"""
Measurement Duration Experiment

"Does the relatively long duration of measurement compared to unitary
operations not present an obstacle for practical adoption?"

Protocol (matching zeno_gates_corrected):
- Standard: Ry(theta) Ry(-theta) measure — ideal outcome |0>
- Zeno: drag 0->theta via N measurements, then Ry(-theta) measure — ideal |0>
- Delay-matched: Ry(theta) + delay(N*t_meas) + Ry(-theta) measure — ideal |0>

Fidelity = P(0) for all circuits. This is a fair comparison because all
circuits target the same output state in the same measurement basis.

The delay-matched circuit isolates the decoherence cost: it experiences the same
wall-clock decoherence as Zeno but without the measurement-based error
suppression. If Zeno > delay-matched, measurements actively help.
"""

import json
import sys
from datetime import datetime, timezone
from dataclasses import dataclass
from pathlib import Path
import numpy as np

from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import (
    QiskitRuntimeService,
    SamplerV2,
    Batch,
)


DATA_DIR = Path("D:/qiskit-zenodragging")


@dataclass
class ExperimentConfig:
    name: str
    circuit: QuantumCircuit
    category: str
    params: dict


def log(msg, level=0):
    indent = "  " * level
    ts = datetime.now().strftime("%H:%M:%S")
    print(f"[{ts}] {indent}{msg}")


def check_usage(service):
    from datetime import datetime, timezone
    jobs = list(service.jobs(limit=200))
    now = datetime.now(timezone.utc)
    month_start = datetime(now.year, now.month, 1, tzinfo=timezone.utc)
    total = 0
    for j in jobs:
        u = j.usage() or 0
        try:
            m = j.metrics()
            ts = m.get('timestamps', {}).get('created', '')
            if ts:
                dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
                if dt >= month_start:
                    total += u
        except Exception:
            pass
    return {"total": total, "remaining": 600 - total, "percentage": 100 * total / 600}


# =============================================================================
# CIRCUIT BUILDERS — matching zeno_gates_corrected protocol
# =============================================================================

def build_standard(theta):
    """Standard: Ry(theta) Ry(-theta) measure. Ideal = |0>."""
    qc = QuantumCircuit(1, 1)
    qc.ry(theta, 0)
    qc.ry(-theta, 0)
    qc.measure(0, 0)
    return qc


def build_zeno(theta, n_meas):
    """Zeno drag from 0 to theta via N measurements, then undo. Ideal = |0>."""
    qr = QuantumRegister(1, 'q')
    cr = ClassicalRegister(n_meas + 1, 'c')
    qc = QuantumCircuit(qr, cr)

    for k in range(1, n_meas + 1):
        theta_k = k * theta / n_meas
        qc.ry(-theta_k, 0)
        qc.measure(0, k - 1)
        qc.ry(theta_k, 0)

    # Undo rotation — same as original
    qc.ry(-theta, 0)
    qc.measure(0, n_meas)
    return qc


def build_delay_matched(theta, n_meas, meas_duration_dt):
    """Standard gate + idle delay = same wall-clock as Zeno. Ideal = |0>."""
    qc = QuantumCircuit(1, 1)
    qc.ry(theta, 0)
    qc.delay(n_meas * meas_duration_dt, 0, unit='dt')
    qc.ry(-theta, 0)
    qc.measure(0, 0)
    return qc


def build_delay_only(n_meas, meas_duration_dt):
    """Identity + delay — pure decoherence baseline. Ideal = |0>."""
    qc = QuantumCircuit(1, 1)
    qc.delay(n_meas * meas_duration_dt, 0, unit='dt')
    qc.measure(0, 0)
    return qc


def build_depth_matched(theta, n_layers):
    """Same Ry gates as Zeno but no measurements. Isolates circuit structure."""
    qc = QuantumCircuit(1, 1)
    for k in range(1, n_layers + 1):
        theta_k = k * theta / n_layers
        qc.ry(-theta_k, 0)
        qc.barrier()
        qc.ry(theta_k, 0)
    qc.ry(-theta, 0)
    qc.measure(0, 0)
    return qc


