
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from braket.ahs.atom_arrangement import SiteType
from braket.timings.time_series import TimeSeries
from braket.ahs.driving_field import DrivingField
from braket.ahs.shifting_field import ShiftingField
from braket.ahs.field import Field
from braket.ahs.pattern import Pattern
from collections import Counter

from typing import Dict, List, Tuple
from braket.tasks.analog_hamiltonian_simulation_quantum_task_result import AnalogHamiltonianSimulationQuantumTaskResult
from braket.ahs.atom_arrangement import AtomArrangement


def show_register(
    register: AtomArrangement, 
    blockade_radius: float=0.0, 
    what_to_draw: str="bond", 
    show_atom_index:bool=True
):
    """Plot the given register 
        Args:
            register (AtomArrangement): A given register
            blockade_radius (float): The blockade radius for the register. Default is 0
            what_to_draw (str): Either "bond" or "circle" to indicate the blockade region. 
                Default is "bond"
            show_atom_index (bool): Whether showing the indices of the atoms. Default is True
        
    """
    filled_sites = [site.coordinate for site in register if site.site_type == SiteType.FILLED]
    empty_sites = [site.coordinate for site in register if site.site_type == SiteType.VACANT]
    
    fig = plt.figure(figsize=(7, 7))
    if filled_sites:
        plt.plot(np.array(filled_sites)[:, 0], np.array(filled_sites)[:, 1], 'r.', ms=15, label='filled')
    if empty_sites:
        plt.plot(np.array(empty_sites)[:, 0], np.array(empty_sites)[:, 1], 'k.', ms=5, label='empty')
    plt.legend(bbox_to_anchor=(1.1, 1.05))
    
    if show_atom_index:
        for idx, site in enumerate(register):
            plt.text(*site.coordinate, f"  {idx}", fontsize=12)
    
    if blockade_radius > 0 and what_to_draw=="bond":
        for i in range(len(filled_sites)):
            for j in range(i+1, len(filled_sites)):            
                dist = np.linalg.norm(np.array(filled_sites[i]) - np.array(filled_sites[j]))
                if dist <= blockade_radius:
                    plt.plot([filled_sites[i][0], filled_sites[j][0]], [filled_sites[i][1], filled_sites[j][1]], 'b')
                    
    if blockade_radius > 0 and what_to_draw=="circle":
        for site in filled_sites:
            plt.gca().add_patch( plt.Circle((site[0],site[1]), blockade_radius/2, color="b", alpha=0.3) )
        plt.gca().set_aspect(1)
    plt.show()


def rabi_pulse(
    rabi_pulse_area: float, 
    omega_max: float,
    omega_slew_rate_max: float
) -> Tuple[List[float], List[float]]:
    """Get a time series for Rabi frequency with specified Rabi phase, maximum amplitude
    and maximum slew rate
        Args:
            rabi_pulse_area (float): Total area under the Rabi frequency time series
            omega_max (float): The maximum amplitude 
            omega_slew_rate_max (float): The maximum slew rate
        Returns:
            Tuple[List[float], List[float]]: A tuple containing the time points and values
                of the time series for the time dependent Rabi frequency
        Notes: By Rabi phase, it means the integral of the amplitude of a time-dependent 
            Rabi frequency, \int_0^T\Omega(t)dt, where T is the duration.
    """

    phase_threshold = omega_max**2 / omega_slew_rate_max
    if rabi_pulse_area <= phase_threshold:
        t_ramp = np.sqrt(rabi_pulse_area / omega_slew_rate_max)
        t_plateau = 0
    else:
        t_ramp = omega_max / omega_slew_rate_max
        t_plateau = (rabi_pulse_area / omega_max) - t_ramp
    t_pules = 2 * t_ramp + t_plateau
    time_points = [0, t_ramp, t_ramp + t_plateau, t_pules]
    amplitude_values = [0, t_ramp * omega_slew_rate_max, t_ramp * omega_slew_rate_max, 0]
    
    return time_points, amplitude_values


def get_counts(result: AnalogHamiltonianSimulationQuantumTaskResult) -> Dict[str, int]:
    """Aggregate state counts from AHS shot results
        Args:
            result (AnalogHamiltonianSimulationQuantumTaskResult): The result 
                from which the aggregated state counts are obtained
        Returns:
            Dict[str, int]: number of times each state configuration is measured
        Notes: We use the following convention to denote the state of an atom (site):
            e: empty site
            r: Rydberg state atom
            g: ground state atom
    """

