Another paper, demonstrating some interesting techniques for overcoming practical problems in using D-Wave hardware. (Apologies Diana for the continuing lack of interpretation of these results :-) ). These techniques were applied to Low Density Parity Check problems.
Discrete optimization using quantum annealing on sparse Ising models
- 1D-Wave Systems, Burnaby, BC, Canada
- 2Department of Computer Science, Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, USA
This paper discusses techniques for solving discrete optimization problems using quantum annealing. Practical issues likely to affect the computation include precision limitations, finite temperature, bounded energy range, sparse connectivity, and small numbers of qubits. To address these concerns we propose a way of finding energy representations with large classical gaps between ground and first excited states, efficient algorithms for mapping non-compatible Ising models into the hardware, and the use of decomposition methods for problems that are too large to fit in hardware. We validate the approach by describing experiments with D-Wave quantum hardware for low density parity check decoding with up to 1000 variables.
More science on data from the D-Wave One system at USC.
Reexamining classical and quantum models for the D-Wave One processor
(Submitted on 12 Sep 2014)
We revisit the evidence for quantum annealing in the D-Wave One device (DW1) based on the study of random Ising instances. Using the probability distributions of finding the ground states of such instances, previous work found agreement with both simulated quantum annealing (SQA) and a classical rotor model. Thus the DW1 ground state success probabilities are consistent with both models, and a different measure is needed to distinguish the data and the models. Here we consider measures that account for ground state degeneracy and the distributions of excited states, and present evidence that for these new measures neither SQA nor the classical rotor model correlate perfectly with the DW1 experiments. We thus provide evidence that SQA and the classical rotor model, both of which are classically efficient algorithms, do not satisfactorily explain all the DW1 data. A complete model for the DW1 remains an open problem. Using the same criteria we find that, on the other hand, SQA and the classical rotor model correlate closely with each other. To explain this we show that the rotor model can be derived as the semiclassical limit of the spin-coherent states path integral. We also find differences in which set of ground states is found by each method, though this feature is sensitive to calibration errors of the DW1 device and to simulation parameters.
A new paper from users of the D-Wave Two at USC. Here’s the abstract:
We demonstrate that the performance of a quantum annealer on hard random Ising optimization problems can be substantially improved using quantum annealing correction (QAC). Our error correction strategy is tailored to the D-Wave Two device. We find that QAC provides a statistically significant enhancement in the performance of the device over a classical repetition code, improving as a function of problem size as well as hardness. Moreover, QAC provides a mechanism for overcoming the precision limit of the device, in addition to correcting calibration errors. Performance is robust even to missing qubits. We present evidence for a constructive role played by quantum effects in our experiments by contrasting the experimental results with the predictions of a classical model of the device. Our work demonstrates the importance of error correction in appropriately determining the performance of quantum annealers.
A great blog post by Alexei Andreev, a long-time investor in D-Wave and PhD in condensed matter physics. Excellent insights, recommended reading!
Hi everyone! Sorry for being silent for a while. Working. :-)
Two interesting papers appeared on the arxiv this week, both from people at Ames working on their D-Wave Two.
First: A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems
Second: Quantum Optimization of Fully-Connected Spin Glasses
A new paper published today in Phys Rev X. It demonstrates eight qubit entanglement in a D-Wave processor, which I believe is a world record for solid state qubits. This is an exceptional paper with an important result. The picture to the left measures a quantity that, if negative, verifies entanglement. The quantity s is the time — the quantum annealing procedure goes from the left to the right, with entanglement maximized near the area where the energy gap is smallest.
Here is the abstract:
Entanglement lies at the core of quantum algorithms designed to solve problems that are intractable by classical approaches. One such algorithm, quantum annealing (QA), provides a promising path to a practical quantum processor. We have built a series of architecturally scalable QA processors consisting of networks of manufactured interacting spins (qubits). Here, we use qubit tunneling spectroscopy to measure the energy eigenspectrum of two- and eight-qubit systems within one such processor, demonstrating quantum coherence in these systems. We present experimental evidence that, during a critical portion of QA, the qubits become entangled and entanglement persists even as these systems reach equilibrium with a thermal environment. Our results provide an encouraging sign that QA is a viable technology for large scale quantum computing.
Here’s a neat paper from UCL and USC researchers ruling out several classical models for the D-Wave Two, including the SSSV model (“…the SSSV model can be rejected as a classical model for the D-Wave device”), and giving indirect evidence for up to 40 qubit entanglement in a real computer processor.