API specification

The emu-mps API is based on the specification here. Concretely, the classes are as follows:

MPSBackend

Bases: EmulatorBackend

A backend for emulating Pulser sequences using Matrix Product States (MPS), aka tensor trains.

Source code in pulser/backend/abc.py

def __init__(
    self,
    sequence: pulser.Sequence,
    *,
    config: EmulationConfig | None = None,
    mimic_qpu: bool = False,
) -> None:
    """Initializes the backend."""
    super().__init__(sequence, mimic_qpu=mimic_qpu)
    config = config or self.default_config
    if not isinstance(config, EmulationConfig):
        raise TypeError(
            "'config' must be an instance of 'EmulationConfig', "
            f"not {type(config)}."
        )
    # See the BackendConfig definition to see why this works
    self._config = type(self.default_config)(**config._backend_options)

resume(autosave_file) staticmethod

Resume simulation from autosave file. Only resume simulations from data you trust! Unpickling of untrusted data is not safe.

Source code in emu_mps/mps_backend.py

@staticmethod
def resume(autosave_file: str | pathlib.Path) -> Results:
    """
    Resume simulation from autosave file.
    Only resume simulations from data you trust!
    Unpickling of untrusted data is not safe.
    """
    if isinstance(autosave_file, str):
        autosave_file = pathlib.Path(autosave_file)

    if not autosave_file.is_file():
        raise ValueError(f"Not a file: {autosave_file}")

    with open(autosave_file, "rb") as f:
        impl: MPSBackendImpl = pickle.load(f)

    impl.autosave_file = autosave_file
    impl.last_save_time = time.time()
    impl.config.init_logging()  # FIXME: might be best to take logger object out of config.

    logging.getLogger("global_logger").warning(
        f"Resuming simulation from file {autosave_file}\n"
        f"Saving simulation state every {impl.config.autosave_dt} seconds"
    )

    return MPSBackend._run(impl)

run()

Emulates the given sequence.

RETURNS	DESCRIPTION
Results	the simulation results

Source code in emu_mps/mps_backend.py

def run(self) -> Results:
    """
    Emulates the given sequence.

    Returns:
        the simulation results
    """
    assert isinstance(self._config, MPSConfig)

    impl = create_impl(self._sequence, self._config)
    impl.init()  # This is separate from the constructor for testing purposes.

    return self._run(impl)

MPSConfig

Bases: EmulationConfig

The configuration of the emu-mps MPSBackend. The kwargs passed to this class are passed on to the base class. See the API for that class for a list of available options.

PARAMETER	DESCRIPTION
dt	the timestep size that the solver uses. Note that observables are only calculated if the evaluation_times are divisible by dt. TYPE: int DEFAULT: 10
precision	up to what precision the state is truncated TYPE: float DEFAULT: 1e-05
max_bond_dim	the maximum bond dimension that the state is allowed to have. TYPE: int DEFAULT: 1024
max_krylov_dim	the size of the krylov subspace that the Lanczos algorithm maximally builds TYPE: int DEFAULT: 100
extra_krylov_tolerance	the Lanczos algorithm uses this*precision as the convergence tolerance TYPE: float DEFAULT: 0.001
num_gpus_to_use	during the simulation, distribute the state over this many GPUs 0=all factors to cpu. As shown in the benchmarks, using multiple GPUs might alleviate memory pressure per GPU, but the runtime should be similar. TYPE: int DEFAULT: DEVICE_COUNT
autosave_prefix	filename prefix for autosaving simulation state to file TYPE: str DEFAULT: 'emu_mps_save_'
autosave_dt	minimum time interval in seconds between two autosaves Saving the simulation state is only possible at specific times, therefore this interval is only a lower bound. TYPE: int DEFAULT: 600
kwargs	arguments that are passed to the base class TYPE: Any DEFAULT: {}

Examples:

>>> num_gpus_to_use = 2 #use 2 gpus if available, otherwise 1 or cpu
>>> dt = 1 #this will impact the runtime
>>> precision = 1e-6 #smaller dt requires better precision, generally
>>> MPSConfig(num_gpus_to_use=num_gpus_to_use, dt=dt, precision=precision,
>>>     with_modulation=True) #the last arg is taken from the base class

Source code in emu_mps/mps_config.py

def __init__(
    self,
    *,
    dt: int = 10,
    precision: float = 1e-5,
    max_bond_dim: int = 1024,
    max_krylov_dim: int = 100,
    extra_krylov_tolerance: float = 1e-3,
    num_gpus_to_use: int = DEVICE_COUNT,
    interaction_cutoff: float = 0.0,
    log_level: int = logging.INFO,
    log_file: pathlib.Path | None = None,
    autosave_prefix: str = "emu_mps_save_",
    autosave_dt: int = 600,  # 10 minutes
    **kwargs: Any,
):
    kwargs.setdefault("observables", [BitStrings(evaluation_times=[1.0])])
    super().__init__(
        dt=dt,
        precision=precision,
        max_bond_dim=max_bond_dim,
        max_krylov_dim=max_krylov_dim,
        extra_krylov_tolerance=extra_krylov_tolerance,
        num_gpus_to_use=num_gpus_to_use,
        interaction_cutoff=interaction_cutoff,
        log_level=log_level,
        log_file=log_file,
        autosave_prefix=autosave_prefix,
        autosave_dt=autosave_dt,
        **kwargs,
    )

    if "doppler" in self.noise_model.noise_types:
        raise NotImplementedError("Unsupported noise type: doppler")
    if (
        "amplitude" in self.noise_model.noise_types
        and self.noise_model.amp_sigma != 0.0
    ):
        raise NotImplementedError("Unsupported noise type: amp_sigma")

    MIN_AUTOSAVE_DT = 10
    assert (
        self.autosave_dt > MIN_AUTOSAVE_DT
    ), f"autosave_dt must be larger than {MIN_AUTOSAVE_DT} seconds"

    self.monkeypatch_observables()

    self.logger = logging.getLogger("global_logger")
    if log_file is None:
        logging.basicConfig(
            level=log_level, format="%(message)s", stream=sys.stdout, force=True
        )  # default to stream = sys.stderr
    else:
        logging.basicConfig(
            level=log_level,
            format="%(message)s",
            filename=str(log_file),
            filemode="w",
            force=True,
        )
    if (self.noise_model.runs != 1 and self.noise_model.runs is not None) or (
        self.noise_model.samples_per_run != 1
        and self.noise_model.samples_per_run is not None
    ):
        self.logger.warning(
            "Warning: The runs and samples_per_run values of the NoiseModel are ignored!"
        )

MPS

Bases: State[complex, Tensor]

Matrix Product State, aka tensor train.

Each tensor has 3 dimensions ordered as such: (left bond, site, right bond).

Only qubits are supported.

This constructor creates a MPS directly from a list of tensors. It is for internal use only.

PARAMETER	DESCRIPTION
factors	the tensors for each site WARNING: for efficiency in a lot of use cases, this list of tensors IS NOT DEEP-COPIED. Therefore, the new MPS object is not necessarily the exclusive owner of the list and its tensors. As a consequence, beware of potential external modifications affecting the list or the tensors. You are responsible for deciding whether to pass its own exclusive copy of the data to this constructor, or some shared objects. TYPE: List[Tensor]
orthogonality_center	the orthogonality center of the MPS, or None (in which case it will be orthogonalized when needed) TYPE: Optional[int] DEFAULT: None
config	the emu-mps config object passed to the run method TYPE: Optional[MPSConfig] DEFAULT: None
num_gpus_to_use	distribute the factors over this many GPUs 0=all factors to cpu, None=keep the existing device assignment. TYPE: Optional[int] DEFAULT: DEVICE_COUNT

Source code in emu_mps/mps.py

def __init__(
    self,
    factors: List[torch.Tensor],
    /,
    *,
    orthogonality_center: Optional[int] = None,
    config: Optional[MPSConfig] = None,
    num_gpus_to_use: Optional[int] = DEVICE_COUNT,
):
    """
    This constructor creates a MPS directly from a list of tensors. It is for internal use only.

