidden Symmetry ◽  
Speed Up ◽  
Subgroup Problem ◽  
Discrete Scale

The fastest quantum algorithms (for the solution of classical computational tasks) known so far are basically variations of the hidden subgroup problem with {$f(U[x])=f(x)$}. Following a discussion regarding which tasks might be solved efficiently by quantum computers, it will be demonstrated by means of a simple example, that the detection of more general hidden (two-point) symmetries {$V\{f(x),f(U[x])\}=0$} by a quantum algorithm can also admit an exponential speed-up. E.g., one member of this class of symmetries {$V\{f(x),f(U[x])\}=0$} is discrete self-similarity (or discrete scale invariance).

scholarly journals Quantum Algorithms for Solving Ordinary Differential Equations via Classical Integration Methods

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 502
Author(s):  
Benjamin Zanger ◽  
Christian B. Mendl ◽  
Martin Schulz ◽  
Martin Schreiber
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
High Order ◽  
Quantum Computers ◽  
Integration Methods ◽  
Linear Ordinary Differential Equations ◽  
Legendre Collocation Method

Identifying computational tasks suitable for (future) quantum computers is an active field of research. Here we explore utilizing quantum computers for the purpose of solving differential equations. We consider two approaches: (i) basis encoding and fixed-point arithmetic on a digital quantum computer, and (ii) representing and solving high-order Runge-Kutta methods as optimization problems on quantum annealers. As realizations applied to two-dimensional linear ordinary differential equations, we devise and simulate corresponding digital quantum circuits, and implement and run a 6th order Gauss-Legendre collocation method on a D-Wave 2000Q system, showing good agreement with the reference solution. We find that the quantum annealing approach exhibits the largest potential for high-order implicit integration methods. As promising future scenario, the digital arithmetic method could be employed as an "oracle" within quantum search algorithms for inverse problems.

scholarly journals Quantum Algorithms for Simulating the Lattice Schwinger Model

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 306 ◽  
Author(s):  
Alexander F. Shaw ◽  
Pavel Lougovski ◽  
Jesse R. Stryker ◽  
Nathan Wiebe
Keyword(s):  
Quantum Electrodynamics ◽  
Quantum Computer ◽  
Fault Tolerant ◽  
Quantum Algorithms ◽  
Gauge Field Theories ◽  
Coupling Constant ◽  
Schwinger Model ◽  
Pair Density ◽  
Trotter Formula ◽  
Better Than

The Schwinger model (quantum electrodynamics in 1+1 dimensions) is a testbed for the study of quantum gauge field theories. We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In particular, we perform a tight analysis of low-order Trotter formula simulations of the Schwinger model, using recently derived commutator bounds, and give upper bounds on the resources needed for simulations in both scenarios. In lattice units, we find a Schwinger model on N/2 physical sites with coupling constant x−1/2 and electric field cutoff x−1/2Λ can be simulated on a quantum computer for time 2xT using a number of T-gates or CNOTs in O~(N3/2T3/2xΛ) for fixed operator error. This scaling with the truncation Λ is better than that expected from algorithms such as qubitization or QDRIFT. Furthermore, we give scalable measurement schemes and algorithms to estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable–the mean pair density. Finally, we bound the root-mean-square error in estimating this observable via simulation as a function of the diamond distance between the ideal and actual CNOT channels. This work provides a rigorous analysis of simulating the Schwinger model, while also providing benchmarks against which subsequent simulation algorithms can be tested.

scholarly journals Solving Combinatorial Optimization Problems on Quantum Computers

2020 ◽  
pp. 5-13
Author(s):  
Vyacheslav Korolyov ◽  
Oleksandr Khodzinskyi
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Quantum Computer ◽  
Independent Set ◽  
Cloud Services ◽  
Quantum Computers ◽  
Maximal Independent Set ◽  
Maximum Independent Set ◽  
Combinatorial Optimization Problems ◽  
D Wave

