Global Warming: Temperature Estimation in Annealers

Raymond, Jack; Yarkoni, Sheir; Andriyash, Evgeny

doi:10.3389/fict.2016.00023

Cited by 63 publications

(57 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also important to devise more controlled experiments that allow us to isolate the different phenomena involved. Two months after submission of this manuscript, we learned of ongoing work addressing some of these issues and putting forward other temperature estimation techniques [63]. Finally, an investigation on the bias and variance of our effective temperature estimator is an interesting theoretical question that we expect to address in future work.…”

Section: Discussionmentioning

confidence: 99%

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

et al. 2016

View full text Add to dashboard Cite

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an instance-dependent effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here.

show abstract

Section: Discussionmentioning

confidence: 99%

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Previous experiments have shown how these QPUs implement quantum annealing and that the quantum bits (qubits) in the QPU remain coherent and entangled during the annealing process (Lanting et al, 2014). It has also been shown how the quantum properties of qubits play a role in the computation of solutions in both sampling and optimization tasks Perdomo-Ortiz et al, 2015;Rieffel et al, 2015;Venturelli et al, 2015a,b;Denchev et al, 2016;Los Alamos National Laboratory, 2016;Raymond et al, 2016). The QPU is designed to solve quadratic unconstrained binary optimization (QUBO) problems, where each qubit represents a variable, and couplers between qubits represent the costs associated with qubit pairs.…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Optimization Using a Quantum Annealer

et al. 2017

Self Cite

View full text Add to dashboard Cite

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 processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem.

show abstract

“…In RRS, it is assumed the temperature of the thermal sampler is known, and this may not always be the case; for example, in quantum annealers different sets of problems may be sampled at effectively different temperatures [13][14][15]. One would first therefore need to estimate the temperature [2,[16][17][18][19]. Since in general one will not obtain the exact temperature, a further study of importance is how the performance of RRS depends on noise in the temperature parameter.…”

Section: Resultsmentioning

confidence: 99%

Perils of embedding for sampling problems

Marshall

Gioacchino

Rieffel

2020

Phys. Rev. Research

View full text Add to dashboard Cite

Sampling from certain distributions is a prohibitively challenging task. Special-purpose hardware such as quantum annealers may be able to more efficiently sample from such distributions, which could find application in optimization, sampling tasks, and machine learning. Current quantum annealers impose certain constraints on the structure of the cost Hamiltonian due to the connectivity of the individual processing units. This means that in order to solve many problems of interest, one is required to embed the native graph into the hardware graph. The effect of embedding for sampling is more pronounced than for optimization; for optimization one is just concerned with mapping ground states to ground states, whereas for sampling one needs to consider states at all energies. We argue that as the problem size grows, or the embedding size grows, the chance of a sample being in the logical subspace is exponentially suppressed. It is therefore necessary to construct post-processing techniques to evade this exponential sampling overhead, techniques that project from the embedded distribution one is physically sampling from, back to the logical space of the native problem. We show that the most naive (and most common) projection technique, known as majority vote, can fail quite spectacularly at preserving distribution properties. We show that, even with care, one cannot avoid bias in the general case. Specifically we prove through a simple and generic (counter) example that no post-processing technique, under reasonable assumptions, can avoid biasing the statistics in the general case. On the positive side, we demonstrate a new resampling technique for performing this projection which substantially out-performs majority vote and may allow one to much more effectively sample from graphs of interest.

show abstract

Global Warming: Temperature Estimation in Annealers

Cited by 63 publications

References 51 publications

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

Traffic Flow Optimization Using a Quantum Annealer

Perils of embedding for sampling problems

Contact Info

Product

Resources

About