Traffic flow optimization using a quantum annealer

Neukart, Florian; Compostella, G.; Seidel, Christian; Dollen, David Von; Yarkoni, Sheir; Parney, Bob

doi:10.48550/arxiv.1708.01625

Cited by 11 publications

(25 citation statements)

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“…Side conditions to optimization problems are crucial for many problems that are encountered in science, technology, and industry, ranging from scheduling problems to quantum chemistry [1][2][3][4][5][6][7][8]. Developing algorithms that solve these problems is notoriously harder compared to their unconstrained counterparts.…”

Section: Introductionmentioning

confidence: 99%

Parity Quantum Optimization: Encoding Constraints

Maike¹,

Ender²,

Javanmard³

et al. 2021

Preprint

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Constraints make hard optimization problems even harder to solve on quantum devices because they are implemented with large energy penalties and additional qubit overhead. The parity mapping, which has been introduced as an alternative to the spin encoding, translates the problem to a representation using only parity variables that encodes products of spin variables. In combining exchange interaction and single spin flip terms in the parity representation, constraints on sums and products of arbitrary k -body terms can be implemented without additional overhead in two-dimensional quantum systems.

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Section: Introductionmentioning

confidence: 99%

Parity Quantum Optimization: Encoding Constraints

Maike¹,

Ender²,

Javanmard³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…From the many applications implemented in quantum annealers (see, for example, Refs. [17,[23][24][25][26][27][28]), fault diagnosis has been one of the leading candidates to benchmark the performance of D-Wave devices as optimizers [26,29]. From the range of circuit model-based fault-diagnosis problems [30] we restrict our attention here to combinational circuit fault diagnosis (CCFD), which in contrast to sequential circuits, does not have any memory components and the output is entirely determined by the present inputs.…”

Section: Introductionmentioning

confidence: 99%

Readiness of Quantum Optimization Machines for Industrial Applications

et al. 2019

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There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current classical optimization algorithms implemented on CMOS technologies. The benchmarking of these devices has been controversial. Initially, random spin-glass problems were used, however, these were quickly shown to be not well suited to detect any quantum speedup. Subsequently, benchmarking shifted to carefully crafted synthetic problems designed to highlight the quantum nature of the hardware while (often) ensuring that classical optimization techniques do not perform well on them. Even worse, to date a true sign of improved scaling with the number of problem variables remains elusive when compared to classical optimization techniques. Here, we analyze the readiness of quantum annealing machines for real-world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimization techniques, both classical and quantum alike. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers. We find that the instances generated from real data in multiplier circuits are harder than other representative random spin-glass benchmarks with a comparable number of variables. Although our results show that transverse-field quantum annealing is outperformed by state-of-the-art classical optimization algorithms, these benchmark instances are hard and small in the size of the input, therefore representing the first industrial application ideally suited for testing near-term quantum annealers and other quantum algorithmic strategies for optimization problems.

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“…where x is a vector of binary variables of size N , and Q is an N × N real-valued matrix describing the relationship between the variables. Given the matrix Q, finding binary variable assignments to minimize the objective function in Equation 2 is equivalent to minimizing an Ising model, a known NP-hard problem [16,17].…”

Section: Introductionmentioning

confidence: 99%

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mentioning

confidence: 99%

Quantum-Enhanced Reinforcement Learning for Finite-Episode Games with Discrete State Spaces

et al. 2018

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Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems [1], have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Here, we present a way to partially embed both Monte Carlo policy iteration for finding an optimal policy on random observations, as well as how to embed n sub-optimal state-value functions for approximating an improved state-value function given a policy for finite horizon games with discrete state spaces on a D-Wave 2000Q quantum processing unit (QPU). We explain how both problems can be expressed as a quadratic unconstrained binary optimization (QUBO) problem, and show that quantum-enhanced Monte Carlo policy evaluation allows for finding equivalent or better state-value functions for a given policy with the same number episodes compared to a purely classical Monte Carlo algorithm. Additionally, we describe a quantum-classical policy learning algorithm. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical policy evaluation algorithm.

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Traffic flow optimization using a quantum annealer

Cited by 11 publications

References 0 publications

Parity Quantum Optimization: Encoding Constraints

Parity Quantum Optimization: Encoding Constraints

Readiness of Quantum Optimization Machines for Industrial Applications

Quantum-Enhanced Reinforcement Learning for Finite-Episode Games with Discrete State Spaces

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