    state_counts = Counter()
    states = ['e', 'r', 'g']
    for shot in result.measurements:
        pre = shot.pre_sequence
        post = shot.post_sequence
        state_idx = np.array(pre) * (1 + np.array(post))
        state = "".join(map(lambda s_idx: states[s_idx], state_idx))
        state_counts.update((state,))

    return dict(state_counts)


def get_drive(
    times: List[float], 
    amplitude_values: List[float], 
    detuning_values: List[float], 
    phase_values: List[float]
) -> DrivingField:
    """Get the driving field from a set of time points and values of the fields
        Args:
            times (List[float]): The time points of the driving field
            amplitude_values (List[float]): The values of the amplitude
            detuning_values (List[float]): The values of the detuning
            phase_values (List[float]): The values of the phase
        Returns:
            DrivingField: The driving field obtained
    """

    assert len(times) == len(amplitude_values)
    assert len(times) == len(detuning_values)
    assert len(times) == len(phase_values)
    
    amplitude = TimeSeries()
    detuning = TimeSeries()  
    phase = TimeSeries()    
    
    for t, amplitude_value, detuning_value, phase_value in zip(times, amplitude_values, detuning_values, phase_values):
        amplitude.put(t, amplitude_value)
        detuning.put(t, detuning_value)
        phase.put(t, phase_value) 

    drive = DrivingField(
        amplitude=amplitude, 
        detuning=detuning, 
        phase=phase
    )    
    
    return drive


def get_shift(times: List[float], values: List[float], pattern: List[float]) -> ShiftingField:
    """Get the shifting field from a set of time points, values and pattern
        Args:
            times (List[float]): The time points of the shifting field
            values (List[float]): The values of the shifting field
            pattern (List[float]): The pattern of the shifting field
        Returns:
            ShiftingField: The shifting field obtained
    """    
    assert len(times) == len(values)    
    
    magnitude = TimeSeries()
    for t, v in zip(times, values):
        magnitude.put(t, v)
    shift = ShiftingField(Field(magnitude, Pattern(pattern)))

    return shift


def show_global_drive(drive, axes=None, **plot_ops):
    """Plot the driving field
        Args:
            drive (DrivingField): The driving field to be plot
            axes: matplotlib axis to draw on
            **plot_ops: options passed to matplitlib.pyplot.plot
    """   

    data = {
        'amplitude [rad/s]': drive.amplitude.time_series,
        'detuning [rad/s]': drive.detuning.time_series,
        'phase [rad]': drive.phase.time_series,
    }


    if axes is None:
        fig, axes = plt.subplots(3, 1, figsize=(7, 7), sharex=True)

    for ax, data_name in zip(axes, data.keys()):
        if data_name == 'phase [rad]':
            ax.step(data[data_name].times(), data[data_name].values(), '.-', where='post',**plot_ops)
        else:
            ax.plot(data[data_name].times(), data[data_name].values(), '.-',**plot_ops)
        ax.set_ylabel(data_name)
        ax.grid(ls=':')
    axes[-1].set_xlabel('time [s]')
    plt.tight_layout()
    plt.show()


def show_local_shift(shift:ShiftingField):
    """Plot the shifting field
        Args:
            shift (ShiftingField): The shifting field to be plot
    """       
    data = shift.magnitude.time_series
    pattern = shift.magnitude.pattern.series
    
    plt.plot(data.times(), data.values(), '.-', label="pattern: " + str(pattern))
    plt.xlabel('time [s]')
    plt.ylabel('shift [rad/s]')
    plt.legend()
    plt.tight_layout()
    plt.show()

    
def show_drive_and_shift(drive: DrivingField, shift: ShiftingField):
    """Plot the driving and shifting fields
    
        Args:
            drive (DrivingField): The driving field to be plot
            shift (ShiftingField): The shifting field to be plot
    """        
    drive_data = {
        'amplitude [rad/s]': drive.amplitude.time_series,
        'detuning [rad/s]': drive.detuning.time_series,
        'phase [rad]': drive.phase.time_series,
    }
    
    fig, axes = plt.subplots(4, 1, figsize=(7, 7), sharex=True)
    for ax, data_name in zip(axes, drive_data.keys()):
        if data_name == 'phase [rad]':
            ax.step(drive_data[data_name].times(), drive_data[data_name].values(), '.-', where='post')
        else:
            ax.plot(drive_data[data_name].times(), drive_data[data_name].values(), '.-')
        ax.set_ylabel(data_name)
        ax.grid(ls=':')
        
