Yes. That is my polemical reply to the titular question in Travis Norsens self-styled polemical response to Howard Wisemans recent paper. Less polemically, I am pleased to see that on two of my positions --- that Bells 1964 theorem is different from
Bells 1976 theorem, and that the former does not include Bells one-paragraph heuristic presentation of the EPR argument --- Norsen has made significant concessions. In his response, Norsen admits that Bells recapitulation of the EPR argument in [the relevant] paragraph leaves something to be desired, that it disappoints and is problematic. Moreover, Norsen makes other statements that imply, on the face of it, that he should have no objections to the title of my recent paper (The Two Bells Theorems of John Bell). My principle aim in writing that paper was to try to bridge the gap between two interpretational camps, whom I call operationalists and realists, by pointing out that they use the phrase Bells theorem to mean different things: his 1964 theorem (assuming locality and determinism) and his 1976 theorem (assuming local causality), respectively. Thus, it is heartening that at least one person from one side has taken one step on my bridge. That said, there are several issues of contention with Norsen, which we (the two authors) address after discussing the extent of our agreement with Norsen. The most significant issues are: the indefiniteness of the word locality prior to 1964; and the assumptions Einstein made in the paper quoted by Bell in 1964 and their relation to Bells theorem.
In this article, we show how to map a sampling of the hardest artificial intelligence problems in space exploration onto equivalent Ising models that then can be attacked using quantum annealing implemented in D-Wave machine. We overview the existing
results as well as propose new Ising model implementations for quantum annealing. We review supervised and unsupervised learning algorithms for classification and clustering with applications to feature identification and anomaly detection. We introduce algorithms for data fusion and image matching for remote sensing applications. We overview planning problems for space exploration mission applications and algorithms for diagnostics and recovery with applications to deep space missions. We describe combinatorial optimization algorithms for task assignment in the context of autonomous unmanned exploration. Finally, we discuss the ways to circumvent the limitation of the Ising mapping using a blackbox approach based on ideas from probabilistic computing. In this article we describe the architecture of the D-Wave One machine and report its benchmarks. Results on random ensemble of problems in the range of up to 96 qubits show improved scaling for median core quantum annealing time compared with classical algorithms; whether this scaling persists for larger problem sizes is an open question. We also review previous results of D-Wave One benchmarking studies for solving binary classification problems with a quantum boosting algorithm which is shown to outperform AdaBoost. We review quantum algorithms for structured learning for multi-label classification and introduce a hybrid classical/quantum approach for learning the weights. Results of D-Wave One benchmarking studies for learning structured labels on four different data sets show a better performance compared with an independent Support Vector Machine approach with linear kernel.
