Source code for qrisp.misc.utility

"""
\********************************************************************************
* Copyright (c) 2023 the Qrisp authors
*
* This program and the accompanying materials are made available under the
* terms of the Eclipse Public License 2.0 which is available at
* http://www.eclipse.org/legal/epl-2.0.
*
* This Source Code may also be made available under the following Secondary
* Licenses when the conditions for such availability set forth in the Eclipse
* Public License, v. 2.0 are satisfied: GNU General Public License, version 2
* with the GNU Classpath Exception which is
* available at https://www.gnu.org/software/classpath/license.html.
*
* SPDX-License-Identifier: EPL-2.0 OR GPL-2.0 WITH Classpath-exception-2.0
********************************************************************************/
"""


import traceback

import numpy as np
import sympy


def bin_rep(n, bits):
    if n < 0:
        raise Exception("Only positive numbers are supported")

    if n >= 2**bits:
        raise Exception(
            str(n) + " can't be represented as a " + str(bits) + " bit number"
        )

    zero_string = "".join(["0" for k in range(bits)])
    return (zero_string + bin(n)[2:])[-bits:]


def int_encoder(qv, encoding_number):
    if encoding_number > 2 ** len(qv) - 1:
        raise Exception("Not enough qubits to encode integer " + str(encoding_number))

    binary_rep = bin_rep(encoding_number, len(qv))[::-1]
    from qrisp import x

    for i in range(len(binary_rep)):
        if int(binary_rep[i]):
            x(qv[i])


# Calculates the binary expression of a given integer and returns it as an array of
# length bits
def int_as_array(k, bit):
    bin_str = bin_rep(k, bit)
    return np.array([int(c) for c in bin_str])


def array_as_int(array):
    result = 0
    for k in range(len(array)):
        if array[::-1][k]:
            result += 2 ** (k)

    return result


# Decomposes the circuit qc until no more decompositions are possible and then counts
# the cnot operations
def cnot_count(qc):
    qc = qc.transpile()

    gate_count_dic = qc.count_ops()

    try:
        return gate_count_dic["cx"]
    except KeyError:
        return 0


def is_inv(x, bit):
    # return (math.gcd(int(np.round(x, 3)),2**bit) == 1)

    # The only divisors 2**bit has is powers of 2
    # ie. if tha factorization of x doesn't contain any powers of 2 it is invertible
    # in other words: x is invertible if it is uneven
    return bool(int(x) % 2)


def get_depth_dic(qc, transpile_qc=True, depth_indicator = lambda x : 1):
    if len(qc.qubits) == 0:
        return {}

    if transpile_qc:
        qc = qc.transpile()

    # Assign each bit in the circuit a unique integer
    # to index into op_stack.
    bit_indices = {bit: idx for idx, bit in enumerate(qc.qubits + qc.clbits)}

    # If no bits, return 0
    if not bit_indices:
        return 0

    # A list that holds the height of each qubit
    # and classical bit.
    op_stack = [0] * len(bit_indices)

    # Here we are playing a modified version of
    # Tetris where we stack gates, but multi-qubit
    # gates, or measurements have a block for each
    # qubit or cbit that are connected by a virtual
    # line so that they all stacked at the same depth.
    # Conditional gates act on all cbits in the register
    # they are conditioned on.
    # We treat barriers or snapshots different as
    # They are transpiler and simulator directives.
    # The max stack height is the circuit depth.

    for instr in qc.data:
        if instr.op.name in ["qb_alloc", "qb_dealloc", "gphase"]:
            continue
        qargs = instr.qubits
        cargs = instr.clbits

        levels = []
        reg_ints = []
        # If count then add one to stack heights
        
        gate_depth = depth_indicator(instr.op)
        
        for ind, reg in enumerate(qargs + cargs):
            # Add to the stacks of the qubits and
            # cbits used in the gate.
            reg_ints.append(bit_indices[reg])
            levels.append(op_stack[reg_ints[ind]] + gate_depth)

        max_level = max(levels)
        for ind in reg_ints:
            op_stack[ind] = max_level