    shift_data = shift.magnitude.time_series
    pattern = shift.magnitude.pattern.series   
    axes[-1].plot(shift_data.times(), shift_data.values(), '.-', label="pattern: " + str(pattern))
    axes[-1].set_ylabel('shift [rad/s]')
    axes[-1].set_xlabel('time [s]')
    axes[-1].legend()
    axes[-1].grid()
    plt.tight_layout()
    plt.show()


def get_avg_density(result: AnalogHamiltonianSimulationQuantumTaskResult) -> np.ndarray:
    """Get the average Rydberg densities from the result
        Args:
            result (AnalogHamiltonianSimulationQuantumTaskResult): The result 
                from which the aggregated state counts are obtained
        Returns: 
            ndarray: The average densities from the result
    """    

    measurements = result.measurements
    postSeqs = [measurement.post_sequence for measurement in measurements]
    postSeqs = 1 - np.array(postSeqs) # change the notation such 1 for rydberg state, and 0 for ground state
    
    avg_density = np.sum(postSeqs, axis=0)/len(postSeqs)
    
    return avg_density


def show_final_avg_density(result: AnalogHamiltonianSimulationQuantumTaskResult):
    """Showing a bar plot for the average Rydberg densities from the result
        Args:
            result (AnalogHamiltonianSimulationQuantumTaskResult): The result 
                from which the aggregated state counts are obtained
    """    
    avg_density = get_avg_density(result)
    
    plt.bar(range(len(avg_density)), avg_density)
    plt.xlabel("Indices of atoms")
    plt.ylabel("Average Rydberg density")
    plt.show()


def constant_time_series(other_time_series: TimeSeries, constant: float=0.0) -> TimeSeries:
    """Obtain a constant time series with the same time points as the given time series
        Args:
            other_time_series (TimeSeries): The given time series
        Returns:
            TimeSeries: A constant time series with the same time points as the given time series
    """
    ts = TimeSeries()
    for t in other_time_series.times():
        ts.put(t, constant)
    return ts


def concatenate_time_series(time_series_1: TimeSeries, time_series_2: TimeSeries) -> TimeSeries:
    """Concatenate two time series to a single time series
        Args:
            time_series_1 (TimeSeries): The first time series to be concatenated
            time_series_2 (TimeSeries): The second time series to be concatenated
        Returns:
            TimeSeries: The concatenated time series
    """
    assert time_series_1.values()[-1] == time_series_2.values()[0]
    
    duration_1 = time_series_1.times()[-1] - time_series_1.times()[0]
    
    new_time_series = TimeSeries()
    new_times = time_series_1.times() + [t + duration_1 - time_series_2.times()[0] for t in time_series_2.times()[1:]]
    new_values = time_series_1.values() + time_series_2.values()[1:]
    for t, v in zip(new_times, new_values):
        new_time_series.put(t, v)
    
    return new_time_series


def concatenate_drives(drive_1: DrivingField, drive_2: DrivingField) -> DrivingField:
    """Concatenate two driving fields to a single driving field
        Args:
            drive_1 (DrivingField): The first driving field to be concatenated
            drive_2 (DrivingField): The second driving field to be concatenated
        Returns:
            DrivingField: The concatenated driving field
    """    
    return DrivingField(
        amplitude=concatenate_time_series(drive_1.amplitude.time_series, drive_2.amplitude.time_series),
        detuning=concatenate_time_series(drive_1.detuning.time_series, drive_2.detuning.time_series),
        phase=concatenate_time_series(drive_1.phase.time_series, drive_2.phase.time_series)
    )


def concatenate_shifts(shift_1: ShiftingField, shift_2: ShiftingField) -> ShiftingField:
    """Concatenate two driving fields to a single driving field
        Args:
            shift_1 (ShiftingField): The first shifting field to be concatenated
            shift_2 (ShiftingField): The second shifting field to be concatenated
        Returns:
            ShiftingField: The concatenated shifting field
    """        
    assert shift_1.magnitude.pattern.series == shift_2.magnitude.pattern.series
    
    new_magnitude = concatenate_time_series(shift_1.magnitude.time_series, shift_2.magnitude.time_series)
    return ShiftingField(Field(new_magnitude, shift_1.magnitude.pattern))


def concatenate_drive_list(drive_list: List[DrivingField]) -> DrivingField:
    """Concatenate a list of driving fields to a single driving field
        Args:
            drive_list (List[DrivingField]): The list of driving fields to be concatenated
        Returns:
            DrivingField: The concatenated driving field
    """        
    drive = drive_list[0]
    for dr in drive_list[1:]:
        drive = concatenate_drives(drive, dr)
    return drive    


