NTT-learning/notebooks/butterfly_mechanics/03_fast_forward_ct/problems.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "pedagogy": {
          "role": "meta",
          "difficulty": 1,
          "kind": "orientation",
          "title": "Problem Set Goals"
        }
      },
      "source": "## META | difficulty 1 | Problem Set Goals\n\nThis notebook checks whether the CT schedule is now a visible object rather than a name.\n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pedagogy": {
          "role": "mandatory",
          "difficulty": 2,
          "kind": "quiz",
          "title": "Multiple-Choice Retrieval"
        }
      },
      "source": "## MANDATORY | difficulty 2 | Multiple-Choice Retrieval\n\nChoose one answer for each:\n\n1. CT makes the transform faster by:\n   A. changing the ring\n   B. reusing bracket structure through staged butterflies\n   C. deleting the inverse\n\n2. The natural output order of the direct CT schedule discussed here is:\n   A. normal order\n   B. bit-reversed order\n   C. random order\n\n3. A stage explorer is useful mainly because it:\n   A. hides the pairings\n   B. makes data movement and zeta usage inspectable\n   C. removes the need for examples\n"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pedagogy": {
          "role": "mandatory",
          "difficulty": 2,
          "kind": "quiz",
          "title": "Answer Key"
        }
      },
      "outputs": [],
      "source": "# MANDATORY | difficulty 2 | Answer Key\n\nanswers = {1: \"B\", 2: \"B\", 3: \"B\"}\nprint(answers)\n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pedagogy": {
          "role": "mandatory",
          "difficulty": 2,
          "kind": "exercise",
          "title": "Paper Example Check"
        }
      },
      "source": "## MANDATORY | difficulty 2 | Paper Example Check\n\nVerify that the CT trace on `[1,2,3,4]` in `Z_7681` with `\u03c8 = 1925` lands on the paper\u2019s BO output.\n"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pedagogy": {
          "role": "mandatory",
          "difficulty": 2,
          "kind": "exercise",
          "title": "Check The BO Output"
        }
      },
      "outputs": [],
      "source": "# MANDATORY | difficulty 2 | Check The BO Output\n\nfrom ntt_learning.toy_ntt import fast_ntt_psi_ct_trace\n\ntrace = fast_ntt_psi_ct_trace([1, 2, 3, 4], 7681, 1925)\nprint(trace.raw_output)\nassert list(trace.raw_output) == [1467, 3471, 2807, 7621]\n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pedagogy": {
          "role": "mandatory",
          "difficulty": 2,
          "kind": "reflection",
          "title": "Written Reflection"
        }
      },
      "source": "## MANDATORY | difficulty 2 | Written Reflection\n\nExplain why the phrase \u201csame transform, better schedule\u201d is the right headline for CT.\n"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pedagogy": {
          "role": "facultative",
          "difficulty": 4,
          "kind": "exploration",
          "title": "Optional Challenge"
        }
      },
      "outputs": [],
      "source": "# FACULTATIVE | difficulty 4 | Optional Challenge\n\nfrom ntt_learning.toy_ntt import fast_ntt_psi_ct_trace, find_psi\n\ntrace = fast_ntt_psi_ct_trace([3, 1, 4, 1, 5, 9, 2, 6], 97, find_psi(8, 97))\nfor stage in trace.stages:\n    print(stage.stage_index, stage.output_values)\n"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "pedagogy": {
          "role": "meta",
          "difficulty": 1,
          "kind": "handoff",
          "title": "Next Notebook"
        }
      },
      "source": "## META | difficulty 1 | Next Notebook\n\nNext notebook: `studio.ipynb`\n"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "name": "python"
    },
    "ntt_learning": {
      "title": "Problems: Fast Forward CT",
      "contract_version": "0.2",
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