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Oishi1029/README.md

Bong Bin Yong

Undergraduate in Malaysia. working on quantum optimal control at the pulse level, and on the classical electronics sitting between the optimizer and the qubit.

Merged upstream

  • harmoniqs/Piccolo.jl#238 — dual-rail encoding and EmbeddedOperator utilities: sparse subspace reduction, logical-ordered subspaces, operator/target consistency checks, 10 testitems. unitaryHACK 2026.
  • harmoniqs/Legato.jl#27 — FFT pulse-spectrum analyzer. unitaryHACK 2026.

My own

  • predistort — a differentiable model of a superconducting control line (DAC zero-order hold, line response, IQ imbalance, amplifier compression) wired to a three-level transmon in JAX, with every stage adjoint derived by hand.

    The composed Julia↔JAX gradient agrees with central finite differences to 1.09e-06 worst-case, and make env && make build && make verify reproduces that from a clean clone (verified on Apple silicon).

    The headline result is negative. Below roughly 131 MHz of drive bandwidth the linear inverse demands DAC codes outside the box, and a clipped inverse is not an inverse — the corrected gate (3.76e-03) comes out worse than no pre-distortion at all (2.40e-03).

I use AI assistance when I write code, and I disclose it where the work is published. Nothing goes out that I have not read, run and tested myself.

I came to this from embedded and storage systems: STM32/FreeRTOS firmware, an automotive radar project, and a ZNS emulation result (1st runner-up, Sandisk symposium).

Pinned Loading

  1. predistort predistort Public

    A differentiable model of a superconducting control line — DAC, filter, IQ mixer, amplifier — wired to a three-level transmon in JAX, with hand-derived adjoints and a gradient checked against finit…

    Python

  2. machine-ear machine-ear Public

    Offline acoustic machine-health monitoring on Arm. FP32→INT8: 4.07× faster in the deployed path, 3.68× smaller, accuracy retained. Every number regenerates from committed benchmarks.

    Python