# =============================================================================
# ANALYSIS
# =============================================================================

def analyze_standard(bitstrings):
    """P(0) = fidelity."""
    total = len(bitstrings)
    zeros = sum(1 for b in bitstrings if b[-1] == '0')
    return {'total': total, 'fidelity': zeros / total, 'type': 'standard'}


def analyze_zeno(bitstrings, n_meas):
    """Zeno analysis: post-selection on intermediate measurements, plus trajectory weighting."""
    total = len(bitstrings)
    successful = 0
    correct = 0
    flip_bins = {}

    for bs in bitstrings:
        if len(bs) < n_meas + 1:
            continue

        # bit[0] = final (last creg index = n_meas), bits[1:n_meas+1] = intermediate
        final = bs[0]
        intermediate = bs[1:n_meas + 1]
        n_flips = sum(1 for b in intermediate if b == '1')

        if n_flips not in flip_bins:
            flip_bins[n_flips] = {'total': 0, 'correct': 0}
        flip_bins[n_flips]['total'] += 1
        if final == '0':
            flip_bins[n_flips]['correct'] += 1

        if all(b == '0' for b in intermediate):
            successful += 1
            if final == '0':
                correct += 1

    success_rate = successful / total if total > 0 else 0
    fidelity_hard = correct / successful if successful > 0 else 0

    # Trajectory-weighted fidelity
    weighted_correct = 0
    total_weight = 0
    for nf, data in flip_bins.items():
        w = np.exp(-nf)
        weighted_correct += w * data['correct']
        total_weight += w * data['total']
    fidelity_weighted = weighted_correct / total_weight if total_weight > 0 else 0

    return {
        'total': total,
        'successful': successful,
        'success_rate': success_rate,
        'fidelity_hard_ps': fidelity_hard,
        'fidelity_weighted': fidelity_weighted,
        'flip_distribution': {str(k): v for k, v in sorted(flip_bins.items())},
        'type': 'zeno',
    }


# =============================================================================
# MAIN
# =============================================================================

def main():
    print("=" * 70)
    print("MEASUREMENT DURATION EXPERIMENT")
    print("Does measurement duration obstruct practical adoption?")
    print("=" * 70)

    service = QiskitRuntimeService(channel="ibm_cloud", instance="claude")

    usage_before = check_usage(service)
    log(f"Usage: {usage_before['total']}s / 600s ({usage_before['percentage']:.1f}%)")
    log(f"Remaining: {usage_before['remaining']}s")

    if usage_before['remaining'] < 30:
        log("Less than 30s remaining. Aborting.")
        return

    backend = service.backend("ibm_torino")
    log(f"Backend: {backend.name} ({backend.num_qubits}q)")

    # Hardware timing
    dt = backend.dt
    target = backend.target
    meas_props = target['measure'][(0,)]
    meas_duration_s = meas_props.duration
    meas_duration_dt = int(meas_duration_s / dt)
    meas_error = meas_props.error
    sx_duration_s = target['sx'][(0,)].duration

    log(f"Measurement: {meas_duration_s*1e6:.3f} us ({meas_duration_dt} dt), error={meas_error:.4f}")
    log(f"SX gate: {sx_duration_s*1e9:.1f} ns")
    log(f"Measurement/gate ratio: {meas_duration_s/sx_duration_s:.0f}x")

    # Collect per-qubit T1/T2
    qubit_t1 = {}
    qubit_t2 = {}
    for qi in range(min(backend.num_qubits, 133)):
        try:
            props = backend.qubit_properties(qi)
            qubit_t1[qi] = props.t1
            qubit_t2[qi] = props.t2
        except Exception:
            pass

    # Use qubit 0's actual T1/T2
    T1 = qubit_t1.get(0, 200e-6)
    T2 = qubit_t2.get(0, 180e-6)
    log(f"Qubit 0: T1={T1*1e6:.1f} us, T2={T2*1e6:.1f} us")

    # Parameters — match original dataset
    thetas = {
        'I': 0,
        'Ry_pi8': np.pi / 8,
        'Ry_pi4': np.pi / 4,
        'Ry_pi2': np.pi / 2,
        'Ry_3pi4': 3 * np.pi / 4,
        'X': np.pi,
    }
    n_values = [2, 4, 8, 12, 16, 24, 32]
    shots = 4096

    all_experiments = []

    for gate_name, theta in thetas.items():
        # 1. Standard gate (fast baseline)
        all_experiments.append(ExperimentConfig(
            name=f"{gate_name}_standard",
            circuit=build_standard(theta),
            category="standard",
            params={'gate': gate_name, 'theta': theta, 'n_meas': 0,
                    'total_time_us': sx_duration_s * 1e6},
        ))

