Blogposts

• Identity-Paired Progressive Depth Training: When Trainability Persists Beyond Expressibility A structural trick that trains variational quantum eigensolvers with ~5× fewer two-qubit gates — plus a theorem for why the circuit stops growing yet keeps improving.
• A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm A classical-acceleration recipe — the proximal point algorithm — applied to quantum linear-system solvers.
• Quantum EigenGame for Excited-State Calculation A game-theoretic decomposition of eigenvalue problems for quantum simulation of excited states.
• Exploiting Low-Rank Structure in Max-K-Cut Problems Algorithm overview, theoretical guarantees, and benchmarks for low-rank Max-3-Cut at scale.
  • 🖥️ What Can 15 Obsolete GPUs Do for Combinatorial Optimization? GPU implementation, scaling experiments, interactive visualisations.
  • 🧱 Rank-1 as a Building Block for Million-Node Max-3-Cut Incremental scoring, hybrid warm-starts, and extreme-scale experiments.
  • 🎲 Randomized Rank-2: When Two Eigenvectors Beat One A 3-phase pipeline that beats SA on 6 of 12 graph families with constant sample complexity.
  • 🔀 Spectral vs. Combinatorial: Two Views of Graph Structure A DSatur + spectral ensemble that beats SA on 11 of 13 graph families.
  • 🎯 How Many Darts to Hit a Maximum? The Theory of Randomized Rounding The sample-complexity theory behind the series: a rounding margin, Beta-distributed caps, and Paley–Zygmund — with a dart budget independent of n.