    return {qc.qubits[i]: op_stack[i] for i in range(len(qc.qubits))}


[docs]def gate_wrap(*args, permeability=None, is_qfree=None, name=None, verify=False): """ Decorator to bundle up the quantum instructions of a function into a single gate object. Bundled gate objects can help debugging as it allows for a more clear QuantumCircuit visualisation. Furthermore, bundling up functions is relevant for Qrisps uncomputation algorithm. When bundling up for uncomputation, this decorator provides the means to annotate the gate objects with information about its permeability and qfree-ness. For further information about these concepts check the :ref:`uncomputation documentation<uncomputation>`. Specifying this information allows to skip the computationally costly automatic determination at runtime. Note that the specified information is not checked for correctness as this would defy the purpose. A shorthand for ``gate_wrap(permeability = "args", is_qfree = True)`` is the :meth:`lifted <qrisp.lifted>` decorator. .. warning:: Using ``gate_wrap`` without specifying permeability and ``qfree``-ness on functions processing a lot of qubits, can causes long compile times, since the unitaries of these gates have to be determined numerically. .. warning:: Incorrect information about permeability and ``qfree``-ness can yield incorrect compilation results. If you are unsure, use the ``verify`` keyword on a small scale first. Parameters ---------- permeability : string or list, optional Specify the permeability behavior of the function. When given "args", it is assumed that the gate is permeable only on the qubits of the arguments. When given "full", it is assumed that the gate is permeable on every qubit it acts on (i.e. also the result). When given a list of integers it is assumed, that the gate is permeable on the qubits of the arguments corresponding to the integers. The default is None. is_qfree : bool, optional Specify the qfree-ness of the function. The default is None. name : string, optional String which will be used for naming the gate object. The default is None. verify : bool, optional If set to ``True``, the specified information about permeability and ``qfree``-ness will be checked numerically. The default is ``False``. Examples -------- We create a simple function wrapping up multiple gates: :: from qrisp import QuantumVariable, cx, x, h, z, gate_wrap @gate_wrap def example_function(a, b): cx(a,b) x(a) cx(b,a) h(b) a = QuantumVariable(3) b = QuantumVariable(3) example_function(a, b) >>> print(a.qs) :: QuantumCircuit: -------------- ┌───────────────────┐ b.0: ┤0 ├ │ │ b.1: ┤1 ├ │ │ b.2: ┤2 ├ │ example_function │ a.0: ┤3 ├ │ │ a.1: ┤4 ├ │ │ a.2: ┤5 ├ └───────────────────┘ Live QuantumVariables: --------------------- QuantumVariable a QuantumVariable b >>> print(a.qs.transpile()) :: ┌───┐ ┌───┐ b.0: ┤ X ├─────────────────■──┤ H ├────────── └─┬─┘┌───┐ │ └───┘┌───┐ b.1: ──┼──┤ X ├────────────┼────■──┤ H ├───── │ └─┬─┘┌───┐ │ │ └───┘┌───┐ b.2: ──┼────┼──┤ X ├───────┼────┼────■──┤ H ├ │ │ └─┬─┘┌───┐┌─┴─┐ │ │ └───┘ a.0: ──■────┼────┼──┤ X ├┤ X ├──┼────┼─────── │ │ ├───┤└───┘┌─┴─┐ │ a.1: ───────■────┼──┤ X ├─────┤ X ├──┼─────── │ ├───┤ └───┘┌─┴─┐ a.2: ────────────■──┤ X ├──────────┤ X ├───── └───┘ └───┘ In the next example, we create a function that performs no quantum gates and specify that it is qfree and permeable on the second argument but not on the first. :: from qrisp import QuantumCircuit @gate_wrap(permeability = [1], is_qfree = True) def example_function(arg_0, arg_1): res = QuantumVariable(1) #Append an identity gate res.qs.append(QuantumCircuit(3).to_gate(), [arg_0, arg_1, res]) return res qv_0 = QuantumVariable(1) qv_1 = QuantumVariable(1) res = example_function(qv_0, qv_1) >>> print(qv_0.qs) :: QuantumCircuit: -------------- ┌───────────────────┐ qv_0.0: ┤0 ├ │ │ qv_1.0: ┤1 example_function ├ │ │ res.0: ┤2 ├ └───────────────────┘ Live QuantumVariables: --------------------- QuantumVariable qv_0 QuantumVariable qv_1 QuantumVariable res >>> qv_1.uncompute() >>> print(qv_0.qs) :: QuantumCircuit: -------------- ┌───────────────────┐ qv_0.0: ┤0 ├ │ │ qv_1.0: ┤1 example_function ├ │ │ res.0: ┤2 ├ └───────────────────┘ Live QuantumVariables: --------------------- QuantumVariable qv_0 QuantumVariable res Since ``arg_1`` is marked as permeable, there are no further gates required for uncomputation. The situation is different for the other two QuantumVariables, where #the qubits are not marked as permeable. >>> qv_0.uncompute(do_it = False) >>> res.uncompute() >>> print(qv_0.qs) :: QuantumCircuit: -------------- ┌───────────────────┐┌──────────────────────┐ qv_0.0: ┤0 ├┤0 ├ │ ││ │ qv_1.0: ┤1 example_function ├┤1 example_function_dg ├ │ ││ │ res.0: ┤2 ├┤2 ├ └───────────────────┘└──────────────────────┘ Live QuantumVariables: --------------------- """ if len(args): return gate_wrap_inner(args[0]) else: def gate_wrap_helper(function): return gate_wrap_inner( function, permeability=permeability, is_qfree=is_qfree, name=name, verify=verify, ) return gate_wrap_helper
def gate_wrap_inner( function, permeability=None, is_qfree=None, name=None, verify=False ): def wrapped_function( *args, permeability=permeability, is_qfree=is_qfree, verify=verify, **kwargs ): wrapped_function.__name__ = function.__name__ from qrisp.circuit import Qubit from qrisp.core import recursive_qs_search, recursive_qv_search from qrisp.environments import GateWrapEnvironment from qrisp import merge, QuantumVariable, QuantumArray try: qs = find_qs(args) except: qs_list = recursive_qs_search([args, kwargs]) qs = qs_list[0] initial_qubits = set(qs.qubits) if name is None: gwe = GateWrapEnvironment(name=function.__name__) else: gwe = GateWrapEnvironment(name=name) with gwe: result = function(*args, **kwargs) if len(qs.env_stack): gwe.compile() if gwe in qs.data: qs.data.remove(gwe) if gwe.instruction is None: return result created_qubits = set(qs.qubits) - initial_qubits ancillas = [] for qb in created_qubits: if qb.allocated is False: ancillas.append(qb) if is_qfree is not None: if verify and is_qfree: from qrisp.uncomputation import is_qfree as is_qfree_function if not is_qfree_function(gwe.instruction.op): raise Exception( f"Verification of qfree-ness for function {function.