def concatenate_shift_list(shift_list: List[ShiftingField]) -> ShiftingField:
    """Concatenate a list of shifting fields to a single driving field
        Args:
            shift_list (List[ShiftingField]): The list of shifting fields to be concatenated
        Returns:
            ShiftingField: The concatenated shifting field
    """            
    shift = shift_list[0]
    for sf in shift_list[1:]:
        shift = concatenate_shifts(shift, sf)
    return shift


def plot_avg_density_2D(densities, register, with_labels = True, batch_index = None, batch_mapping = None, custom_axes = None):
    
    # get atom coordinates
    atom_coords = list(zip(register.coordinate_list(0), register.coordinate_list(1)))
    # convert all to micrometers
    atom_coords = [(atom_coord[0] * 10**6, atom_coord[1] * 10**6) for atom_coord in atom_coords]
    
    plot_avg_of_avgs = False
    plot_single_batch = False
        
    if batch_index is not None:
        if batch_mapping is not None:
                plot_single_batch = True
                # provided both batch and batch_mapping, show averages of single batch
                batch_subindices = batch_mapping[batch_index]
                batch_labels = {i:label for i,label in enumerate(batch_subindices)}
                # get proper positions
                pos = {i:tuple(coord) for i,coord in enumerate(list(np.array(atom_coords)[batch_subindices]))}
                # narrow down densities
                densities = np.array(densities)[batch_subindices]
                
        else:
            raise Exception("batch_mapping required to index into")
    else:
        if batch_mapping is not None:
            plot_avg_of_avgs = True
            # just need the coordinates for first batch_mapping
            subcoordinates = np.array(atom_coords)[batch_mapping[(0,0)]]
            pos = {i:coord for i,coord in enumerate(subcoordinates)}                                     
        else:
            # If both not provided do standard FOV
            # handle 1D case
            pos = {i:coord for i,coord in enumerate(atom_coords)}
           
    # get colors
    vmin = 0
    vmax = 1
    cmap = plt.cm.Blues
    
    # construct graph
    g = nx.Graph()
    g.add_nodes_from(list(range(len(densities))))
    
    # construct plot
    if custom_axes is None:
        fig, ax = plt.subplots()
    else:
        ax = custom_axes
    
    nx.draw(g, 
            pos,
            node_color=densities,
            cmap=cmap,
            node_shape="o",
            vmin=vmin,
            vmax=vmax,
            font_size=9,
            with_labels=with_labels,
            labels= batch_labels if plot_single_batch else None,
            ax = custom_axes if custom_axes is not None else ax)
        
    ## Set axes
    ax.set_axis_on()
    ax.tick_params(left=True, 
                   bottom=True, 
                   top=True,
                   right=True,
                   labelleft=True, 
                   labelbottom=True, 
                   # labeltop=True,
                   # labelright=True,
                   direction="in")
    ## Set colorbar
    sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
    sm.set_array([])

    
    ax.ticklabel_format(style="sci", useOffset=False)
    
    # set titles on x and y axes
    plt.xlabel("x [μm]")
    plt.ylabel("y [μm]")
    
    
    if plot_avg_of_avgs:
        cbar_label = "Averaged Rydberg Density"
    else:
        cbar_label = "Rydberg Density"
        
    plt.colorbar(sm, ax=ax, label=cbar_label)

開始！

ツールを読み込みます。


import numpy as np
import matplotlib.pyplot as plt

from braket.ahs.atom_arrangement import AtomArrangement
from braket.ahs.analog_hamiltonian_simulation import AnalogHamiltonianSimulation
from braket.devices import LocalSimulator

ちょっと原子配列を見る関数を改造します。


def show_register_moji(
    register: AtomArrangement, 
    blockade_radius: float=0.0, 
    what_to_draw: str="bond", 
    show_atom_index:bool=True
):
    """Plot the given register 
        Args:
            register (AtomArrangement): A given register
            blockade_radius (float): The blockade radius for the register. Default is 0
            what_to_draw (str): Either "bond" or "circle" to indicate the blockade region. 
                Default is "bond"
            show_atom_index (bool): Whether showing the indices of the atoms. Default is True
        