    Args:
        factors: the tensors for each site
            WARNING: for efficiency in a lot of use cases, this list of tensors
            IS NOT DEEP-COPIED. Therefore, the new MPS object is not necessarily
            the exclusive owner of the list and its tensors. As a consequence,
            beware of potential external modifications affecting the list or the tensors.
            You are responsible for deciding whether to pass its own exclusive copy
            of the data to this constructor, or some shared objects.
        orthogonality_center: the orthogonality center of the MPS, or None (in which case
            it will be orthogonalized when needed)
        config: the emu-mps config object passed to the run method
        num_gpus_to_use: distribute the factors over this many GPUs
            0=all factors to cpu, None=keep the existing device assignment.
    """
    self._eigenstates = ["0", "1"]
    self.config = config if config is not None else MPSConfig()
    assert all(
        factors[i - 1].shape[2] == factors[i].shape[0] for i in range(1, len(factors))
    ), "The dimensions of consecutive tensors should match"
    assert (
        factors[0].shape[0] == 1 and factors[-1].shape[2] == 1
    ), "The dimension of the left (right) link of the first (last) tensor should be 1"

    self.factors = factors
    self.num_sites = len(factors)
    assert self.num_sites > 1  # otherwise, do state vector

    assert (orthogonality_center is None) or (
        0 <= orthogonality_center < self.num_sites
    ), "Invalid orthogonality center provided"
    self.orthogonality_center = orthogonality_center

    if num_gpus_to_use is not None:
        assign_devices(self.factors, min(DEVICE_COUNT, num_gpus_to_use))

n_qudits property

The number of qudits in the state.

__add__(other)

Returns the sum of two MPSs, computed with a direct algorithm. The resulting MPS is orthogonalized on the first site and truncated up to self.config.precision.

PARAMETER	DESCRIPTION
other	the other state TYPE: State

RETURNS	DESCRIPTION
MPS	the summed state

Source code in emu_mps/mps.py

def __add__(self, other: State) -> MPS:
    """
    Returns the sum of two MPSs, computed with a direct algorithm.
    The resulting MPS is orthogonalized on the first site and truncated
    up to `self.config.precision`.

    Args:
        other: the other state

    Returns:
        the summed state
    """
    assert isinstance(other, MPS), "Other state also needs to be an MPS"
    new_tt = add_factors(self.factors, other.factors)
    result = MPS(
        new_tt,
        config=self.config,
        num_gpus_to_use=None,
        orthogonality_center=None,  # Orthogonality is lost.
    )
    result.truncate()
    return result

__rmul__(scalar)

Multiply an MPS by a scalar.

PARAMETER	DESCRIPTION
scalar	the scale factor TYPE: complex

RETURNS	DESCRIPTION
MPS	the scaled MPS

Source code in emu_mps/mps.py

def __rmul__(self, scalar: complex) -> MPS:
    """
    Multiply an MPS by a scalar.

    Args:
        scalar: the scale factor

    Returns:
        the scaled MPS
    """
    which = (
        self.orthogonality_center
        if self.orthogonality_center is not None
        else 0  # No need to orthogonalize for scaling.
    )
    factors = scale_factors(self.factors, scalar, which=which)
    return MPS(
        factors,
        config=self.config,
        num_gpus_to_use=None,
        orthogonality_center=self.orthogonality_center,
    )

apply(qubit_index, single_qubit_operator)

Apply given single qubit operator to qubit qubit_index, leaving the MPS orthogonalized on that qubit.