__name__} " f"failed" ) gwe.instruction.op.is_qfree = is_qfree if permeability is not None: permeability_dict = {i : None for i in range(gwe.instruction.op.num_qubits)} permeable_qubits = [] not_permeable_qubits = [] if isinstance(permeability, list): for i in range(len(args)): if i in permeability: extension_list = permeable_qubits else: extension_list = not_permeable_qubits arg = args[i] if isinstance(arg, QuantumVariable): extension_list += arg.reg elif isinstance(arg, (tuple, list)): for item in arg: if isinstance(item, Qubit): extension_list.append(item) elif isinstance(item, QuantumVariable): extension_list += item.reg elif isinstance(arg, QuantumArray): for qv in arg.flatten(): extension_list += qv.reg if isinstance(result, QuantumVariable): not_permeable_qubits += result.reg elif isinstance(result, (tuple, list)): for item in result: if isinstance(item, Qubit): not_permeable_qubits.append(item) elif isinstance(item, QuantumVariable): not_permeable_qubits += item.reg elif isinstance(result, QuantumArray): for qv in result.flatten(): not_permeable_qubits += qv.reg elif isinstance(permeability, str): for arg in args: if isinstance(arg, QuantumVariable): permeable_qubits += arg.reg elif isinstance(arg, (tuple, list)): for item in arg: if isinstance(item, Qubit): permeable_qubits.append(item) elif isinstance(item, QuantumVariable): permeable_qubits += item.reg elif isinstance(arg, QuantumArray): for qv in arg.flatten(): permeable_qubits += qv.reg if permeability == "full": extension_list = permeable_qubits elif permeability == "args": extension_list = not_permeable_qubits else: raise Exception(f"Don't know permeability option {permeability}") if isinstance(result, QuantumVariable): extension_list += result.reg elif isinstance(result, (tuple, list)): for item in result: if isinstance(item, Qubit): extension_list.append(item) elif isinstance(item, QuantumVariable): extension_list += item.reg elif isinstance(result, QuantumArray): for qv in result.flatten(): extension_list += qv.reg for i in range(len(gwe.instruction.qubits)): qb = gwe.instruction.qubits[i] if qb in permeable_qubits: permeability_dict[i] = True elif qb in not_permeable_qubits: permeability_dict[i] = False elif qb in ancillas: # Even though ancilla qubits are permeable, we want to be able to # use the gate_wrap decorator as an interface to perform # recomputation. If we mark them as permeable, Unqomp won't wrap # the uncomputed gate in alloc/dealloc gates but instead wrap both # the computation gate and the recomputation in alloc/dealloc gates # To undestand this behavior better consider the following example # from qrisp import QuantumFloat # a = QuantumFloat(2) # qf_res = a * a # qf_res.uncompute() # print(qf_res.qs) # If we mark the ancilla qubits as permeable, this gives # QuantumCircuit: # --------------- # ┌──────────┐┌──────────┐┌─────────────┐ # a.0: ┤ qb_alloc ├┤0 ├┤0 ├────────────── # ├──────────┤│ ││ │ # a.1: ┤ qb_alloc ├┤1 ├┤1 ├────────────── # ├──────────┤│ ││ │┌────────────┐ # return.0: ┤ qb_alloc ├┤2 ├┤2 ├┤ qb_dealloc ├ # ├──────────┤│ ││ │├────────────┤ # return.1: ┤ qb_alloc ├┤3 __mul__ ├┤3 __mul___dg ├┤ qb_dealloc ├ # ├──────────┤│ ││ │├────────────┤ # return.2: ┤ qb_alloc ├┤4 ├┤4 ├┤ qb_dealloc ├ # ├──────────┤│ ││ │├────────────┤ # return.3: ┤ qb_alloc ├┤5 ├┤5 ├┤ qb_dealloc ├ # ├──────────┤│ ││ │├────────────┤ # sbp_anc_0.0: ┤ qb_alloc ├┤6 ├┤6 ├┤ qb_dealloc ├ # └──────────┘└──────────┘└─────────────┘└────────────┘ # Live QuantumVariables: # ---------------------- # QuantumFloat a # If we set them to non-permeable, we get instead # QuantumCircuit: # --------------- # ┌──────────┐┌──────────┐ ┌─────────────┐» # a.0: ┤ qb_alloc ├┤0 ├──────────────────────────┤0 ├» # ├──────────┤│ │ │ │» # a.1: ┤ qb_alloc ├┤1 ├──────────────────────────┤1 ├» # ├──────────┤│ │ │ │» # return.0: ┤ qb_alloc ├┤2 ├──────────────────────────┤2 ├» # ├──────────┤│ │ │ │» # return.1: ┤ qb_alloc ├┤3 __mul__ ├──────────────────────────┤3 __mul___dg ├» # ├──────────┤│ │ │ │» # return.2: ┤ qb_alloc ├┤4 ├──────────────────────────┤4 ├» # ├──────────┤│ │ │ │» # return.3: ┤ qb_alloc ├┤5 ├──────────────────────────┤5 ├» # ├──────────┤│ │┌────────────┐┌──────────┐│ │» # sbp_anc_0.0: ┤ qb_alloc ├┤6 ├┤ qb_dealloc ├┤ qb_alloc ├┤6 ├» # └──────────┘└──────────┘└────────────┘└──────────┘└─────────────┘» # « # « a.0: ────────────── # « # « a.1: ────────────── # « ┌────────────┐ # « return.0: ┤ qb_dealloc ├ # « ├────────────┤ # « return.1: ┤ qb_dealloc ├ # « ├────────────┤ # « return.2: ┤ qb_dealloc ├ # « ├────────────┤ # « return.3: ┤ qb_dealloc ├ # « ├────────────┤ # «sbp_anc_0.0: ┤ qb_dealloc ├ # « └────────────┘ # Live QuantumVariables: # ---------------------- # QuantumFloat a # In both cases, sbp_anc is recomputed, but in the second case, the # reallocation allows the compiler to use the free qubit elsewhere # and afterwards pick a potentially different qubit for the # recomputation. permeability_dict[i] = False # for i in range(len(gwe.instruction.qubits)): # if permeability_dict[i] is None: # permeability_dict[i] = False if verify: from qrisp.uncomputation import is_permeable permeable_qubit_indices = [] for i in range(gwe.instruction.op.num_qubits): if permeability_dict[i]: permeable_qubit_indices.append(i) if not is_permeable(gwe.instruction.op, permeable_qubit_indices): raise Exception( f"Verification of permeability for function " f"{function.__name__} failed" ) gwe.instruction.op.permeability = permeability_dict return result return wrapped_function def find_qs(args): if hasattr(args, "qs"): return args.qs() from qrisp import QuantumVariable, QuantumArray, Qubit for arg in args: if isinstance(arg, (QuantumVariable, QuantumArray)): return arg.qs if isinstance(arg, Qubit): return arg.qs() else: for arg in args: if isinstance(arg, (list, tuple)): try: return find_qs(arg) except: pass if isinstance(arg, dict): try: return find_qs(arg.items()) except: pass raise Exception("Couldn't find QuantumSession") # Function to measure multiple quantum variables at once to assess their entanglement
