Datasets:

phanerozoic
/

qiskit-ftqc

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
experiment: string
version: string
timestamp: string
duration_seconds: double
parameters: struct<max_time: int64, theta_drive: double, theta_zz: double, shots: int64, fibonacci_sequence: list<item: int64>>
backend: struct<name: string, num_qubits: int64, basis_gates: list<item: string>>
calibration: struct<backend: string, num_qubits: int64, basis_gates: list<item: string>, qubits: list<item: struct<index: int64, t1: double, t2: double, frequency: null>>, coupling_map: list<item: list<item: int64>>>
circuits: list<item: struct<time_step: int64, fibonacci_drives: list<item: int64>, total_zz_layers: int64, total_fib_drives: int64>>
transpilation: list<item: struct<time_step: int64, original_depth: int64, transpiled_depth: int64, two_qubit_gates: int64>>
job: struct<job_id: string, status: string, usage_seconds: int64, metrics: struct<timestamps: struct<created: string, finished: string, running: string>, bss: struct<seconds: int64>, usage: struct<quantum_seconds: int64, seconds: int64>, qiskit_version: string, caller: string>>
raw_bitstrings: list<item: list<item: string>>
magnetization: list<item: struct<time_step: int64, magnetization: struct<per_qubit: list<item: double>, mean: double, std: double>, counts: struct<011: int64, 111: int64, 101: int64, 010: int64, 001: int64, 100: int64, 110: int64, 000: int64>>>
usage_tracking: struct<before: int64, after: int64, this_job: int64, remaining: int64>
vs
experiment: string
timestamp: string
backend: string
conditions: struct<fib_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, periodic_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, random_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, nodrive_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: int64, non_drive_effect: double>>, fib_5q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, fib_3q_weak: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, fib_3q_strong: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>>
job_id: string
usage: struct<before: int64, after: int64, this_job: int64>
parameters: struct<max_time: int64, shots: int64, theta_zz: double>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 563, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              experiment: string
              version: string
              timestamp: string
              duration_seconds: double
              parameters: struct<max_time: int64, theta_drive: double, theta_zz: double, shots: int64, fibonacci_sequence: list<item: int64>>
              backend: struct<name: string, num_qubits: int64, basis_gates: list<item: string>>
              calibration: struct<backend: string, num_qubits: int64, basis_gates: list<item: string>, qubits: list<item: struct<index: int64, t1: double, t2: double, frequency: null>>, coupling_map: list<item: list<item: int64>>>
              circuits: list<item: struct<time_step: int64, fibonacci_drives: list<item: int64>, total_zz_layers: int64, total_fib_drives: int64>>
              transpilation: list<item: struct<time_step: int64, original_depth: int64, transpiled_depth: int64, two_qubit_gates: int64>>
              job: struct<job_id: string, status: string, usage_seconds: int64, metrics: struct<timestamps: struct<created: string, finished: string, running: string>, bss: struct<seconds: int64>, usage: struct<quantum_seconds: int64, seconds: int64>, qiskit_version: string, caller: string>>
              raw_bitstrings: list<item: list<item: string>>
              magnetization: list<item: struct<time_step: int64, magnetization: struct<per_qubit: list<item: double>, mean: double, std: double>, counts: struct<011: int64, 111: int64, 101: int64, 010: int64, 001: int64, 100: int64, 110: int64, 000: int64>>>
              usage_tracking: struct<before: int64, after: int64, this_job: int64, remaining: int64>
              vs
              experiment: string
              timestamp: string
              backend: string
              conditions: struct<fib_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, periodic_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, random_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, nodrive_3q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: int64, non_drive_effect: double>>, fib_5q: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, fib_3q_weak: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>, fib_3q_strong: struct<magnetization: list<item: struct<per_qubit: list<item: double>, mean: double, time_step: int64>>, analysis: struct<autocorr_lag3: double, autocorr_lag5: double, autocorr_lag8: double, drive_effect: double, non_drive_effect: double>>>
              job_id: string
              usage: struct<before: int64, after: int64, this_job: int64>
              parameters: struct<max_time: int64, shots: int64, theta_zz: double>

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qiskit-ftqc

Fibonacci Time Quasi-Crystal experimental data from IBM Quantum hardware.

fibonacci_quasicrystal_20260124_212456.json — Initial 3-qubit Fibonacci drive experiment (6656 raw bitstrings)
fibonacci_v2_20260124_213552.json — Comparative study: Fibonacci, periodic, random, no-drive conditions (3q, 5q)
phase_transition_20260124_214413.json — Drive strength sweep (θ = 0.4 to 0.8)
parallel_fibonacci_20260124_221018.json — 43 parallel chains on 129 qubits (286,208 data points, 3s QPU time)

Protocol

3-7 qubit chains with ZZ coupling (θ_zz = π/8), driven by Ry(θ) rotations at Fibonacci-numbered time steps {1, 2, 3, 5, 8, 13}. Measurements at each time step t ∈ [1, 13].

Key Results

Phase transition at θ_c(∞) = 0.71 ≈ π/4.4
Finite-size scaling: θ_c(N) = 0.71 − 0.31/N
Autocorrelation at lag 5: positive (SYNC) below θ_c, negative (ANTI) above
t=5 magnetization reset: 14σ significance (43 independent chains)
Chain-to-chain std: 0.087 (uniform across chip)

Hardware

IBM Quantum ibm_torino (133 qubits), ibm_fez (156 qubits). Total QPU time: 194 seconds.

Citation

If using this data, cite as experimental observation of a drive-strength-tuned phase transition in a Fibonacci time quasi-crystal.

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qiskit-ftqc

Contents

Protocol

Key Results

Hardware

Citation