William Hahn

William Hahn, Natalia Romero

We map the computational phase transition boundary in binary compressed sensing and identify a regime where D-Wave's quantum annealer recovers signals in a region where all tested classical methods fail, including Approximate Message Passing (AMP), which achieves the Bayes-optimal recovery threshold asymptotically for Gaussian matrices. In 19,775 experiments (n in {32, 64}, nine classical solvers, two D-Wave modes), we find that quantum annealing recovers sparse binary signals in the relaxation gap -- the regime below the Donoho-Tanner l1 phase transition where the l0 solution exists but convex relaxations fail. At n=32, k=5, m/n=0.19, D-Wave achieves 7% exact recovery while AMP and eight other solvers score 0% across 250 combined trials (Fisher exact p=0.018). At n=64, embedding overhead limits the QPU, but D-Wave's hybrid solver remains competitive with AMP. Energy landscape analysis reveals that the QUBO ground state contains the true signal, but incorrect solutions occupy shallower local basins that trap classical search -- a structure consistent with quantum tunneling dynamics. To our knowledge, this constitutes preliminary finite-size evidence that quantum annealing succeeds in a narrow regime where all tested classical methods, including the Bayes-optimal AMP, fail within a well-characterized combinatorial inference problem. Confirmation at larger n, higher trial counts, and with stronger classical controls remains an open problem.

Neil Sachdeva, Misha Klopukh, Rachel St. Clair et al.

The introduction of surgical robots brought about advancements in surgical procedures. The applications of remote telesurgery range from building medical clinics in underprivileged areas, to placing robots abroad in military hot-spots where accessibility and diversity of medical experience may be limited. Poor wireless connectivity may result in a prolonged delay, referred to as latency, between a surgeon's input and action a robot takes. In surgery, any micro-delay can injure a patient severely and in some cases, result in fatality. One was to increase safety is to mitigate the effects of latency using deep learning aided computer vision. While the current surgical robots use calibrated sensors to measure the position of the arms and tools, in this work we present a purely optical approach that provides a measurement of the tool position in relation to the patient's tissues. This research aimed to produce a neural network that allowed a robot to detect its own mechanical manipulator arms. A conditional generative adversarial networks (cGAN) was trained on 1107 frames of mock gastrointestinal robotic surgery data from the 2015 EndoVis Instrument Challenge and corresponding hand-drawn labels for each frame. When run on new testing data, the network generated near-perfect labels of the input images which were visually consistent with the hand-drawn labels and was able to do this in 299 milliseconds. These accurately generated labels can then be used as simplified identifiers for the robot to track its own controlled tools. These results show potential for conditional GANs as a reaction mechanism such that the robot can detect when its arms move outside the operating area within a patient. This system allows for more accurate monitoring of the position of surgical instruments in relation to the patient's tissue, increasing safety measures that are integral to successful telesurgery systems.