[docs]def multi_measurement(qv_list, shots=10000, backend=None): """ This functions facilitates the measurement of multiple QuantumVariables at the same time. This can be used if the entanglement structure between several QuantumVariables is of interest. Parameters ---------- qv_list : list[QuantumVariable] A list of QuantumVariables. shots : int, optional The amount of shots to perform. The default is 10000. backend : BackendClient, optional The backend to evaluate the compiled QuantumCircuit on. By default, the backend from default_backend.py will be used. Raises ------ Exception Tried to perform measurement with open environments. Returns ------- counts_list : list A list of tuples. The first element of each tuple is a tuple again and contains the labels of the QuantumVariables. The second element is a float and indicates the probability of measurement. Examples -------- We entangle three QuantumFloats via addition and perform a multi-measurement: >>> from qrisp import QuantumFloat, h, multi_measurement >>> qf_0 = QuantumFloat(4) >>> qf_1 = QuantumFloat(4) >>> qf_0[:] = 3 >>> qf_1[:] = 2 >>> h(qf_1[0]) >>> qf_sum = qf_0 + qf_1 >>> multi_measurement([qf_0, qf_1, qf_sum]) {(3, 2, 5): 0.5, (3, 3, 6): 0.5} """ if backend is None: if qv_list[0].qs.backend is None: from qrisp.default_backend import def_backend backend = def_backend else: backend = qv_list[0].qs.backend if len(qv_list[0].qs.env_stack) != 0: raise Exception("Tried to perform measurement with open environments") from qrisp import merge merge(qv_list) # Copy circuit in order to prevent modification from qrisp import QuantumArray, QuantumVariable, recursive_qv_search from qrisp.core.compilation import qompiler temp = recursive_qv_search(qv_list) compiled_qc = qompiler( qv_list[0].qs, intended_measurements=sum([qv.reg for qv in temp], []) ) # compiled_qc = qv_list[0].qs.copy() # Add classical registers for the measurement results to be stored in cl_reg_list = [] for var in qv_list[::-1]: cl_reg = [] if isinstance(var, QuantumArray): qubits = sum([qv.reg for qv in var.flatten()[::-1]], []) elif isinstance(var, QuantumVariable): qubits = var.reg else: raise Exception(f"Found type {type(var)} in measurement list") for i in range(len(qubits)): cl_reg.append(compiled_qc.add_clbit()) cl_reg_list.append(cl_reg) # Add measurement instruction compiled_qc.measure(qubits, cl_reg) # counts = execute(qs_temp, backend, basis_gates = basis_gates, # noise_model = noise_model, shots = shots).result().get_counts() counts = backend.run(compiled_qc, shots) counts = {k: counts[k] for k in sorted(counts)} # Convert the labeling bistrings of counts into list of labels new_counts = {} for i in range(len(counts)): # Retrieve the separated strings of each measurement variable counts_strings = [] counts_bitstring = list(counts.keys())[i] bitstring_adress = 0 for j in range(len(cl_reg_list)): cl_reg = cl_reg_list[::-1][j] counts_strings.append( counts_bitstring[bitstring_adress : bitstring_adress + len(cl_reg)][ ::-1 ] ) bitstring_adress += len(cl_reg) # Convert to integers and insert outcome labels counts_values = [] for j in range(len(counts_strings)): outcome_int = int(counts_strings[j][::-1], 2) try: label = qv_list[j].decoder(outcome_int) if isinstance(label, np.ndarray): from qrisp import OutcomeArray label = OutcomeArray(label) counts_values.append(label) except AttributeError: counts_values.append(outcome_int) # Create array array_state = tuple(counts_values) try: new_counts[array_state] = counts[list(counts.keys())[i]] / shots except TypeError: raise Exception( "Tried to create measurement outcome dic for QuantumVariable " "with unhashable labels" ) # Append to the counts list # counts_list.append((array_state, counts[list(counts.keys())[i]]/shots)) # Sort counts_list such the most probable values come first new_counts = dict(sorted(new_counts.items(), key=lambda item: -item[1])) return new_counts
# Function to apply a phase function of signature phase_function(x,y,z..) -> float # which specifies the phase for each constellation of outcome labels of the quantum # variables in qv_list. def app_phase_function(qv_list, phase_function, t=1, **kwargs): # Prepare the list of index tuples # For this we first create a list of outcome indices of each qv first index_lists = [list(range(2**qv.size)) for qv in qv_list] # We now calculate the direct product in order to obtain every possible combination from itertools import product product_index_list = list(product(*index_lists)) # The next step is to iterate over every combination in order to determine the # phases. phases = [] for i in range(len(product_index_list)): # Calculate the outcome labels of the current constellation of indices labels = [ qv_list[j].decoder(product_index_list[i][j]) for j in range(len(qv_list)) ] # Calculate the phase phases.append(phase_function(*labels, **kwargs) * t) # Synthesize phase from qrisp.logic_synthesis import gray_phase_synth_qb_list gray_phase_synth_qb_list( qv_list[0].qs, sum([qv.reg[::-1] for qv in qv_list], []), phases )
[docs]def as_hamiltonian(hamiltonian): r""" Decorator that recieves a regular Python function (returning a float) and returns a function of QuantumVariables, applying phases based on the function's output. Parameters ---------- hamiltonian : function A function of arbitrary (non-quantum) variables returning a float. Returns ------- hamiltonian_application : function A function of QuantumVariables, which applies the phase dictated by the hamiltonian to the corresponding states. Examples -------- In this example we will demonstrate how a phase function with multiple arguments can be synthesized. For this we will create a phase function which encodes the fourier transform of different integers on the QuantumFloat x conditioned on the value of a QuantumChar ch. We will then apply the inverse Fourier transform to x and measure the results. :: import numpy as np from qrisp import QuantumChar, QuantumFloat, QFT, h #Create Variables x_size = 3 x = QuantumFloat(x_size, 0, signed = False) ch = QuantumChar() # Bring x into uniform superposition so the phase function application yields # a fourier transformed computation basis state h(x) #Bring ch into partial superposition (here |a> + |b> + |c> + |d>) h(ch[0]) h(ch[1]) from qrisp import multi_measurement, as_hamiltonian #In order to define the hamiltonian, we use regular Python syntax #The decorator "as_hamiltonian" turns it into a function #that takes Quantum Variables as arguments. The decorator will add the #keyword argument t to the function which mimics the t in exp(i*H*t) @as_hamiltonian def apply_multi_var_hamiltonian(ch, x): if ch == "a": k = 2 elif ch == "b": k = 7 elif ch == "c": k = 3 else: k = 4 #Return phase value #This is the phase distribution of the Fourier-transform #of the computational basis state |k> return k*x * 2*np.pi/2**x_size #Apply Hamiltonian apply_multi_var_hamiltonian(ch,x, t = 1) #Apply inverse Fourier transform