Introduction. Quantum computers provide several times faster solutions to several NP-hard combinatorial optimization problems in comparison with computing clusters. The trend of doubling the number of qubits of quantum computers every year suggests the existence of an analog of Moore's law for quantum computers, which means that soon they will also be able to get a significant acceleration of solving many applied large-scale problems. The purpose of the article is to review methods for creating algorithms of quantum computer mathematics for combinatorial optimization problems and to analyze the influence of the qubit-to-qubit coupling and connections strength on the performance of quantum data processing. Results. The article offers approaches to the classification of algorithms for solving these problems from the perspective of quantum computer mathematics. It is shown that the number and strength of connections between qubits affect the dimensionality of problems solved by algorithms of quantum computer mathematics. It is proposed to consider two approaches to calculating combinatorial optimization problems on quantum computers: universal, using quantum gates, and specialized, based on a parameterization of physical processes. Examples of constructing a half-adder for two qubits of an IBM quantum processor and an example of solving the problem of finding the maximum independent set for the IBM and D-wave quantum computers are given. Conclusions. Today, quantum computers are available online through cloud services for research and commercial use. At present, quantum processors do not have enough qubits to replace semiconductor computers in universal computing. The search for a solution to a combinatorial optimization problem is performed by achieving the minimum energy of the system of coupled qubits, on which the task is mapped, and the data are the initial conditions. Approaches to solving combinatorial optimization problems on quantum computers are considered and the results of solving the problem of finding the maximum independent set on the IBM and D-wave quantum computers are given. Keywords: quantum computer, quantum computer mathematics, qubit, maximal independent set for a graph.

scholarly journals Structure optimization for parameterized quantum circuits

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 391
Author(s):  
Mateusz Ostaszewski ◽  
Edward Grant ◽  
Marcello Benedetti
Keyword(s):  
Ground State ◽  
Heisenberg Model ◽  
Quantum Computer ◽  
Structure Optimization ◽  
Hydrogen Gas ◽  
Quantum Circuits ◽  
Quantum Computers ◽  
Computational Overhead ◽  
Parameter Values ◽  
Better Than

We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimization perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers. We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer.

Multivariant Optimization Algorithm for the 0-1 Knapsack Problem

2014 ◽  
Vol 556-562 ◽  
pp. 3514-3518
Author(s):  
Lan Juan Liu ◽  
Bao Lei Li ◽  
Qin Hu Zhang ◽  
Dan Jv Lv ◽  
Xin Ling Shi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Global Search ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Better Than

In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.

scholarly journals Quantum Computing Concepts with Deutsch Jozsa Algorithm

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Poornima Aradyamath ◽  
Naghabhushana N M ◽  
Rohitha Ujjinimatad
Keyword(s):  
Fourier Transform ◽  
Quantum Computing ◽  
Quantum Computation ◽  
Mathematical Description ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Quantum Computers ◽  
Quantum Fourier Transform ◽  
Basic Concepts ◽  
Classical Computer

In this paper, we briefly review the basic concepts of quantum computation,  entanglement,  quantum cryptography and quantum fourier  transform.   Quantum algorithms like Deutsch Jozsa, Shor’s   factorization and Grover’s data search are developed using fourier  transform  and quantum computation concepts to build quantum computers.  Researchers are finding a way to build quantum computer that works more efficiently than classical computer.  Among the  standard well known  algorithms  in the field of quantum computation  and communication we  describe  mathematically Deutsch Jozsa algorithm  in detail for  2  and 3 qubits.  Calculation of balanced and unbalanced states is shown in the mathematical description of the algorithm.

scholarly journals Quantum Computing in the Biomedical Sciences; A Brief Introduction into Concepts and Applications

2019 ◽  
Vol 12 (3) ◽  
pp. 104
Author(s):  
Casper van der Kerk ◽  
Attila Csala ◽  
Aeilko H. Zwinderman
Keyword(s):  
Image Processing ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Quantum Computers ◽  
Biomedical Sciences ◽  
Quantum Bits ◽  
Early Stages ◽  
Made In

Quantum computing is a field that aims to exploit the principles of superposition and entanglement to perform computations. By using quantum bits (qubits) a quantum computer is able to perform certain tasks more efficiently when compared to classical computers. While applied quantum computing is still in its early stages, quantum algorithms on simulated quantum computers have already been applied to certain problems in epidemics modeling and image processing. Furthermore, companies like Google and IBM continue to develop new quantum computers with an increasing number of qubits. While much progress has been made in the recent years, the so called ”quantum supremacy”has not yet been achieved, and quantum computing appears to be still unsuitable for most applications in biomedical sciences.

scholarly journals Nearest centroid classification on a trapped ion quantum computer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sonika Johri ◽  
Shantanu Debnath ◽  
Avinash Mocherla ◽  
Alexandros SINGK ◽  
Anupam Prakash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Computer ◽  
State Of The Art ◽  
Synthetic Data ◽  
Quantum Computers ◽  
Trapped Ion ◽  
Quantum Machine Learning ◽  
Real World Applications ◽  
Promising Area ◽  
Centroid Classifier

AbstractQuantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.

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