Source code in emu_mps/mps.py

def apply(self, qubit_index: int, single_qubit_operator: torch.Tensor) -> None:
    """
    Apply given single qubit operator to qubit qubit_index, leaving the MPS
    orthogonalized on that qubit.
    """
    self.orthogonalize(qubit_index)

    self.factors[qubit_index] = torch.tensordot(
        self.factors[qubit_index],
        single_qubit_operator.to(self.factors[qubit_index].device),
        ([1], [1]),
    ).transpose(1, 2)

expect_batch(single_qubit_operators)

Computes expectation values for each qubit and each single qubit operator in the batched input tensor.

Returns a tensor T such that T[q, i] is the expectation value for qubit #q and operator single_qubit_operators[i].

Source code in emu_mps/mps.py

def expect_batch(self, single_qubit_operators: torch.Tensor) -> torch.Tensor:
    """
    Computes expectation values for each qubit and each single qubit operator in
    the batched input tensor.

    Returns a tensor T such that T[q, i] is the expectation value for qubit #q
    and operator single_qubit_operators[i].
    """
    orthogonality_center = (
        self.orthogonality_center
        if self.orthogonality_center is not None
        else self.orthogonalize(0)
    )

    result = torch.zeros(
        self.num_sites, single_qubit_operators.shape[0], dtype=torch.complex128
    )

    center_factor = self.factors[orthogonality_center]
    for qubit_index in range(orthogonality_center, self.num_sites):
        temp = torch.tensordot(center_factor.conj(), center_factor, ([0, 2], [0, 2]))

        result[qubit_index] = torch.tensordot(
            single_qubit_operators.to(temp.device), temp, dims=2
        )

        if qubit_index < self.num_sites - 1:
            _, r = torch.linalg.qr(center_factor.reshape(-1, center_factor.shape[2]))
            center_factor = torch.tensordot(
                r, self.factors[qubit_index + 1].to(r.device), dims=1
            )

    center_factor = self.factors[orthogonality_center]
    for qubit_index in range(orthogonality_center - 1, -1, -1):
        _, r = torch.linalg.qr(
            center_factor.reshape(center_factor.shape[0], -1).mT,
        )
        center_factor = torch.tensordot(
            self.factors[qubit_index],
            r.to(self.factors[qubit_index].device),
            ([2], [1]),
        )

        temp = torch.tensordot(center_factor.conj(), center_factor, ([0, 2], [0, 2]))

        result[qubit_index] = torch.tensordot(
            single_qubit_operators.to(temp.device), temp, dims=2
        )

    return result

get_correlation_matrix(*, operator=n_operator)

Efficiently compute the symmetric correlation matrix C_ij = in basis ("r", "g").

PARAMETER	DESCRIPTION
operator	a 2x2 Torch tensor to use TYPE: Tensor DEFAULT: n_operator

RETURNS	DESCRIPTION
Tensor	the corresponding correlation matrix

Source code in emu_mps/mps.py

def get_correlation_matrix(
    self, *, operator: torch.Tensor = n_operator
) -> torch.Tensor:
    """
    Efficiently compute the symmetric correlation matrix
        C_ij = <self|operator_i operator_j|self>
    in basis ("r", "g").

    Args:
        operator: a 2x2 Torch tensor to use

    Returns:
        the corresponding correlation matrix
    """
    assert operator.shape == (2, 2)

    result = torch.zeros(self.num_sites, self.num_sites, dtype=torch.complex128)

    for left in range(0, self.num_sites):
        self.orthogonalize(left)
        accumulator = torch.tensordot(
            self.factors[left],
            operator.to(self.factors[left].device),
            dims=([1], [0]),
        )
        accumulator = torch.tensordot(
            accumulator, self.factors[left].conj(), dims=([0, 2], [0, 1])
        )
        result[left, left] = accumulator.trace().item().real
        for right in range(left + 1, self.num_sites):
            partial = torch.tensordot(
                accumulator.to(self.factors[right].device),
                self.factors[right],
                dims=([0], [0]),
            )
            partial = torch.tensordot(
                partial, self.factors[right].conj(), dims=([0], [0])
            )

            result[left, right] = (
                torch.tensordot(
                    partial, operator.to(partial.device), dims=([0, 2], [0, 1])
                )
                .trace()
                .item()
                .real
            )
            result[right, left] = result[left, right]
            accumulator = tensor_trace(partial, 0, 2)

    return result

get_max_bond_dim()

Return the max bond dimension of this MPS.