QFT(x, inv = True) Acquire measurement results >>> multi_measurement([ch, x]) {('a', 2): 0.25, ('b', 7): 0.25, ('c', 3): 0.25, ('d', 4): 0.25} We see that the measurement results correspond to what we specified in the Hamiltonian. In Bra-Ket notation, before applying the Hamiltonian, we are in the state .. math:: \ket{\psi} = \frac{1}{\sqrt{4}}(\ket{a} + \ket{b} + \ket{c} + \ket{d}) \left( \frac{1}{\sqrt{8}} \sum_{x = 0}^8 \ket{x} \right) We then apply the Hamiltonian: .. math:: \text{exp}(i\text{H(ch, x)})\ket{\psi} = \frac{1}{\sqrt{32}} ( &\ket{a} \sum_{x = 0}^{2^3-1} \text{exp}(2x \frac{2 \pi i}{2^3}) \ket{x} \\ +&\ket{b} \sum_{x = 0}^{2^3-1} \text{exp}(7x \frac{2 \pi i}{2^3}) \ket{x} \\ +&\ket{c} \sum_{x = 0}^{2^3-1} \text{exp}(3x \frac{2 \pi i}{2^3}) \ket{x} \\ +&\ket{d} \sum_{x = 0}^{2^3-1} \text{exp}(4x \frac{2 \pi i}{2^3}) \ket{x}) For each branch, the QuantumFloat tensor-factor is in a Fourier-transformed computational basis state. Thus, if we apply the inverse QFT, we receive: .. math:: &\text{QFT}^{-1}\text{exp}(i\text{H(ch, x)})\ket{\psi} \\ = & \frac{1}{\sqrt{4}} (\ket{a} \ket{2} + \ket{b} \ket{7} +\ket{c} \ket{3} + \ket{d} \ket{4}) """ def hamiltonian_application(*args, t=1, **kwargs): gate_wrap(app_phase_function)(args, hamiltonian, t=t, **kwargs) # app_phase_function(args, hamiltonian, t = t, **kwargs) return hamiltonian_application
[docs]def perm_lock(qubits, message=""): """ Locks a list of qubits such that only permeable gates can be executed on these qubits. This means that an error will be raised if the user attempts to perform any operation involving these qubits if the operation does not commute with the Z-operator of this qubit. For more information, what a permeable gate is, check the :ref:`uncomputation documentation <uncomputation>`. This can be helpfull as it forbids all operations that change that computational basis state of this qubit but still allow controling on this qubit or applying phase gates. Using the keywoard message it is possible to extend the displayed error message. The effect of this function can be reversed using perm_unlock. Parameters ---------- qubits : list[Qubit] or QuantumVariable The qubits to phase-tolerantly lock. message : str, optional The message why these qubits are locked. Examples -------- We create a QuantumChar, perm-lock it's Qubits and attempt to initialize. >>> from qrisp import QuantumChar, perm_lock, cx, p >>> q_ch_0 = QuantumChar() >>> perm_lock(q_ch_0) >>> q_ch_0[:] = "g" Exception: Tried to perform non-permeable operations on perm_locked qubits We now create a second QuantumChar and perform a CNOT gate >>> q_ch_1 = QuantumChar() >>> cx(q_ch_0[3], q_ch_1[2]) Phase-gates are possible, too >>> p(0.1, q_ch_0) """ from qrisp.circuit.quantum_circuit import convert_to_qb_list for qb in convert_to_qb_list(qubits): if isinstance(qb, list): for item in qb: perm_lock(item) continue qb.perm_lock = True qb.perm_lock_message = message
[docs]def perm_unlock(qubits): """ Reverses the effect of "perm_lock". Parameters ---------- qubits : list[Qubit] or QuantumVariable The qubits to phase-tolerantly unlock. Examples -------- We create a QuantumChar, perm-lock it's Qubits and attempt to initialize. >>> from qrisp import QuantumChar, perm_lock, perm_unlock >>> q_ch = QuantumChar() >>> perm_lock(q_ch, message = "Qubits are perm-locked due to testing purposes") >>> q_ch[:] = "g" Exception: Qubits are perm-locked due to testing purposes >>> perm_unlock(q_ch) >>> q_ch[:] = "g" >>> print(q_ch) {'g': 1.0} """ from qrisp.circuit.quantum_circuit import convert_to_qb_list for qb in convert_to_qb_list(qubits): if isinstance(qb, list): for item in qb: perm_unlock(item) continue qb.perm_lock = False if hasattr(qb, "perm_lock_message"): del qb.perm_lock_message
[docs]def lock(qubits, message=""): """ Locks a list of qubits, implying an error will be raised if the user tries to perform any operation involving these qubits. Using the keywoard message it is possible to extend the displayed error message. This can be reversed by calling unlock. Parameters ---------- qubits : list[Qubit] or QuantumVariable The list of Qubits to lock. message : str, optional The message why these qubits are locked. Examples -------- We create a QuantumChar, lock it's Qubits and attempt to initialize. >>> from qrisp import QuantumChar, lock >>> q_ch = QuantumChar() >>> lock(q_ch, message = "Qubits are locked due to testing purposes") >>> q_ch[:] = "g" Exception: Qubits are locked due to testing purposes """ from qrisp.circuit.quantum_circuit import convert_to_qb_list for qb in convert_to_qb_list(qubits): if isinstance(qb, list): for item in qb: lock(item) continue qb.lock = True qb.lock_message = message
[docs]def unlock(qubits): """ Reverses the effect of "lock". Parameters ---------- qubits : list[Qubit] or QuantumVariable The list of Qubits to lock. Examples -------- We create a QuantumChar, lock it's Qubits and attempt to initialize. >>> from qrisp import QuantumChar, lock, unlock >>> q_ch = QuantumChar() >>> lock(q_ch) >>> q_ch[:] = "g" Exception: Tried to perform operations on locked qubits We now unlock and try again >>> unlock(q_ch) >>> q_ch[:] = "g" >>> print(q_ch) {'g': 1.0} """ from qrisp.circuit.quantum_circuit import convert_to_qb_list for qb in convert_to_qb_list(qubits): if isinstance(qb, list): for item in qb: unlock(item) continue qb.lock = False if hasattr(qb, "lock_message"): del qb.lock_message
def benchmark_function(function): def benchmarked_function(*args, sort_stats="tottime", stat_amount=20, **kwargs): def slow_function(): function(*args, **kwargs) import cProfile import pstats profile = cProfile.Profile() profile.runcall(slow_function) ps = pstats.Stats(profile) ps.strip_dirs() ps.sort_stats(sort_stats) ps.print_stats(stat_amount) return benchmarked_function def custom_qv(labels, decoder=None, qs=None, name=None): if not isinstance(labels, list): raise Exception( "Tried to create custom QuantumVariable without providing a list type" ) if len(labels) == 0: raise Exception( "Tried to create custom QuantumVariable without providing labels" ) elif len(labels) == 1: n = 1 else: n = int(np.ceil(np.log2(len(labels)))) from qrisp import QuantumVariable class CustomQuantumVariable(QuantumVariable): def __init__(self, qs=None, name=None): super().