{"abstract":"An outcome on basis index 1 of two qubits is counted as \"10\" instead of \"01\".","category":"Quantum circuit simulation","checks":7,"contract":"Input [n, weights, uniforms]; weights are nonnegative integers for basis indices 0..2**n-1 and uniforms are exact decimal or fraction strings in [0, 1). Each uniform u selects the first index i with u * total < cumulative weight through i. Return counts {bitstring: count} with qubit n-1 leftmost. Errors: \"bad-length\", \"negative-weight\", \"no-support\", [\"bad-uniform\", position].","evaluation_group":"w2-quantum_circuit_simulation-shot-sampling-inverse-cdf","failed_approach":"The attempted repair removes the reversal but also drops zero padding, producing keys like \"1\" for \"01\".","family":"w2-quantum_circuit_simulation-shot-sampling-inverse-cdf-bitstring-endianness","id":"FA-91126","implementations":{"attempt":{"sha256":"e45bfe16f82cd081453aae772230cb7f9de1b7f0ca4bf5924b00c8e13bfea311","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(x):\n    n, weights, us = x\n    if len(weights) != 1 << n:\n        return 'bad-length'\n    if any(w < 0 for w in weights):\n        return 'negative-weight'\n    total = sum(weights)\n    if total == 0:\n        return 'no-support'\n    counts = {}\n    for idx, s in enumerate(us):\n        u = Fraction(s)\n        if u < 0 or u >= 1:\n            return ['bad-uniform', idx]\n        target = u * total\n        cum = 0\n        for i, w in enumerate(weights):\n            cum += w\n            if target < cum:\n                break\n        key = format(i, 'b')\n        counts[key] = counts.get(key, 0) + 1\n    return counts\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: high qubit outcome', [2, [0, 0, 1, 0], ['0.3', '0.9']], {'10': 2}], ['regression: random shots 1', [3, [5, 0, 2, 0, 5, 3, 0, 1], ['9/16', '9/16']], {'100': 2}], ['repair check: zero weight first outcome', [2, [0, 1, 1, 2], ['0', '0.25']], {'01': 1, '10': 1}], ['control: boundary uniform on cumulative edge', [1, [1, 1], ['0.5']], {'1': 1}], ['control: uniform exactly one', [1, [1, 1], ['1']], ['bad-uniform', 0]], ['control: negative uniform', [1, [1, 1], ['-0.25']], ['bad-uniform', 0]], ['control: negative weight', [1, [2, -1], ['0.5']], 'negative-weight']], [['regression: random shots 6', [3, [1, 1, 0, 0, 0, 3, 3, 0], ['0.597', '5/8', '0.601']], {'101': 2, '110': 1}], ['regression: random shots 7', [2, [0, 1, 0, 0], ['0/1', '0.123']], {'01': 2}], ['repair check: random shots 8', [2, [1, 0, 1, 3], ['0.813', '2/5', '4/5', '0.149', '0.53']], {'11': 4, '00': 1}], ['control: all zero weights', [1, [0, 0], ['0.5']], 'no-support'], ['control: bad length', [2, [1, 1], ['0.1']], 'bad-length'], ['control: random shots 0', [1, [0, 1], ['0/1', '0.952', '0/1']], {'1': 3}], ['control: random shots 2', [1, [0, 5], ['0.967', '0.119', '0.012', '1/5', '0.243', '0/5']], {'1': 6}]], [['regression: random shots 11', [2, [1, 2, 0, 2], ['0.643', '0.519', '0.64', '0.875']], {'11': 3, '01': 1}], ['regression: random shots 14', [3, [0, 1, 2, 0, 0, 3, 1, 0], ['0.559', '6/7', '2/7', '0.29', '0.711', '5/7']], {'101': 3, '110': 1, '010': 2}], ['repair check: random shots 12', [3, [5, 5, 5, 5, 1, 3, 0, 0], ['14/24', '0/24', '0.202']], {'010': 