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Science

Benchmarking quantum simulation

I. S. Osborne

Published Apr 9, 2015• 0 citations
Publisher

Abstract

Quantum Computing
Finding a solution to a problem often amounts to an optimization problem and thus can be recast in terms of the lowest-energy state of a system. To find such ground states, mathematical methods based on annealing were developed. To reach the ground state more quickly than with the
DOI
10.1126/science.348.6231.196-k

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Simulated Annealing (SA) is often used to provide satisfactory solutions to solving combinatorial problems. However, the SA is limited by its tendency to get stuck in local minima, which often prevents the global optimal solution from being reached for large or very complex problems. To overcome this obstacle, we present a new method inspired by the properties of degenerate quantum systems, named Degenerate Quantum Simulated Annealing (SA-DQS). This innovative approach exploits the coexistence of several quantum states of the same energy; this multiplicity of states is advantageous because it allows to finding of several solutions with identical energies, thus increasing the chances of discovering and building a promising solution among them, and of getting out of the traps of local minima more efficiently. The traveling salesman problem (TSP) is used as an application benchmark to validate the effectiveness of SA-DQS. Compared to standard simulated annealing (SA-S), SA-DQS showed superior performance on multiple instances of TSP. On average, the solutions provided by SA-DQS are equal to or closer to the optimum known in TSPLIB, in addition, it also offers a shorter computation time compared to that of other heuristic methods.
Combinatorial optimization problems are frequently classified as NP-hard, which means that the time needed to find the optimal solution generally increases exponentially with the problem size. However, the existing research gap highlights the necessity to develop combinatorial optimization methods capable of scaling to large problems while maintaining low solution latency. In this paper, we introduce stochastic simulated quantum annealing (SSQA) for fast solution of combinatorial optimization problems. SSQA is designed based on stochastic computing, which can simulate quantum annealing (QA) by using multiple replicas of spins (probabilistic bits) in classical computing. The use of stochastic computing leads to an efficient parallel spin-state update algorithm, enabling quick search for a solution around the global minimum energy. Therefore, SSQA realizes quantum-like annealing for large-scale problems and can handle fully connected models in combinatorial optimization, unlike QA. The proposed method is evaluated in MATLAB on graph isomorphism problems, which are typical combinatorial optimization problems. It can handle a 100-times larger problem size compared to QA and a 25-times larger problem size compared to a traditional SA method, respectively, for similar convergence probabilities. The time to solution of SSQA is 11.5 times and 423 times smaller than that of the conventional stochastic simulated annealing method and traditional SA, respectively, when solving a 2,025-node combinatorial optimization problem.
The solution of large-scale eigenvalue problems is crucial in nuclear many-body theory, where Hamiltonian matrices often reach extremely large dimensions. Quantum computing opens new perspectives for addressing such demanding problems. Although the Quantum Phase Estimation algorithm offers, in principle, a systematic route to matrix diagonalization, its practical deployment demands levels of coherence and error correction that current quantum hardware cannot yet support. A viable near-term strategy is instead to exploit quantum annealing, which enables the recasting of eigenvalue problems into quadratic unconstrained binary optimization formulations that can be addressed by existing annealing-based processors. Here, we propose a hybrid quantum-classical algorithm that combines quantum annealing and classical deflation to iteratively extract the full eigenspectrum of both standard and generalized eigenvalue problems. We benchmark this method on eigenvalue problems arising from the Equation of Motion Phonon Method performing calculations on real quantum hardware. Our approach illustrates the capabilities and limitations of near-term quantum devices in addressing nuclear eigenvalue problems.