RETURNS	DESCRIPTION
int	the largest bond dimension in the state

Source code in emu_mps/mps.py

def get_max_bond_dim(self) -> int:
    """
    Return the max bond dimension of this MPS.

    Returns:
        the largest bond dimension in the state
    """
    return max((x.shape[2] for x in self.factors), default=0)

get_memory_footprint()

Returns the number of MBs of memory occupied to store the state

RETURNS	DESCRIPTION
float	the memory in MBs

Source code in emu_mps/mps.py

def get_memory_footprint(self) -> float:
    """
    Returns the number of MBs of memory occupied to store the state

    Returns:
        the memory in MBs
    """
    return (  # type: ignore[no-any-return]
        sum(factor.element_size() * factor.numel() for factor in self.factors) * 1e-6
    )

inner(other)

Compute the inner product between this state and other. Note that self is the left state in the inner product, so this function is linear in other, and anti-linear in self

PARAMETER	DESCRIPTION
other	the other state TYPE: State

RETURNS	DESCRIPTION
Tensor	inner product

Source code in emu_mps/mps.py

def inner(self, other: State) -> torch.Tensor:
    """
    Compute the inner product between this state and other.
    Note that self is the left state in the inner product,
    so this function is linear in other, and anti-linear in self

    Args:
        other: the other state

    Returns:
        inner product
    """
    assert isinstance(other, MPS), "Other state also needs to be an MPS"
    assert (
        self.num_sites == other.num_sites
    ), "States do not have the same number of sites"

    acc = torch.ones(1, 1, dtype=self.factors[0].dtype, device=self.factors[0].device)

    for i in range(self.num_sites):
        acc = acc.to(self.factors[i].device)
        acc = torch.tensordot(acc, other.factors[i].to(acc.device), dims=1)
        acc = torch.tensordot(self.factors[i].conj(), acc, dims=([0, 1], [0, 1]))

    return acc.reshape(1)[0].cpu()

make(num_sites, config=None, num_gpus_to_use=DEVICE_COUNT) classmethod

Returns a MPS in ground state |000..0>.

PARAMETER	DESCRIPTION
num_sites	the number of qubits TYPE: int
config	the MPSConfig TYPE: Optional[MPSConfig] DEFAULT: None
num_gpus_to_use	distribute the factors over this many GPUs 0=all factors to cpu TYPE: int DEFAULT: DEVICE_COUNT

Source code in emu_mps/mps.py

@classmethod
def make(
    cls,
    num_sites: int,
    config: Optional[MPSConfig] = None,
    num_gpus_to_use: int = DEVICE_COUNT,
) -> MPS:
    """
    Returns a MPS in ground state |000..0>.

    Args:
        num_sites: the number of qubits
        config: the MPSConfig
        num_gpus_to_use: distribute the factors over this many GPUs
            0=all factors to cpu
    """
    config = config if config is not None else MPSConfig()

    if num_sites <= 1:
        raise ValueError("For 1 qubit states, do state vector")

    return cls(
        [
            torch.tensor([[[1.0], [0.0]]], dtype=torch.complex128)
            for _ in range(num_sites)
        ],
        config=config,
        num_gpus_to_use=num_gpus_to_use,
        orthogonality_center=0,  # Arbitrary: every qubit is an orthogonality center.
    )

norm()

Computes the norm of the MPS.