1, '000': 2}], ['control: random shots 3', [1, [3, 5], ['0.916', '3/8', '0.642']], {'1': 3}], ['control: random shots 10', [3, [3, 0, 1, 2, 0, 3, 0, 5], ['10/14', '0.687', '0.608']], {'111': 2, '101': 1}], ['control: random shots 13', [1, [3, 0], ['0.586', '1/3']], {'0': 2}], ['control: random shots 15', [1, [0, 1], ['0/1', '0.769', '0.573', '0.347', '0.401', '0/1']], {'1': 6}]], [['regression: random shots 25', [3, [0, 0, 3, 3, 0, 0, 0, 3], ['5/9', '0.444', '0.463']], {'011': 3}], ['regression: random shots 26', [3, [2, 1, 1, 2, 0, 0, 5, 5], ['14/16', '0.295']], {'111': 1, '011': 1}], ['regression: random shots 14', [3, [0, 1, 2, 0, 0, 3, 1, 0], ['0.559', '6/7', '2/7', '0.29', '0.711', '5/7']], {'101': 3, '110': 1, '010': 2}], ['control: random shots 16', [1, [0, 1], ['0.929', '0.975', '0.277', '0/1', '0.216']], {'1': 5}], ['control: random shots 17', [1, [2, 1], ['0.153', '0.906', '0/3', '0.007', '1/3']], {'0': 4, '1': 1}], ['control: random shots 18', [1, [5, 3], ['4/8', '4/8', '0.129', '0.153', '1/8', '0.326']], {'0': 6}], ['control: random shots 19', [1, [3, 2], ['0.826', '0.88']], {'1': 2}]], [['regression: random shots 30', [3, [0, 1, 3, 3, 0, 2, 0, 0], ['0/9', '0.411', '7/9', '0.796', '0.678', '0/9']], {'001': 2, '010': 1, '101': 2, '011': 1}], ['regression: random shots 31', [2, [3, 2, 0, 1], ['2/6', '0.726', '2/6', '0/6', '0.328', '0.558']], {'00': 4, '01': 2}], ['regression: random shots 25', [3, [0, 0, 3, 3, 0, 0, 0, 3], ['5/9', '0.444', '0.463']], {'011': 3}], ['control: random shots 20', [1, [5, 1], ['0.272', '0/6', '0.203', '0/6']], {'0': 4}], ['control: random shots 21', [1, [1, 3], ['1/4', '0.282', '0.17']], {'1': 2, '0': 1}], ['control: random shots 22', [1, [0, 5], ['0.608', '1/5', '2/5']], {'1': 3}], ['control: random shots 23', [1, [2, 0], ['1/2', '0.071', '0/2', '0/2', '0.086', '0.733']], {'0': 6}]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"5bc1b505f7fdcbe7ec9533dfba198f62aab12e7667b72bd824f3e41587617d6b","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(x):\n    n, weights, us = x\n    if len(weights) != 1 << n:\n        return 'bad-length'\n    if any(w < 0 for w in weights):\n        return 'negative-weight'\n    total = sum(weights)\n    if total == 0:\n        return 'no-support'\n    counts = {}\n    for idx, s in enumerate(us):\n        u = Fraction(s)\n        if u < 0 or u >= 1:\n            return ['bad-uniform', idx]\n        target = u * total\n        cum = 0\n        for i, w in enumerate(weights):\n            cum += w\n            if target < cum:\n                break\n        key = format(i, '0%db' % n)[::-1]\n        counts[key] = counts.get(key, 0) + 1\n    return counts\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: high qubit outcome', [2, [0, 0, 1, 0], ['0.3', '0.9']], {'10': 2}], ['regression: random shots 1', [3, [5, 0, 2, 0, 5, 3, 0, 1], ['9/16', '9/16']], {'100': 2}], ['repair check: zero weight first outcome', [2, [0, 1, 1, 2], ['0', '0.25']], {'01': 1, '10': 1}], ['control: boundary uniform on cumulative edge', [1, [1, 1], ['0.5']], {'1': 1}], ['control: uniform exactly one', [1, [1, 1], ['1']], ['bad-uniform', 0]], ['control: negative uniform', [1, [1, 1], ['-0.25']], ['bad-uniform', 