    """
    filled_sites = [site.coordinate for site in register if site.site_type == SiteType.FILLED]
    empty_sites = [site.coordinate for site in register if site.site_type == SiteType.VACANT]
    
    fig = plt.figure(figsize=(7,7))
    if filled_sites:
        plt.plot(np.array(filled_sites)[:, 0], np.array(filled_sites)[:, 1], 'r.', ms=30, label='filled')
    if empty_sites:
        plt.plot(np.array(empty_sites)[:, 0], np.array(empty_sites)[:, 1], 'k.', ms=5, label='empty')
    plt.legend(bbox_to_anchor=(1.1, 1.05))
    
    if show_atom_index:
        for idx, site in enumerate(register):
            plt.text(*site.coordinate, f"  {idx}", fontsize=12)
    
    if blockade_radius > 0 and what_to_draw=="bond":
        for i in range(len(filled_sites)):
            for j in range(i+1, len(filled_sites)):            
                dist = np.linalg.norm(np.array(filled_sites[i]) - np.array(filled_sites[j]))
                if dist <= blockade_radius:
                    plt.plot([filled_sites[i][0], filled_sites[j][0]], [filled_sites[i][1], filled_sites[j][1]], 'b')
                    
    if blockade_radius > 0 and what_to_draw=="circle":
        for site in filled_sites:
            plt.gca().add_patch( plt.Circle((site[0],site[1]), blockade_radius/2, color="b", alpha=0.3) )
        plt.gca().set_aspect(1)
    plt.show()

早速描画します


register1 = AtomArrangement()

s = 6.7e-6

position = []

position.append((0,2))
position.append((0,3))
position.append((0,4))
position.append((0,5))
position.append((0,6))

position.append((1,1))
position.append((1,2))
position.append((1,3))
position.append((1,4))
position.append((1,5))
position.append((1,6))
position.append((1,7))

position.append((2,1))
position.append((2,2))
position.append((2,3))
position.append((2,4))
position.append((2,5))
position.append((2,6))
position.append((2,7))
position.append((2,8))

position.append((3,0))
position.append((3,1))
#position.append((3,2))
#position.append((3,3))
position.append((3,4))
position.append((3,5))
position.append((3,6))
position.append((3,7))
position.append((3,8))
position.append((3,9))

position.append((4,0))
#position.append((4,1))
#position.append((4,2))
position.append((4,3))
#position.append((4,4))
#position.append((4,5))
position.append((4,6))
position.append((4,7))
position.append((4,8))
position.append((4,9))

position.append((5,0))
#position.append((5,1))
position.append((5,2))
#position.append((5,3))
position.append((5,4))
position.append((5,5))
#position.append((5,6))
position.append((5,7))
position.append((5,8))
position.append((5,9))
position.append((5,10))

position.append((6,0))
#position.append((6,1))
position.append((6,2))
position.append((6,3))
#position.append((6,4))
#position.append((6,5))
position.append((6,6))
position.append((6,7))
position.append((6,8))
position.append((6,9))
position.append((6,10))
position.append((6,11))

position.append((7,0))
#position.append((7,1))
position.append((7,2))
position.append((7,3))
position.append((7,4))
position.append((7,5))
position.append((7,6))
position.append((7,7))
position.append((7,8))
position.append((7,9))
position.append((7,10))
position.append((7,11))
position.append((7,12))
position.append((7,13))

position.append((6+2,0))
#position.append((6+2,1))
position.append((6+2,2))
position.append((6+2,3))
#position.append((6+2,4))
#position.append((6+2,5))
position.append((6+2,6))
position.append((6+2,7))
position.append((6+2,8))
position.append((6+2,9))
position.append((6+2,10))
position.append((6+2,11))

position.append((5+4,0))
#position.append((5+4,1))
position.append((5+4,2))
#position.append((5+4,3))
position.append((5+4,4))
position.append((5+4,5))
#position.append((5+4,6))
position.append((5+4,7))
position.append((5+4,8))
position.append((5+4,9))
position.append((5+4,10))

position.append((4+6,0))
#position.append((4+6,1))
#position.append((4+6,2))
position.append((4+6,3))
#position.append((4+6,4))
#position.append((4+6,5))
position.append((4+6,6))
position.append((4+6,7))
position.append((4+6,8))
position.append((4+6,9))

position.append((3+8,0))
position.append((3+8,1))
#position.append((3+8,2))
#position.append((3+8,3))
position.append((3+8,4))
position.append((3+8,5))
position.append((3+8,6))
position.append((3+8,7))
position.append((3+8,8))
position.append((3+8,9))

position.append((2+10,1))
position.append((2+10,2))
position.append((2+10,3))
position.append((2+10,4))
position.append((2+10,5))
position.append((2+10,6))
position.append((2+10,7))
position.append((2+10,8))

position.append((1+12,1))
position.append((1+12,2))
position.append((1+12,3))
position.append((1+12,4))
position.append((1+12,5))
position.append((1+12,6))
position.append((1+12,7))

position.append((0+14,2))
position.append((0+14,3))
position.append((0+14,4))
position.append((0+14,5))
position.append((0+14,6))

for pos in position:
    register1.add((pos[0]*s,pos[1]*s))
    
show_register_moji(register1, show_atom_index=False)