__init__(n, qs=qs, name=name) def decoder(self, x): if decoder is None: if x < len(labels): return labels[x] else: return "undefined_label_" + str(x) return decoder(x) return CustomQuantumVariable(qs=qs, name=name) def init_state(qv, target_array): from qiskit.circuit.library.data_preparation.state_preparation import ( StatePreparation, ) qiskit_qc = StatePreparation(target_array).definition from qrisp import QuantumCircuit init_qc = QuantumCircuit.from_qiskit(qiskit_qc) # Find global phase correction from qrisp.simulator import statevector_sim init_qc.qubits.reverse() sim_array = statevector_sim(init_qc) init_qc.qubits.reverse() arg_max = np.argmax(np.abs(sim_array)) gphase_dif = (np.angle(target_array[arg_max] / sim_array[arg_max])) % (2 * np.pi) init_qc.gphase(gphase_dif, 0) init_gate = init_qc.to_gate() init_gate.name = "state_init" qv.qs.append(init_gate, qv) def get_statevector_function(qs, decimals=None): if len(qs.qv_list) == 0: return lambda x: 0 else: from qrisp.simulator import statevector_sim compiled_qc = qs.compile() sv_array = statevector_sim(compiled_qc) if decimals is not None: sv_array = np.round(sv_array, decimals) def statevector(label_constellation, round=None): from qrisp import bin_rep qs = list(label_constellation.keys())[0].qs if len(label_constellation) != len(qs.qv_list): missing_variables = set([qv.name for qv in qs.qv_list]) - set( [qv.name for qv in label_constellation.keys()] ) raise Exception( "Tried to invoke statevector debugger without specifying an " "outcome label for each QuantumVariable registered in " "QuantumSession. Missing variables are: " + str(missing_variables) ) bitstring = len(compiled_qc.qubits) * ["0"] for qf in label_constellation.keys(): label_int = qf.encoder(label_constellation[qf]) bin_label_int = bin_rep(label_int, qf.size)[::-1] for i in range(qf.size): qubit_pos = compiled_qc.qubits.index(qf[i]) bitstring[qubit_pos] = bin_label_int[i] bitstring = "".join(bitstring) state_index = int(bitstring, base=2) if round is None: return sv_array[state_index] else: return np.around(sv_array[state_index], round) return statevector def check_if_fresh(qubits, qs, ignore_q_envs = True): from qrisp import QuantumEnvironment if not ignore_q_envs: temp_data = list(qs.data) qs.data = [] for i in range(len(temp_data)): if isinstance(temp_data[i], QuantumEnvironment): env = temp_data[i] env.compile() else: qs.append(temp_data[i]) for qb in qubits: reversed_data = qb.qs().data[::-1] for instr in reversed_data: if isinstance(instr, QuantumEnvironment) and ignore_q_envs: continue if qb in instr.qubits: if instr.op.name == "qb_alloc": break else: return False else: return False return True def get_measurement_from_qc(qc, qubits, backend, shots=10000): # Add classical registers for the measurement results to be stored in cl = [] for i in range(len(qubits)): cl.append(qc.add_clbit()) # Add measurement instruction for i in range(len(qubits)): qc.measure(qubits[i], cl[i]) # Execute circuit counts = backend.run(qc, shots) # Remove other measurements outcomes from counts dic new_counts_dic = {} for key in counts.keys(): # Remove possible whitespaces new_key = key.replace(" ", "") # Remove other measurements new_key = new_key[:len(cl)] new_key = int(new_key, base=2) try: new_counts_dic[new_key] += counts[key] except KeyError: new_counts_dic[new_key] = counts[key] counts = new_counts_dic # Plot result (if needed) # Normalize counts for key in counts.keys(): counts[key] = counts[key] / abs(shots) return counts def find_calling_line(level=0): stack = traceback.extract_stack(limit=level + 3) return str( traceback.format_list(stack)[1].split("\n")[1].strip() ) # prints "a = fct1()" def retarget_instructions(data, source_qubits, target_qubits): from qrisp import QuantumEnvironment, recursive_qs_search, multi_session_merge for i in range(len(data)): instr = data[i] if isinstance(instr, QuantumEnvironment): retarget_instructions(instr.original_data, source_qubits, target_qubits) retarget_instructions(instr.env_data, source_qubits, target_qubits) continue for j in range(len(instr.qubits)): if instr.qubits[j] in source_qubits: instr.qubits[j] = target_qubits[source_qubits.index(instr.qubits[j])]
[docs]def redirect_qfunction(function_to_redirect): """ Decorator to turn a function returning a QuantumVariable into an in-place function. This can be helpful for manual uncomputation if we have a function returning some QuantumVariable, but we want the result to operate on some other variable, which is supposed to be uncomputed. Parameters ---------- function_to_redirect : function A function returning a QuantumVariable. Raises ------ Exception Given function did not return a QuantumVariable Exception Tried to redirect quantum function into QuantumVariable of differing size Returns ------- redirected_function : function A function which performs the same operation as the input but now has the keyword argument target. Every instruction that would have been executed on the input functions result is executed on the QuantumVariable specified by target instead. Examples -------- We create a function that determins the AND value of its inputs and redirect it onto another QuantumBool. :: from qrisp import QuantumBool, mcx, redirect_qfunction #This function has only two arguments and returns its result def AND(a, b): res = QuantumBool() mcx([a,b], res) return res a = QuantumBool(name = "a") b = QuantumBool(name = "b") c = QuantumBool(name = "c") #This function has two arguments and the keyword argument target redirected_AND = redirect_qfunction(AND) redirected_AND(a, b, target = c) >>> print(a.qs) :: QuantumCircuit: -------------- b.0: ──■── a.0: ──■── ┌─┴─┐ c.0: ┤ X ├ └───┘ Live QuantumVariables: --------------------- QuantumBool b QuantumBool a QuantumBool c """ from qrisp import QuantumEnvironment, QuantumVariable import weakref def redirected_qfunction(*args, target=None, **kwargs): env = QuantumEnvironment() env.manual_allocation_management = True qs = target.qs with env: res = function_to_redirect(*args, **kwargs) if not isinstance(res, QuantumVariable): raise Exception("Given function did not return a QuantumVariable") target = list(target) if len(res) != len(target): raise Exception( "Tried to redirect quantum function into QuantumVariable of " "differing size" ) i = 0 res_is_new = False while i < len(env.env_qs.data): instr = env.env_qs.data[i] if isinstance(instr, QuantumEnvironment): pass elif instr.op.name == "qb_alloc" and instr.qubits[0] in list(res): env.env_qs.data.pop(i) res_is_new = True continue else: for qb in instr.qubits: qb.qs = weakref.ref(qs) i += 1 retarget_instructions(env.env_qs.data, list(res), target) if res_is_new: # Remove all traces of res res.delete() for i in range(res.size): res.qs.qubits.remove(res[i]) res.qs.data.pop(-1) for i in range(len(res.qs.deleted_qv_list)): qv = res.qs.deleted_qv_list[i] if qv.name == res.name: res.qs.deleted_qv_list.pop(i) break return target redirected_qfunction.__name__ = function_to_redirect.__name__ return redirected_qfunction