0]], ['control: negative weight', [1, [2, -1], ['0.5']], 'negative-weight']], [['regression: random shots 6', [3, [1, 1, 0, 0, 0, 3, 3, 0], ['0.597', '5/8', '0.601']], {'101': 2, '110': 1}], ['regression: random shots 7', [2, [0, 1, 0, 0], ['0/1', '0.123']], {'01': 2}], ['repair check: random shots 8', [2, [1, 0, 1, 3], ['0.813', '2/5', '4/5', '0.149', '0.53']], {'11': 4, '00': 1}], ['control: all zero weights', [1, [0, 0], ['0.5']], 'no-support'], ['control: bad length', [2, [1, 1], ['0.1']], 'bad-length'], ['control: random shots 0', [1, [0, 1], ['0/1', '0.952', '0/1']], {'1': 3}], ['control: random shots 2', [1, [0, 5], ['0.967', '0.119', '0.012', '1/5', '0.243', '0/5']], {'1': 6}]], [['regression: random shots 11', [2, [1, 2, 0, 2], ['0.643', '0.519', '0.64', '0.875']], {'11': 3, '01': 1}], ['regression: random shots 14', [3, [0, 1, 2, 0, 0, 3, 1, 0], ['0.559', '6/7', '2/7', '0.29', '0.711', '5/7']], {'101': 3, '110': 1, '010': 2}], ['repair check: random shots 12', [3, [5, 5, 5, 5, 1, 3, 0, 0], ['14/24', '0/24', '0.202']], {'010': 1, '000': 2}], ['control: random shots 3', [1, [3, 5], ['0.916', '3/8', '0.642']], {'1': 3}], ['control: random shots 10', [3, [3, 0, 1, 2, 0, 3, 0, 5], ['10/14', '0.687', '0.608']], {'111': 2, '101': 1}], ['control: random shots 13', [1, [3, 0], ['0.586', '1/3']], {'0': 2}], ['control: random shots 15', [1, [0, 1], ['0/1', '0.769', '0.573', '0.347', '0.401', '0/1']], {'1': 6}]], [['regression: random shots 25', [3, [0, 0, 3, 3, 0, 0, 0, 3], ['5/9', '0.444', '0.463']], {'011': 3}], ['regression: random shots 26', [3, [2, 1, 1, 2, 0, 0, 5, 5], ['14/16', '0.295']], {'111': 1, '011': 1}], ['regression: random shots 14', [3, [0, 1, 2, 0, 0, 3, 1, 0], ['0.559', '6/7', '2/7', '0.29', '0.711', '5/7']], {'101': 3, '110': 1, '010': 2}], ['control: random shots 16', [1, [0, 1], ['0.929', '0.975', '0.277', '0/1', '0.216']], {'1': 5}], ['control: random shots 17', [1, [2, 1], ['0.153', '0.906', '0/3', '0.007', '1/3']], {'0': 4, '1': 1}], ['control: random shots 18', [1, [5, 3], ['4/8', '4/8', '0.129', '0.153', '1/8', '0.326']], {'0': 6}], ['control: random shots 19', [1, [3, 2], ['0.826', '0.88']], {'1': 2}]], [['regression: random shots 30', [3, [0, 1, 3, 3, 0, 2, 0, 0], ['0/9', '0.411', '7/9', '0.796', '0.678', '0/9']], {'001': 2, '010': 1, '101': 2, '011': 1}], ['regression: random shots 31', [2, [3, 2, 0, 1], ['2/6', '0.726', '2/6', '0/6', '0.328', '0.558']], {'00': 4, '01': 2}], ['regression: random shots 25', [3, [0, 0, 3, 3, 0, 0, 0, 3], ['5/9', '0.444', '0.463']], {'011': 3}], ['control: random shots 20', [1, [5, 1], ['0.272', '0/6', '0.203', '0/6']], {'0': 4}], ['control: random shots 21', [1, [1, 3], ['1/4', '0.282', '0.17']], {'1': 2, '0': 1}], ['control: random shots 22', [1, [0, 5], ['0.608', '1/5', '2/5']], {'1': 3}], ['control: random shots 23', [1, [2, 0], ['1/2', '0.071', '0/2', '0/2', '0.086', '0.733']], {'0': 6}]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"6d3d7919f59116ea102a8bdb15fd992841fb8bc7f74e28de502ca2d1521d51d6","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(x):\n    n, weights, us = x\n    if len(weights) != 1 << n:\n        return 'bad-length'\n    if any(w < 0 for w in weights):\n        return 