        # 2. Depth-matched (no measurements, same Ry structure as Zeno N=8)
        all_experiments.append(ExperimentConfig(
            name=f"{gate_name}_depth_matched",
            circuit=build_depth_matched(theta, 8),
            category="depth_matched",
            params={'gate': gate_name, 'theta': theta, 'n_meas': 0},
        ))

        # 3. Zeno at each N
        for n in n_values:
            total_time_us = n * meas_duration_s * 1e6
            all_experiments.append(ExperimentConfig(
                name=f"{gate_name}_zeno_N{n}",
                circuit=build_zeno(theta, n),
                category="zeno",
                params={'gate': gate_name, 'theta': theta, 'n_meas': n,
                        'total_time_us': total_time_us,
                        'T1_fraction': n * meas_duration_s / T1,
                        'T2_fraction': n * meas_duration_s / T2},
            ))

            # 4. Delay-matched at each N
            all_experiments.append(ExperimentConfig(
                name=f"{gate_name}_delay_N{n}",
                circuit=build_delay_matched(theta, n, meas_duration_dt),
                category="delay_matched",
                params={'gate': gate_name, 'theta': theta, 'n_meas': n,
                        'total_time_us': total_time_us,
                        'T1_fraction': n * meas_duration_s / T1,
                        'T2_fraction': n * meas_duration_s / T2},
            ))

    # Add delay-only baselines
    for n in [8, 16, 32]:
        total_time_us = n * meas_duration_s * 1e6
        all_experiments.append(ExperimentConfig(
            name=f"delay_only_N{n}",
            circuit=build_delay_only(n, meas_duration_dt),
            category="delay_only",
            params={'gate': 'I', 'theta': 0, 'n_meas': n,
                    'total_time_us': total_time_us,
                    'T1_fraction': n * meas_duration_s / T1},
        ))

    log(f"Total experiments: {len(all_experiments)}")

    # Transpile
    log("Transpiling...")
    pm = generate_preset_pass_manager(backend=backend, optimization_level=1)

    transpiled = []
    for exp in all_experiments:
        try:
            tc = pm.run(exp.circuit)
            transpiled.append(tc)
        except Exception as e:
            log(f"ERROR transpiling {exp.name}: {e}")
            transpiled.append(None)

    valid_indices = [i for i, tc in enumerate(transpiled) if tc is not None]
    valid_transpiled = [transpiled[i] for i in valid_indices]
    valid_experiments = [all_experiments[i] for i in valid_indices]

    log(f"Transpiled: {len(valid_transpiled)}/{len(all_experiments)}")

    depths = [tc.depth() for tc in valid_transpiled]
    log(f"Depths: min={min(depths)}, max={max(depths)}")

    # Submit — single Batch, single run
    log("Submitting batch...")
    start_time = datetime.now(timezone.utc)

    with Batch(backend=backend) as batch:
        sampler = SamplerV2(mode=batch)
        job = sampler.run(valid_transpiled, shots=shots)
        log(f"Job ID: {job.job_id()}")
        log("Waiting...")
        job.wait_for_final_state()

    end_time = datetime.now(timezone.utc)
    wall_time = (end_time - start_time).total_seconds()
    log(f"Done. Wall time: {wall_time:.1f}s, QPU: {job.usage() or 0}s")

    # Retrieve and analyze
    result = job.result()
    metrics = job.metrics()
    results_data = {}

    for i, exp in enumerate(valid_experiments):
        pub_result = result[i]
        data_bin = pub_result.data

        if hasattr(data_bin, 'c'):
            bitstrings = list(data_bin.c.get_bitstrings())
        elif hasattr(data_bin, 'meas'):
            bitstrings = list(data_bin.meas.get_bitstrings())
        else:
            cr_name = list(data_bin.keys())[0]
            bitstrings = list(getattr(data_bin, cr_name).get_bitstrings())

        if exp.category == 'zeno':
            analysis = analyze_zeno(bitstrings, exp.params['n_meas'])
        else:
            analysis = analyze_standard(bitstrings)

        results_data[exp.name] = {
            'category': exp.category,
            'params': {k: (float(v) if isinstance(v, (np.floating, float)) else v)
                       for k, v in exp.params.items()},
            'transpiled_depth': depths[i],
            'analysis': analysis,
            'raw_bitstrings': bitstrings[:200],
        }

        if exp.category == 'zeno':
            a = analysis
            log(f"{exp.name}: hard={a['fidelity_hard_ps']:.3f} wt={a['fidelity_weighted']:.3f} "
                f"sr={a['success_rate']:.3f}", 1)
        else:
            log(f"{exp.name}: fidelity={analysis['fidelity']:.3f}", 1)