<Figure size 504x504 with 1 Axes>

どうやらこれを実機に投げようとしたら横幅が大きすぎるようです。もう一つ作ってみます。


register2 = AtomArrangement()

s = 6.7e-6

position = []

position.append((0,1))
position.append((0,2))
position.append((0,3))
position.append((0,4))

position.append((1,4))
position.append((1,5))

position.append((2,1))
position.append((2,2))
position.append((2,3))
position.append((2,4))
position.append((2,5))
position.append((2,6))
position.append((2,8))

position.append((3,0))
position.append((3,2))
position.append((3,3))
position.append((3,4))
#position.append((3,5))
position.append((3,6))
position.append((3,7))

position.append((4,0))
position.append((4,2))
position.append((4,3))
position.append((4,4))
position.append((4,5))
position.append((4,6))

position.append((5,2))
position.append((5,3))
position.append((5,4))
position.append((5,5))
position.append((5,6))

position.append((4+2,0))
position.append((4+2,2))
position.append((4+2,3))
position.append((4+2,4))
position.append((4+2,5))
position.append((4+2,6))

position.append((3+4,0))
position.append((3+4,2))
position.append((3+4,3))
position.append((3+4,4))
#position.append((3+4,5))
position.append((3+4,6))
position.append((3+4,7))

position.append((2+6,1))
position.append((2+6,2))
position.append((2+6,3))
position.append((2+6,4))
position.append((2+6,5))
position.append((2+6,6))
position.append((2+6,8))

position.append((1+8,4))
position.append((1+8,5))

position.append((0+10,1))
position.append((0+10,2))
position.append((0+10,3))
position.append((0+10,4))

for pos in position:
    register2.add((pos[0]*s,pos[1]*s))
    
show_register_moji(register, show_atom_index=False)
#show_register_moji(register2, show_atom_index=False, blockade_radius= 1.5*s)

<Figure size 504x504 with 1 Axes>

時間発展の関数は標準の断熱計算を使います。


time_points = [0, 2.5e-7, 2.75e-6, 3e-6]
amplitude_min = 0
amplitude_max = 1.57e7  # rad / s

detuning_min = -5.5e7  # rad / s
detuning_max = 5.5e7  # rad / s

amplitude_values = [amplitude_min, amplitude_max, amplitude_max, amplitude_min]  # piecewise linear
detuning_values = [detuning_min, detuning_min, detuning_max, detuning_max]  # piecewise linear
phase_values = [0, 0, 0, 0]  # piecewise constant


drive = get_drive(time_points, amplitude_values, detuning_values, phase_values)

show_global_drive(drive);

<Figure size 504x504 with 3 Axes>

横磁場を一定値、リュードベリ状態の操作は最初すべて0に持っていくようにマイナスの値をデルタにかけ、徐々に上げてリュードベリ状態にもっていきます。最終的にはリュードベリ半径内の原子同士はどちらかが0になりますので、パターンの生成に至ります。

上記の配置からマシンやシミュレーションに投げるプログラムを生成します。


ahs_program2 = AnalogHamiltonianSimulation(
    register=register2, 
    hamiltonian=drive
)


from braket.aws import AwsDevice 
device = AwsDevice("arn:aws:braket:us-east-1::device/qpu/quera/Aquila")


task = device.run(ahs_program2, shots=100)

metadata = task.metadata()
task_arn = metadata['quantumTaskArn']
task_status = metadata['status']

print(f"ARN: {task_arn}")
print(f"status: {task_status}")

ARN: arn:aws:braket:us-east-1:722034924650:quantum-task/10114035-bd83-42a4-821b-4a9441e2faa8

status: CREATED

実機が動いたら結果を共有します。以上です。

中性原子アナログ量子コンピュータでキャラクターを描く

Yuichiro Minato

開始！.css-1yssqf7{opacity:0.6;border:0;border-color:inherit;border-style:solid;border-bottom-width:1px;width:100%;margin-top:var(--chakra-space-2);}

開始！