Source code in emu_mps/mps.py

def norm(self) -> torch.Tensor:
    """Computes the norm of the MPS."""
    orthogonality_center = (
        self.orthogonality_center
        if self.orthogonality_center is not None
        else self.orthogonalize(0)
    )
    # the torch.norm function is not properly typed.
    return self.factors[orthogonality_center].norm().cpu()  # type: ignore[no-any-return]

orthogonalize(desired_orthogonality_center=0)

Orthogonalize the state on the given orthogonality center.

Returns the new orthogonality center index as an integer, this is convenient for type-checking purposes.

Source code in emu_mps/mps.py

def orthogonalize(self, desired_orthogonality_center: int = 0) -> int:
    """
    Orthogonalize the state on the given orthogonality center.

    Returns the new orthogonality center index as an integer,
    this is convenient for type-checking purposes.
    """
    assert (
        0 <= desired_orthogonality_center < self.num_sites
    ), f"Cannot move orthogonality center to nonexistent qubit #{desired_orthogonality_center}"

    lr_swipe_start = (
        self.orthogonality_center if self.orthogonality_center is not None else 0
    )

    for i in range(lr_swipe_start, desired_orthogonality_center):
        q, r = torch.linalg.qr(self.factors[i].reshape(-1, self.factors[i].shape[2]))
        self.factors[i] = q.reshape(self.factors[i].shape[0], 2, -1)
        self.factors[i + 1] = torch.tensordot(
            r.to(self.factors[i + 1].device), self.factors[i + 1], dims=1
        )

    rl_swipe_start = (
        self.orthogonality_center
        if self.orthogonality_center is not None
        else (self.num_sites - 1)
    )

    for i in range(rl_swipe_start, desired_orthogonality_center, -1):
        q, r = torch.linalg.qr(
            self.factors[i].reshape(self.factors[i].shape[0], -1).mT,
        )
        self.factors[i] = q.mT.reshape(-1, 2, self.factors[i].shape[2])
        self.factors[i - 1] = torch.tensordot(
            self.factors[i - 1], r.to(self.factors[i - 1].device), ([2], [1])
        )

    self.orthogonality_center = desired_orthogonality_center

    return desired_orthogonality_center

overlap(other)

Compute the overlap of this state and other. This is defined as \(|\langle self | other \rangle |^2\)

Source code in emu_mps/mps.py

def overlap(self, other: State, /) -> torch.Tensor:
    """
    Compute the overlap of this state and other. This is defined as
    $|\\langle self | other \\rangle |^2$
    """
    return torch.abs(self.inner(other)) ** 2  # type: ignore[no-any-return]

sample(*, num_shots, one_state=None, p_false_pos=0.0, p_false_neg=0.0)

Samples bitstrings, taking into account the specified error rates.

PARAMETER	DESCRIPTION
num_shots	how many bitstrings to sample TYPE: int
p_false_pos	the rate at which a 0 is read as a 1 TYPE: float DEFAULT: 0.0
p_false_neg	the rate at which a 1 is read as a 0 TYPE: float DEFAULT: 0.0

RETURNS	DESCRIPTION
Counter[str]	the measured bitstrings, by count

Source code in emu_mps/mps.py

def sample(
    self,
    *,
    num_shots: int,
    one_state: Eigenstate | None = None,
    p_false_pos: float = 0.0,
    p_false_neg: float = 0.0,
) -> Counter[str]:
    """
    Samples bitstrings, taking into account the specified error rates.

