text: 80,8,1212440767077244929,313814795,M. Sohaib Alam,"My new paper, from the last decade, explores the feasibility of using reinforcement learning for quantum programming, particularly state preparation and gate compilation. (1/n) <LINK> It does so by forming finite MDPs for single-qubit state prep and gate compilation, exactly solving for these cases using policy iteration, and comparing against brute-force calculations, finding that the two can be made to agree. (2/n) This shows that the reinforcement learning notion of optimality can be made to agree with our intuitive notion of ""optimality"" in the sense of the shortest possible quantum circuit to prep a state, or compile a gate, up to some accuracy (3/n) While policy iteration/dynamic programming is fine to use for the 1q problems set up here, it won't be practical for larger q scenarios, but it does mean that you could throw reinforcement learning techniques at the problem, and hope to find optimally short circuits (4/n) My guess is similar considerations would play a big part for larger q states, in particular choice of good coordinate systems for state and action spaces, the form and scale of the discretization etc (5/n) I suppose one way to express the results in plainer but drastically simplifying words would be that a robot could be trained through rewards to produce the shortest possible quantum circuit necessary to prepare a state or compile a gate, up to some accuracy. (n/n) @razaa_aasad Yep, and it's duly acknowledged in the references! Drawing from your paper, one could similarly hope that RL would discover close-to-optimal sequences for state prep + gate compilation at the logical gate level. @razaa_aasad As my new paper shows, exact-optimality, i.e. shortest length circuits, is a feature (upto caveats) of the underlying MDP for the single-qubit case. RL can therefore be expected to find at least close-to-optimal gate sequences for state prep/compilation in the n-qubit case.",https://arxiv.org/abs/1912.12002,"Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming. The underlying assumption is that those tasks could be modeled as Markov Decision Processes (MDPs). Here, we investigate the feasibility of this assumption by exploring its consequences for two of the simplest tasks in quantum programming: state preparation and gate compilation. By forming discrete MDPs, focusing exclusively on the single-qubit case, we solve for the optimal policy exactly through policy iteration. We find optimal paths that correspond to the shortest possible sequence of gates to prepare a state, or compile a gate, up to some target accuracy. As an example, we find sequences of H and T gates with length as small as 11 producing ~99% fidelity for states of the form (HT)^{n} |0> with values as large as n=10^{10}. This work provides strong evidence that reinforcement learning can be used for optimal state preparation and gate compilation for larger qubit spaces. ",Quantum Logic Gate Synthesis as a Markov Decision Process,8,"['My new paper, from the last decade, explores the feasibility of using reinforcement learning for quantum programming, particularly state preparation and gate compilation. (1/n)\n\n<LINK>', 'It does so by forming finite MDPs for single-qubit state prep and gate compilation, exactly solving for these cases using policy iteration, and comparing against brute-force calculations, finding that the two can be made to agree. (2/n)', 'This shows that the reinforcement learning notion of optimality can be made to agree with our intuitive notion of ""optimality"" in the sense of the shortest possible quantum circuit to prep a state, or compile a gate, up to some accuracy (3/n)', ""While policy iteration/dynamic programming is fine to use for the 1q problems set up here, it won't be practical for larger q scenarios, but it does mean that you could throw reinforcement learning techniques at the problem, and hope to find optimally short circuits (4/n)"", 'My guess is similar considerations would play a big part for larger q states, in particular choice of good coordinate systems for state and action spaces, the form and scale of the discretization etc (5/n)', 'I suppose one way to express the results in plainer but drastically simplifying words would be that a robot could be trained through rewards to produce the shortest possible quantum circuit necessary to prepare a state or compile a gate, up to some accuracy. (n/n)', ""@razaa_aasad Yep, and it's duly acknowledged in the references! Drawing from your paper, one could similarly hope that RL would discover close-to-optimal sequences for state prep + gate compilation at the logical gate level."", '@razaa_aasad As my new paper shows, exact-optimality, i.e. shortest length circuits, is a feature (upto caveats) of the underlying MDP for the single-qubit case. RL can therefore be expected to find at least close-to-optimal gate sequences  for state prep/compilation in the n-qubit case.']",19,12,1920