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AFAIK D-Wave primarily advertises their machines as tools for solving problems with classical input, i.e. when the Hamiltonian to be minimized is a function of $Z$s.

Can one use their machines in application to simulating Hamiltonians arising in quantum chemistry and, more generally, in quantum physics?

(I probably made here some wrong / misleading points, please feel free to comment on any.)

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Adiabatic quantum computation, the principle on which quantum annealing is based, is universal, so theory quantum annealing with arbitrary Hamiltonians could be used for simulation (up to the error introduced by annealing).

However, D-Wave's devices only have 2-local interactions, which means they only support problems that can be written as QUBOs (or equivalently, Ising models). If you can write your simulation in such a form, you're good to go. That may be hard or impossible.

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The D-Wave quantum computer leverages quantum dynamics to accelerate and enable new methods for solving complex problems. People are building quantum applications for a broad spectrum of industries and use cases such as logistics, financial services, drug discovery, materials sciences, scheduling, fault detection, mobility, and supply chain management. They are also used in academia, not as much as Qiskit though. Here are some examples:

  1. Quantum Molecular Unfolding : Molecular Docking is an important step of the drug discovery process which aims at calculating the preferred position and shape of one molecule to a second when they are bound to each other. Using D-Wave's quantum system, results and performances are compared with state of art classical solvers.Click for the paper here
  2. Optimization of Complex Systems :In this talk, Steven Abel, Professor at Durham University, discusses recent developments in the implementation of complex physical systems on quantum annealers. These include systems that allow quantum tunnelling and barrier penetration across specified potentials. The results we find are in accord with those predicted theoretically showing that a quantum annealer is a genuine quantum system that can be used as a quantum laboratory. More generally he shows how his techniques can generally be used to optimise arbitrary potentials in several dimensions. He compares classical with quantum optimisation methods and finds a qualitative improvement over classical techniques.Link to the video here
  3. Magnetic Monopole Kinetics in Qubit Spin Ice :An artificial spin ice is a manufactured physical system defined by interacting binary variables. Interesting physical properties arise when all constraints defining the interactions cannot be satisfied, namely the system is frustrated. Researchers from Los Alamos National Laboratory (LANL) presents the first realization of artificial spin ice in a lattice of coupled qubits to recreate physical phenomena found in actual magnetic materials.Link to the video here
  4. Lattice Qauge Theory :Lattice gauge theory is an essential tool for strongly interacting non-Abelian elds, such as those in quantum chromodynamics where lattice results have been of central importance for several decades. In this work, researchers from York University implement SU(2) pure gauge theory on a quantum annealer for lattices comprising a few plaquettes in a row with a periodic boundary condition.Link to the video here
  5. mRNA Codon Optimization on Quantum Computers :Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP- hard combinatorial optimization problem. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. An adiabatic quantum computer (AQC) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The AQC is found to be competitive in identifying optimal solutions and future generations of AQCs may be able to outperform classical GAs.Paper here

and many other examples too.

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