'negative-weight'\n    total = sum(weights)\n    if total == 0:\n        return 'no-support'\n    counts = {}\n    for idx, s in enumerate(us):\n        u = Fraction(s)\n        if u < 0 or u >= 1:\n            return ['bad-uniform', idx]\n        target = u * total\n        cum = 0\n        for i, w in enumerate(weights):\n            cum += w\n            if target < cum:\n                break\n        key = format(i, '0%db' % n)\n        counts[key] = counts.get(key, 0) + 1\n    return counts\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[['regression: high qubit outcome', [2, [0, 0, 1, 0], ['0.3', '0.9']], {'10': 2}], ['regression: random shots 1', [3, [5, 0, 2, 0, 5, 3, 0, 1], ['9/16', '9/16']], {'100': 2}], ['repair check: zero weight first outcome', [2, [0, 1, 1, 2], ['0', '0.25']], {'01': 1, '10': 1}], ['control: boundary uniform on cumulative edge', [1, [1, 1], ['0.5']], {'1': 1}], ['control: uniform exactly one', [1, [1, 1], ['1']], ['bad-uniform', 0]], ['control: negative uniform', [1, [1, 1], ['-0.25']], ['bad-uniform', 0]], ['control: negative weight', [1, [2, -1], ['0.5']], 'negative-weight']], [['regression: random shots 6', [3, [1, 1, 0, 0, 0, 3, 3, 0], ['0.597', '5/8', '0.601']], {'101': 2, '110': 1}], ['regression: random shots 7', [2, [0, 1, 0, 0], ['0/1', '0.123']], {'01': 2}], ['repair check: random shots 8', [2, [1, 0, 1, 3], ['0.813', '2/5', '4/5', '0.149', '0.53']], {'11': 4, '00': 1}], ['control: all zero weights', [1, [0, 0], ['0.5']], 'no-support'], ['control: bad length', [2, [1, 1], ['0.1']], 'bad-length'], ['control: random shots 0', [1, [0, 1], ['0/1', '0.952', '0/1']], {'1': 3}], ['control: random shots 2', [1, [0, 5], ['0.967', '0.119', '0.012', '1/5', '0.243', '0/5']], {'1': 6}]], [['regression: random shots 11', [2, [1, 2, 0, 2], ['0.643', '0.519', '0.64', '0.875']], {'11': 3, '01': 1}], ['regression: random shots 14', [3, [0, 1, 2, 0, 0, 3, 1, 0], ['0.559', '6/7', '2/7', '0.29', '0.711', '5/7']], {'101': 3, '110': 1, '010': 2}], ['repair check: random shots 12', [3, [5, 5, 5, 5, 1, 3, 0, 0], ['14/24', '0/24', '0.202']], {'010': 1, '000': 2}], ['control: random shots 3', [1, [3, 5], ['0.916', '3/8', '0.642']], {'1': 3}], ['control: random shots 10', [3, [3, 0, 1, 2, 0, 3, 0, 5], ['10/14', '0.687', '0.608']], {'111': 2, '101': 1}], ['control: random shots 13', [1, [3, 0], ['0.586', '1/3']], {'0': 2}], ['control: random shots 15', [1, [0, 1], ['0/1', '0.769', '0.573', '0.347', '0.401', '0/1']], {'1': 6}]], [['regression: random shots 25', [3, [0, 0, 3, 3, 0, 0, 0, 3], ['5/9', '0.444', '0.463']], {'011': 3}], ['regression: random shots 26', [3, [2, 1, 1, 2, 0, 0, 5, 5], ['14/16', '0.295']], {'111': 1, '011': 1}], ['regression: random shots 14', [3, [0, 1, 2, 0, 0, 3, 1, 0], ['0.559', '6/7', '2/7', '0.29', '0.711', '5/7']], {'101': 3, '110': 1, '010': 2}], ['control: random shots 16', [1, [0, 1], ['0.929', '0.975', '0.277', '0/1', '0.216']], {'1': 5}], ['control: random shots 17', [1, [2, 1], ['0.153', '0.906', '0/3', '0.007', '1/3']], {'0': 4, '1': 1}], ['control: random shots 18', [1, [5, 3], ['4/8', '4/8', '0.129', '0.153', '1/8', '0.326']], {'0': 6}], ['control: random shots 19', [1, [3, 2], ['0.826', '0.88']], {'1': 2}]], [['regression: