Programming a D-wave annealing-based quantum computer: tools and techniques (pp0721-0759)
          Scott Pakin and Steven P. Reinhardt
Quantum annealing is a form of quantum computing that exploits quantum effects to probabilistically solve a specific, NP-hard problem: finding the ground state of a classical, Ising-model Hamiltonian.  Because physical quantum annealers are already available, there exists the pressing question of how to \emph{program} such systems.  That is, how can one map a computational problem into the coefficients of an Ising-model Hamiltonian for solution by quantum-annealing hardware? In this article, we address that question primarily from a practical standpoint.  We survey extant software tools intended for programming \dw\ annealing-based quantum processors and examine the   programming model and solution technique promoted by each tool in an attempt to showcase the variety of contemporary approaches to solving computationally challenging problems on an existing annealing-based quantum computer.

Span program for non-binary functions (pp0760-0792)
          Salman Beigi and Leila Taghavi
Span programs characterize the quantum query complexity of \emph{binary} functions $f:\{0,\ldots,\ell\}^n \to \{0,1\}$ up to a constant factor. In this paper we generalize the notion of span programs for functions with \emph{non-binary} input/output alphabets $f: [\ell]^n \to [m]$.  We show that \emph{non-binary span program} characterizes the quantum query complexity of any such function up to a constant factor. We argue that this non-binary span program is indeed the generalization of its binary counterpart. We also generalize the notion of span programs for a special class of relations. Learning graphs provide another tool for designing quantum query algorithms for binary functions. In this paper, we also generalize this tool for non-binary functions,  and as an application of our non-binary span program show that any non-binary learning graph gives an upper bound on the quantum query complexity.

Impossibility of blind quantum sampling for classical client (pp0793-0806)
          Tomoyuki Morimae, Harumichi Nishimura, Yuki Takeuchi, and Seiichiro Tani
Blind quantum computing enables a client, who can only generate or measure single-qubit states, to delegate quantum computing to a remote quantum server in such a way that the input, output, and program are hidden from the server. It is an open problem whether a completely classical client can delegate quantum computing blindly (in the information theoretic sense). In this paper, we show that if a completely classical client can blindly delegate sampling of subuniversal models, such as the DQC1 model and the IQP model, then the polynomial-time hierarchy collapses to the third level. Our delegation protocol is the one where the client first sends a polynomial-length bit string to the server and then the server returns a single bit to the client. Generalizing the no-go result to more general setups is an open problem.