    # Save
    output = {
        'experiment': 'measurement_duration_analysis',
        'description': "Tests whether measurement duration overhead negates Zeno fidelity advantage",
        'question': "Does the relatively long duration of measurement compared to "
                    "unitary operations not present an obstacle for practical adoption?",
        'timestamp': start_time.isoformat(),
        'backend': backend.name,
        'shots': shots,
        'job_id': job.job_id(),
        'usage_seconds': job.usage() or 0,
        'wall_time_seconds': wall_time,
        'metrics': metrics,
        'hardware_timing': {
            'dt_ns': dt * 1e9,
            'measurement_duration_us': meas_duration_s * 1e6,
            'measurement_duration_dt': meas_duration_dt,
            'measurement_error': meas_error,
            'sx_duration_ns': sx_duration_s * 1e9,
            'measurement_to_gate_ratio': meas_duration_s / sx_duration_s,
            'qubit_0_T1_us': T1 * 1e6,
            'qubit_0_T2_us': T2 * 1e6,
        },
        'results': results_data,
    }

    outfile = DATA_DIR / 'measurement_duration_analysis.json'
    with open(outfile, 'w') as f:
        json.dump(output, f, indent=2, default=str)
    log(f"Saved: {outfile}")

    # =========================================================================
    # SUMMARY
    # =========================================================================
    print("\n" + "=" * 70)
    print("ANSWERING LEWALLE'S QUESTION")
    print("=" * 70)

    # First: reproduce original result at N=8
    print("\n--- Reproducing original zeno_gates_corrected (N=8) ---")
    print(f"\n{'Gate':>8} | {'Standard':>8} | {'Zeno(hard)':>10} | {'Zeno(wt)':>8} | {'DepthM':>6} | {'Zeno-Std':>8}")
    print("-" * 60)

    for gate_name in thetas:
        sk = f"{gate_name}_standard"
        zk = f"{gate_name}_zeno_N8"
        dk = f"{gate_name}_depth_matched"
        if all(k in results_data for k in [sk, zk, dk]):
            sf = results_data[sk]['analysis']['fidelity']
            zf_h = results_data[zk]['analysis']['fidelity_hard_ps']
            zf_w = results_data[zk]['analysis']['fidelity_weighted']
            df = results_data[dk]['analysis']['fidelity']
            print(f"{gate_name:>8} | {sf:8.4f} | {zf_h:10.4f} | {zf_w:8.4f} | {df:6.4f} | {zf_h-sf:+8.4f}")

    # Then: the key question — Zeno vs delay-matched
    print("\n--- KEY: Zeno vs delay-matched (same wall-clock time) ---")
    for gate_name in ['Ry_pi2', 'X']:
        theta = thetas[gate_name]
        print(f"\n  {gate_name}:")
        sk = f"{gate_name}_standard"
        sf = results_data[sk]['analysis']['fidelity']
        print(f"    Standard (fast):  {sf:.4f}")

        print(f"\n    {'N':>3} | {'Zeno(hard)':>10} | {'Zeno(wt)':>8} | {'Delay':>6} | {'Z-D':>6} | {'Time(us)':>8} | {'T1%':>5}")
        print(f"    " + "-" * 60)

        for n in n_values:
            zk = f"{gate_name}_zeno_N{n}"
            dlk = f"{gate_name}_delay_N{n}"
            if zk in results_data and dlk in results_data:
                za = results_data[zk]['analysis']
                da = results_data[dlk]['analysis']
                t = results_data[zk]['params']['total_time_us']
                t1f = results_data[zk]['params']['T1_fraction']
                zd = za['fidelity_hard_ps'] - da['fidelity']
                print(f"    {n:3d} | {za['fidelity_hard_ps']:10.4f} | {za['fidelity_weighted']:8.4f} | "
                      f"{da['fidelity']:6.4f} | {zd:+6.4f} | {t:8.2f} | {t1f*100:5.1f}%")

    # Usage
    usage_after = check_usage(service)
    log(f"\nUsage after: {usage_after['total']}s / 600s ({usage_after['percentage']:.1f}%)")
    log(f"This job: {job.usage() or 0}s")


if __name__ == '__main__':
    try:
        main()
    except KeyboardInterrupt:
        log("Interrupted.")
        sys.exit(1)
    except Exception as e:
        log(f"FATAL: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)