random shots 30', [3, [0, 1, 3, 3, 0, 2, 0, 0], ['0/9', '0.411', '7/9', '0.796', '0.678', '0/9']], {'001': 2, '010': 1, '101': 2, '011': 1}], ['regression: random shots 31', [2, [3, 2, 0, 1], ['2/6', '0.726', '2/6', '0/6', '0.328', '0.558']], {'00': 4, '01': 2}], ['regression: random shots 25', [3, [0, 0, 3, 3, 0, 0, 0, 3], ['5/9', '0.444', '0.463']], {'011': 3}], ['control: random shots 20', [1, [5, 1], ['0.272', '0/6', '0.203', '0/6']], {'0': 4}], ['control: random shots 21', [1, [1, 3], ['1/4', '0.282', '0.17']], {'1': 2, '0': 1}], ['control: random shots 22', [1, [0, 5], ['0.608', '1/5', '2/5']], {'1': 3}], ['control: random shots 23', [1, [2, 0], ['1/2', '0.071', '0/2', '0/2', '0.086', '0.733']], {'0': 6}]]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic bounded teaching model with a stipulated toy contract; amplitudes are rounded to fixed decimals for strict JSON output. It is not a production quantum SDK and claims no standards conformance. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-quantum_circuit_simulation-shot-sampling-inverse-cdf-bitstring-endianness","generated_at":"2026-09-29T14:51:33.000681+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Deterministic replay of sampled shots makes simulator results reproducible; CDF boundary slips bias counts toward impossible outcomes.","repair":"Use the zero-padded binary index directly (qubit n-1 leftmost).","root_cause":"The formatted index string is reversed before use as the key.","sha256":"9224697326c18e3515bc819ce05293d63fa2b71c70a95cb5720372315f7a7720","title":"Shot sampler writes qubit 0 as the leftmost character · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":43.898,"exit_code":1,"observations":[{"actual":{"10":2},"check":"regression: high qubit outcome","expected":{"10":2},"passed":true},{"actual":{"100":2},"check":"regression: random shots 1","expected":{"100":2},"passed":true},{"actual":{"1":1,"10":1},"check":"repair check: zero weight first outcome","expected":{"01":1,"10":1},"passed":false},{"actual":{"1":1},"check":"control: boundary uniform on cumulative edge","expected":{"1":1},"passed":true},{"actual":["bad-uniform",0],"check":"control: uniform exactly one","expected":["bad-uniform",0],"passed":true},{"actual":["bad-uniform",0],"check":"control: negative uniform","expected":["bad-uniform",0],"passed":true},{"actual":"negative-weight","check":"control: negative weight","expected":"negative-weight","passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: high qubit outcome\", \"actual\": {\"10\": 2}, \"expected\": {\"10\": 2}, \"passed\": true}, {\"check\": \"regression: random shots 1\", \"actual\": {\"100\": 2}, \"expected\": {\"100\": 2}, \"passed\": true}, {\"check\": \"repair check: zero weight first outcome\", \"actual\": {\"1\": 1, \"10\": 1}, \"expected\": {\"01\": 1, \"10\": 1}, \"passed\": false}, {\"check\": \"control: boundary uniform on cumulative edge\", \"actual\": {\"1\": 1}, \"expected\": {\"1\": 1}, \"passed\": true}, {\"check\": \"control: uniform exactly one\", \"actual\": [\"bad-uniform\", 0], \"expected\": [\"bad-uniform\", 0], \"passed\": true}, {\"check\": \"control: negative uniform\", \"actual\": [\"bad-uniform\", 0], \"expected\": [\"bad-uniform\", 