    Args:
        num_shots: how many bitstrings to sample
        p_false_pos: the rate at which a 0 is read as a 1
        p_false_neg: the rate at which a 1 is read as a 0

    Returns:
        the measured bitstrings, by count
    """
    assert one_state in {None, "r", "1"}
    self.orthogonalize(0)

    rnd_matrix = torch.rand(num_shots, self.num_sites).to(self.factors[0].device)

    bitstrings: Counter[str] = Counter()

    # Shots are performed in batches.
    # Larger max_batch_size is faster but uses more memory.
    max_batch_size = 32

    shots_done = 0
    while shots_done < num_shots:
        batch_size = min(max_batch_size, num_shots - shots_done)
        batched_accumulator = torch.ones(
            batch_size, 1, dtype=torch.complex128, device=self.factors[0].device
        )

        batch_outcomes = torch.empty(batch_size, self.num_sites, dtype=torch.bool)

        for qubit, factor in enumerate(self.factors):
            batched_accumulator = torch.tensordot(
                batched_accumulator.to(factor.device), factor, dims=1
            )

            # Probability of measuring qubit == 0 for each shot in the batch
            probas = (
                torch.linalg.vector_norm(batched_accumulator[:, 0, :], dim=1) ** 2
            )

            outcomes = (
                rnd_matrix[shots_done : shots_done + batch_size, qubit].to(
                    factor.device
                )
                > probas
            )
            batch_outcomes[:, qubit] = outcomes

            # Batch collapse qubit
            tmp = torch.stack((~outcomes, outcomes), dim=1).to(dtype=torch.complex128)

            batched_accumulator = (
                torch.tensordot(batched_accumulator, tmp, dims=([1], [1]))
                .diagonal(dim1=0, dim2=2)
                .transpose(1, 0)
            )
            batched_accumulator /= torch.sqrt(
                (~outcomes) * probas + outcomes * (1 - probas)
            ).unsqueeze(1)

        shots_done += batch_size

        for outcome in batch_outcomes:
            bitstrings.update(["".join("0" if x == 0 else "1" for x in outcome)])

    if p_false_neg > 0 or p_false_pos > 0:
        bitstrings = apply_measurement_errors(
            bitstrings,
            p_false_pos=p_false_pos,
            p_false_neg=p_false_neg,
        )
    return bitstrings

truncate()

SVD based truncation of the state. Puts the orthogonality center at the first qubit. Calls orthogonalize on the last qubit, and then sweeps a series of SVDs right-left. Uses self.config for determining accuracy. An in-place operation.

Source code in emu_mps/mps.py

def truncate(self) -> None:
    """
    SVD based truncation of the state. Puts the orthogonality center at the first qubit.
    Calls orthogonalize on the last qubit, and then sweeps a series of SVDs right-left.
    Uses self.config for determining accuracy.
    An in-place operation.
    """
    self.orthogonalize(self.num_sites - 1)
    truncate_impl(self.factors, config=self.config)
    self.orthogonality_center = 0

inner

Wrapper around MPS.inner.

PARAMETER	DESCRIPTION
left	the anti-linear argument TYPE: MPS
right	the linear argument TYPE: MPS

RETURNS	DESCRIPTION
Tensor	the inner product

Source code in emu_mps/mps.py

def inner(left: MPS, right: MPS) -> torch.Tensor:
    """
    Wrapper around MPS.inner.

    Args:
        left: the anti-linear argument
        right: the linear argument

    Returns:
        the inner product
    """
    return left.inner(right)

MPO

Bases: Operator[complex, Tensor, MPS]

Matrix Product Operator.

Each tensor has 4 dimensions ordered as such: (left bond, output, input, right bond).

PARAMETER	DESCRIPTION
factors	the tensors making up the MPO TYPE: List[Tensor]

Source code in emu_mps/mpo.py

def __init__(
    self, factors: List[torch.Tensor], /, num_gpus_to_use: Optional[int] = None
):
    self.factors = factors
    self.num_sites = len(factors)
    if not self.num_sites > 1:
        raise ValueError("For 1 qubit states, do state vector")
    if factors[0].shape[0] != 1 or factors[-1].shape[-1] != 1:
        raise ValueError(
            "The dimension of the left (right) link of the first (last) tensor should be 1"
        )
    assert all(
        factors[i - 1].shape[-1] == factors[i].shape[0]
        for i in range(1, self.num_sites)
    )

    if num_gpus_to_use is not None:
        assign_devices(self.factors, min(DEVICE_COUNT, num_gpus_to_use))

