Open and completed research requests

Estimate achievable X-gate speeds and fidelities for a Yb-171 nuclear qubit using Raman transfer via a long-lived inner-shell J=2 intermediate (with 1 W/cm^2 available), limited by hyperfine splitting versus intermediate lifetime, and report gate-time vs target-infidelity tradeoffs (10^-2-10^-5).

We propose the problem of designing a dynamic, continuously-adaptive activation function that embeds continual-learning inductive biases directly into neuron nonlinearity. The target activation should trade off plasticity versus stability so networks can incrementally integrate concepts from multiple domains without catastrophic forgetting, while remaining scalable (avoiding saturation/dead neurons) and self-stabilizing (preserving bounded mean/variance across layers). Crucially, this mechanism must not rely on predefined task counts, explicit task identities, or brittle masking hacks; instead it should fold related concepts into shared representations and enable layer-wise progressive knowledge integration and reuse so that higher-level abstractions and deductive reasoning can emerge.

Robust dynamic operating envelopes (RDOEs) solve the problem of secure allocation of latent network capacity to flexible distributed energy resources (DER) in unbalanced distribution networks. As the computational complexity of RDOEs is much higher than that of dynamic operating envelopes (DOEs), which disregard uncertainties in network parameters and DER capacity utilisation, existing approaches to computing RDOEs have relied on linearised unbalanced three-phase optimal power flow (UTOPF) models to numerate the network feasible region approximately. The use of linearised models, however, risks producing RDOEs that undermine network integrity due to inherent errors in the approximation. This letter presents a practical sensitivity-filtering technique to simplify RDOE numerical computation based on non-convex UTOPF formulations. The accuracy and efficiency of the proposed approach are demonstrated on RDOE allocation with various fairness metrics by testing on representative Australian distribution networks.

We evaluate thresholds for the surface code under biased noise models using realistic gate decompositions and circuit scheduling. We compare analytical bounds with Monte Carlo simulations and quantify logical error rates across code distances.

Type 1 diabetes (T1D) is an autoimmune disease characterized by immune-mediated destruction of pancreatic beta cells, resulting in absent endogenous insulin secretion, dysglycemia, and lifelong dependence on exogenous insulin. A key unmet need is durable restoration of physiologic glucose regulation without recurrent severe hypoglycemia and without the burdens and risks of intensive insulin therapy. This problem focuses on clinical translation of beta-cell replacement using an allogeneic, stem cell–derived, fully differentiated islet-cell therapy (zmislecel / VX-880) delivered by portal-vein infusion plus glucocorticoid-free immunosuppression. The objective is to rigorously evaluate whether such a therapy can (i) engraft and produce endogenous insulin (C-peptide production), (ii) eliminate severe hypoglycemic events, (iii) improve glycated hemoglobin (HbA1c) and continuous glucose monitoring metrics (time-in-range), and (iv) achieve clinically meaningful insulin independence—while maintaining an acceptable safety profile over meaningful follow-up.

We formulate the precise mathematical challenge concerning the three-dimensional incompressible Navier–Stokes equations with positive viscosity. The objective is to determine whether smooth, physically reasonable solutions exist globally in time for all admissible initial data, or whether there exist initial data and forcing terms that lead to breakdown (nonexistence of such solutions). A successful resolution requires proving exactly one of the four official statements (A, B, C, or D).