0], \"passed\": true}, {\"check\": \"control: negative weight\", \"actual\": \"negative-weight\", \"expected\": \"negative-weight\", \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":47.825,"exit_code":1,"observations":[{"actual":{"01":2},"check":"regression: high qubit outcome","expected":{"10":2},"passed":false},{"actual":{"001":2},"check":"regression: random shots 1","expected":{"100":2},"passed":false},{"actual":{"01":1,"10":1},"check":"repair check: zero weight first outcome","expected":{"01":1,"10":1},"passed":true},{"actual":{"1":1},"check":"control: boundary uniform on cumulative edge","expected":{"1":1},"passed":true},{"actual":["bad-uniform",0],"check":"control: uniform exactly one","expected":["bad-uniform",0],"passed":true},{"actual":["bad-uniform",0],"check":"control: negative uniform","expected":["bad-uniform",0],"passed":true},{"actual":"negative-weight","check":"control: negative weight","expected":"negative-weight","passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: high qubit outcome\", \"actual\": {\"01\": 2}, \"expected\": {\"10\": 2}, \"passed\": false}, {\"check\": \"regression: random shots 1\", \"actual\": {\"001\": 2}, \"expected\": {\"100\": 2}, \"passed\": false}, {\"check\": \"repair check: zero weight first outcome\", \"actual\": {\"10\": 1, \"01\": 1}, \"expected\": {\"01\": 1, \"10\": 1}, \"passed\": true}, {\"check\": \"control: boundary uniform on cumulative edge\", \"actual\": {\"1\": 1}, \"expected\": {\"1\": 1}, \"passed\": true}, {\"check\": \"control: uniform exactly one\", \"actual\": [\"bad-uniform\", 0], \"expected\": [\"bad-uniform\", 0], \"passed\": true}, {\"check\": \"control: negative uniform\", \"actual\": [\"bad-uniform\", 0], \"expected\": [\"bad-uniform\", 0], \"passed\": true}, {\"check\": \"control: negative weight\", \"actual\": \"negative-weight\", \"expected\": \"negative-weight\", \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":43.151,"exit_code":0,"observations":[{"actual":{"10":2},"check":"regression: high qubit outcome","expected":{"10":2},"passed":true},{"actual":{"100":2},"check":"regression: random shots 1","expected":{"100":2},"passed":true},{"actual":{"01":1,"10":1},"check":"repair check: zero weight first outcome","expected":{"01":1,"10":1},"passed":true},{"actual":{"1":1},"check":"control: boundary uniform on cumulative edge","expected":{"1":1},"passed":true},{"actual":["bad-uniform",0],"check":"control: uniform exactly one","expected":["bad-uniform",0],"passed":true},{"actual":["bad-uniform",0],"check":"control: negative uniform","expected":["bad-uniform",0],"passed":true},{"actual":"negative-weight","check":"control: negative weight","expected":"negative-weight","passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: high qubit outcome\", \"actual\": {\"10\": 2}, \"expected\": {\"10\": 2}, \"passed\": true}, {\"check\": \"regression: random shots 1\", \"actual\": {\"100\": 2}, \"expected\": {\"100\": 2}, \"passed\": true}, {\"check\": \"repair check: zero weight first outcome\", \"actual\": {\"01\": 1, \"10\": 1}, \"expected\": {\"01\": 1, \"10\": 1}, \"passed\": true}, {\"check\": \"control: boundary uniform on cumulative edge\", 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