__add__(other)

Returns the sum of two MPOs, computed with a direct algorithm. The result is currently not truncated

PARAMETER	DESCRIPTION
other	the other operator TYPE: MPO

RETURNS	DESCRIPTION
MPO	the summed operator

Source code in emu_mps/mpo.py

def __add__(self, other: MPO) -> MPO:
    """
    Returns the sum of two MPOs, computed with a direct algorithm.
    The result is currently not truncated

    Args:
        other: the other operator

    Returns:
        the summed operator
    """
    assert isinstance(other, MPO), "MPO can only be added to another MPO"
    sum_factors = add_factors(self.factors, other.factors)
    return MPO(sum_factors)

__matmul__(other)

Compose two operators. The ordering is that self is applied after other.

PARAMETER	DESCRIPTION
other	the operator to compose with self TYPE: MPO

RETURNS	DESCRIPTION
MPO	the composed operator

Source code in emu_mps/mpo.py

def __matmul__(self, other: MPO) -> MPO:
    """
    Compose two operators. The ordering is that
    self is applied after other.

    Args:
        other: the operator to compose with self

    Returns:
        the composed operator
    """
    assert isinstance(other, MPO), "MPO can only be applied to another MPO"
    factors = zip_right(self.factors, other.factors)
    return MPO(factors)

__rmul__(scalar)

Multiply an MPO by scalar. Assumes the orthogonal centre is on the first factor.

PARAMETER	DESCRIPTION
scalar	the scale factor to multiply with TYPE: complex

RETURNS	DESCRIPTION
MPO	the scaled MPO

Source code in emu_mps/mpo.py

def __rmul__(self, scalar: complex) -> MPO:
    """
    Multiply an MPO by scalar.
    Assumes the orthogonal centre is on the first factor.

    Args:
        scalar: the scale factor to multiply with

    Returns:
        the scaled MPO
    """
    factors = scale_factors(self.factors, scalar, which=0)
    return MPO(factors)

apply_to(other)

Applies this MPO to the given MPS. The returned MPS is:

- othogonal on the first site
- truncated up to `other.precision`
- distributed on the same devices of `other`

PARAMETER	DESCRIPTION
other	the state to apply this operator to TYPE: MPS

RETURNS	DESCRIPTION
MPS	the resulting state

Source code in emu_mps/mpo.py

def apply_to(self, other: MPS) -> MPS:
    """
    Applies this MPO to the given MPS.
    The returned MPS is:

        - othogonal on the first site
        - truncated up to `other.precision`
        - distributed on the same devices of `other`

    Args:
        other: the state to apply this operator to

    Returns:
        the resulting state
    """
    assert isinstance(other, MPS), "MPO can only be multiplied with MPS"
    factors = zip_right(
        self.factors,
        other.factors,
        config=other.config,
    )
    return MPS(factors, orthogonality_center=0)

expect(state)

Compute the expectation value of self on the given state.

PARAMETER	DESCRIPTION
state	the state with which to compute TYPE: State

RETURNS	DESCRIPTION
Tensor	the expectation

Source code in emu_mps/mpo.py

def expect(self, state: State) -> torch.Tensor:
    """
    Compute the expectation value of self on the given state.

    Args:
        state: the state with which to compute

    Returns:
        the expectation
    """
    assert isinstance(
        state, MPS
    ), "currently, only expectation values of MPSs are \
    supported"
    acc = torch.ones(
        1, 1, 1, dtype=state.factors[0].dtype, device=state.factors[0].device
    )
    n = len(self.factors) - 1
    for i in range(n):
        acc = new_left_bath(acc, state.factors[i], self.factors[i]).to(
            state.factors[i + 1].device
        )
    acc = new_left_bath(acc, state.factors[n], self.factors[n])
    return acc.reshape(1)[0].cpu()