def get_sympy_state(qs, decimals): from sympy import ( I, Rational, Symbol, cancel, cos, count_ops, exp, factor, nsimplify, pi, simplify, sin, ) from sympy.physics.quantum import Ket, OrthogonalKet from qrisp.simulator import statevector_sim qv_list = list(qs.qv_list) labels = [] for qv in qv_list: labels.append([qv.decoder(i) for i in range(2**qv.size)]) compiled_qc = qs.compile() sv_array = statevector_sim(compiled_qc) if not sv_array.dtype == np.dtype("O"): angles = np.angle(sv_array) % (2 * np.pi) / (np.pi) if decimals is not None: sv_array = np.round(sv_array, decimals) angles = np.round(angles, decimals) else: sv_array = np.round(sv_array, 5) angles = np.round(angles, 5) nz_indices = np.nonzero(sv_array)[0] nnz = len(nz_indices) else: import sympy as sp nz_indices = [] for i in range(len(sv_array)): entry = simplify(sv_array[i]) for a in sp.preorder_traversal(entry): if isinstance(a, sp.Float): entry = entry.subs(a, round(a, 5)) sv_array[i] = entry if not sv_array[i] == 0: nz_indices.append(i) nnz = len(nz_indices) res = 0 for ind in list(nz_indices): amplitude = sv_array[ind] if not sv_array.dtype == np.dtype("O"): if decimals is None: try: abs_amp = trigify_amp(amplitude, nnz) except TypeError: abs_amp = amplitude # For some reason there is a sympy error, when the angle is equal to 1 if angles[ind] == 1: phase = 1 else: phase = nsimplify(angles[ind], tolerance=10 ** -5) if count_ops(phase) > 5: phase = angles[ind] ket_expr = exp(I * phase * pi) * abs_amp * nnz**0.5 else: ket_expr = sympy.N(amplitude, decimals) else: process_stack = [amplitude] while process_stack: a = process_stack.pop(0) if ( isinstance(a, (sympy.core.add.Add, sympy.core.mul.Mul)) and len(a.free_symbols) != 0 ): process_stack.extend(a.args) elif len(a.free_symbols) == 0: sub_float = np.round(complex(a.evalf()), 5) if np.abs(sub_float - 1) < 10**-5: abs_amp = 1 continue elif np.abs(sub_float) < 10**-5: entry = entry.subs(a, 0) continue elif np.abs(sub_float) > 1: continue else: abs_amp = trigify_amp(sub_float, nnz) if np.angle(complex(a.evalf())) / np.pi == 1: phase = -1 else: phase = sp.exp( sp.I * nsimplify( np.angle(complex(a.evalf())) / np.pi, tolerance=10 ** -5, ) * Symbol("pi") ) expr = abs_amp * phase amplitude = amplitude.subs(a, expr) amplitude = amplitude.subs(1j, sp.I) ket_expr = sp.trigsimp(amplitude) * nnz**0.5 int_string = bin_rep(ind, len(compiled_qc.qubits)) labels = [] for qv in qv_list: bit_string = "" for qb in qv.reg: bit_string += int_string[compiled_qc.qubits.index(qb)] label = qv.decoder(int(bit_string[::-1], 2)) ket_expr *= OrthogonalKet((label)) res += ket_expr if decimals is None or sv_array.dtype == np.dtype("O"): res = cancel(nsimplify(1 / nnz**0.5) * res) if isinstance(res, sympy.core.mul.Mul): temp = 1 for arg in res.args[:-1]: temp *= nsimplify(arg.subs({Symbol("pi"): pi})) res = temp * res.args[-1] res = res.subs({Symbol("pi"): pi}) return res def trigify_amp(amplitude, nnz): from sympy import ( I, Rational, Symbol, cancel, cos, count_ops, exp, factor, latex, nsimplify, pi, simplify, sin, ) cos_expr = nsimplify( np.arccos(np.abs(amplitude)) / np.pi, tolerance=10 ** -5 ) sin_expr = nsimplify( np.arcsin(np.abs(amplitude)) / np.pi, tolerance=10 ** -5 ) # if count_ops(sin_expr) > count_ops(cos_expr): if len(latex(sin_expr)) > len(latex(cos_expr)): expr = "cos" temp = cos_expr # elif count_ops(sin_expr) < count_ops(cos_expr): elif len(latex(sin_expr)) < len(latex(cos_expr)): expr = "sin" temp = sin_expr elif len(sin_expr.free_symbols) == 0: if sin_expr.evalf() > cos_expr.evalf(): expr = "cos" temp = cos_expr else: expr = "sin" temp = sin_expr else: temp = ( nsimplify(np.abs(amplitude) * nnz**0.5, tolerance=10 ** -5) / nnz**0.5 ) # if count_ops(temp) > 4: if len(latex(temp)) > 20: temp = ( nsimplify(np.abs(amplitude) * nnz**0.5, tolerance=10 ** -5) / nnz**0.5 ) if len(latex(temp)) > 20: abs = np.abs(amplitude) else: abs = temp else: if expr == "cos": abs = cos(cos_expr * Symbol("pi")) else: abs = sin(sin_expr * Symbol("pi")) return abs def render_qc(qc): latex_str = qc.to_latex() import os.path import subprocess import tempfile from IPython.display import Image, display with tempfile.TemporaryDirectory(prefix="texinpy_") as tmpdir: path = os.path.join(tmpdir, "document.tex") with open(path, "w") as fp: fp.write(latex_str) subprocess.run(["lualatex", path], cwd=tmpdir) subprocess.run( [ "pdftocairo", "-singlefile", "-transp", "-r", "100", "-png", "document.pdf", "document", ], cwd=tmpdir, ) im = Image(filename=os.path.join(tmpdir, "document.png")) display(im)
[docs]def lifted(*args, verify=False): """ Shorthand for ``gate_wrap(permability = "args", is_qfree = True)``. A lifted function is ``qfree`` and permeable on its inputs. The results of lifted functions can be automatically uncomputed even if they contain functions that could not be uncomputed on their own. You can find more information about these concepts :ref:`here <Uncomputation>` or `here <https://silq.ethz.ch/overview#/overview/3_uncomputation>`_. Note that the concept of permeability in Qrisp is a more general version of Silq's ``const``. .. warning:: Incorrect information about permeability and ``qfree``-ness can yield incorrect compilation results. If you are unsure, use the ``verify`` keyword on a small scale first. Parameters ---------- verify : bool, optional If set to ``True``, the specified information about permeability and ``qfree``-ness will be checked numerically. The default is ``False``. Examples -------- We create a function performing the `Margolus gate <https://arxiv.org/abs/quant-ph/0312225>`_. As it contains ``ry`` rotations, there are non-``qfree`` steps involved. Putting on the ``lifted`` decorator however marks the function as ``qfree`` as a whole. :: from qrisp import QuantumVariable, cx, ry, lifted from numpy import pi @lifted(verify = True) def margolus(control): res = QuantumVariable(1) ry(pi/4, res) cx(control[1], res) ry(-pi/4, res) cx(control[0], res) ry(pi/4, res) cx(control[1], res) ry(-pi/4, res) return res control = QuantumVariable(2) res = margolus(control) >>> print(res.qs) :: QuantumCircuit: -------------- ┌───────────┐ control.0: ┤0 ├ │ │ control.1: ┤1 margolus ├ │ │ res.0: ┤2 ├ └───────────┘ Live QuantumVariables: --------------------- QuantumVariable control QuantumVariable res >>> res.uncompute() >>> print(res.qs) :: QuantumCircuit: -------------- ┌───────────┐┌──────────────┐ control.0: ┤0 ├┤0 ├ │ ││ │ control.1: ┤1 margolus ├┤1 margolus_dg ├ │ ││ │ res.0: ┤2 ├┤2 ├ └───────────┘└──────────────┘ Live QuantumVariables: --------------------- QuantumVariable control Note that we set the ``verify`` keyword to ``True`` in this example. In more complex functions, involving many qubits this feature should only be used for bug-fixing on a small scale, since the verification can be time-consuming. """ if len(args) == 0: def lifted_helper(function): return gate_wrap(permeability="args", is_qfree=True, verify=verify)( function ) return lifted_helper else: return gate_wrap(permeability="args", is_qfree=True)(args[0])
[docs]def t_depth_indicator(op, epsilon): r""" This function returns the T-depth of an :ref:`Operation` object. According to `this paper <https://arxiv.org/abs/1403.2975>`_, the synthesis of an $RZ(\phi)$ up to precision $\epsilon$ requires $3\text{log}_2(\frac{1}{\epsilon})$ T-gates. Parameters ---------- op : :ref:`Operation` The operation, whose T-depth should be estimated. epsilon : float The precision of the RZ gate simulation. Returns ------- float The estimated T-depth of the Operation. """ from qrisp import ClControlledOperation if isinstance(op, ClControlledOperation): return t_depth_indicator(op.base_op, epsilon) elif op.definition is not None: return op.definition.t_depth(epsilon) elif op.name in ["cx", "cx", "cz", "x", "y", "z", "s", "h", "s_dg", "measure", "reset", "qb_alloc", "qb_dealloc", "barrier", "gphase"]: return 0 elif op.name in ["rx", "ry", "rz", "p", "u1"]: par = op.params[0]/(np.pi)%1 if par in [0, 1/2]: return 0 elif par in [1/4, 3/4]: return 1 else: return 3*np.log2(1/epsilon) elif op.name in ["t", "t_dg"]: return 1 elif op.name == "u3": res = 0 for i in range(3): par = op.params[0]/(np.pi)%1 if par in [0, 1/2]: pass elif par in [1/4, 3/4]: res += 1 else: res += 3*np.log2(1/epsilon) return res else: raise Exception(f"Gate {op.name} not implemented")
[docs]def cnot_depth_indicator(op): r""" This function returns the CNOT-depth of an :ref:`Operation` object. In NISQ-era devices, CNOT gates are the restricting bottleneck for quantum circuit execution. This function can be used as a gate-speed specifier for the :meth:`compile <qrisp.QuantumSession.compile>`_ method. Parameters ---------- op : :ref:`Operation` The operation, whose CNOT-depth should be computed. Returns ------- float The CNOT-depth of the Operation. """ from qrisp import ClControlledOperation if isinstance(op, ClControlledOperation): return cnot_depth_indicator(op.base_op) elif op.definition is not None: return op.definition.cnot_depth() if op.num_qubits == 1: return 0 elif op.name in ["cx", "cx", "cz"]: return 1 else: raise Exception(f"Gate {op.name} not implemented")
[docs]def inpl_adder_test(inpl_adder): """ This function runs tests on a desired inplace addition function. An inplace addition function is a function mapping (a, b) to (a, a+b), where a is a :ref:`QuantumVariable`, list[:ref:`Qubit`] or an integer and b is either a :ref:`QuantumVariable` or a list[:ref:`Qubit`]. Parameters ---------- inpl_adder : callable A quantum inplace addition function that can either act on single QuantumVariables or on lists of Qubits by adding the first one to the second. Returns ------- Bool: True if all tests are passed, else False/ Exceptions. Examples -------- We test the built-in Cuccaro adder: :: from qrisp import cuccaro_adder, inpl_adder_test inpl_adder_test(cuccaro_adder) print("The cuccaro adder passed the tests without errors.") And now a new user-defined qcla adder: :: from qrisp import inpl_adder_test, qcla qcla_2_0 = lambda x, y : qcla(x, y, radix_base = 2, radix_exponent = 0) inpl_adder_test(qcla_2_0) print("The qcla_2_0 adder passed the tests without errors.") """ from qrisp import QuantumFloat, multi_measurement, h, control, QuantumBool for i in range(1, 7): for j in range(1, i+1): a = QuantumFloat(j) b = QuantumFloat(i) c = QuantumFloat(i) h(a) h(b) c[:] = b inpl_adder(a, c) statevector_arr = a.qs.compile().statevector_array() angles = np.angle( statevector_arr[ np.abs(statevector_arr) > 1 / 2 ** ((a.size + b.size) / 2 + 1) ] ) # Test correct phase behavior assert np.sum(np.abs(angles)) < 0.1, f"Quantum-quantum adder produced a faulty phase shift on input sizes, {i},{j}." mes_res = multi_measurement([a,b,c]) for a, b, c in mes_res.keys(): assert (a + b)%(2**i) == c, f"Quantum-quantum addition result was incorrect for input values {a} += {c} on input sizes, {i},{j}." if i < 6: for j in range(1, 2**i): a = QuantumFloat(i) b = QuantumFloat(i) h(a) b[:] = a inpl_adder(j, a) statevector_arr = a.qs.compile().statevector_array() angles = np.angle( statevector_arr[ np.abs(statevector_arr) > 1 / 2 ** ((a.size) / 2 + 1) ] ) assert np.sum(np.abs(angles)) < 0.1, f"Classical-quantum adder produced a faulty phase shift on input size {i}." mes_res = multi_measurement([a,b]) for a, b in mes_res.keys(): assert (b + j)%(2**i) == a, f"Classical-quantum addition result was incorrect for input values {a} += {c} on input size {i}." for i in range(1, 7): for j in range(1, i+1): a = QuantumFloat(j) b = QuantumFloat(i) c = QuantumFloat(i) qbl = QuantumBool() h(qbl) h(a) h(b) c[:] = b with control(qbl): inpl_adder(a, c) statevector_arr = a.qs.compile().statevector_array() angles = np.angle( statevector_arr[ np.abs(statevector_arr) > 1 / 2 ** ((a.size+ b.size) / 2 + 1) ] ) assert np.sum(np.abs(angles)) < 0.1, f"Controlled quantum-quantum adder produced a faulty phase shift on input sizes, {i},{j}." mes_res = multi_measurement([a,b,c, qbl]) for a, b, c, qbl in mes_res.keys(): if qbl: assert (a + b)%(2**i) == c, f"Controlled quantum-quantum addition result was incorrect for input values {a} += {c} on input sizes, {i},{j}." else: assert c == b, f"Controlled quantum-quantum addition behaviour was incorrect; an operation was performed without the control qubit in |1> state.Faulty input sizes: {i},{j}" if i < 6: for j in range(1, 2**i): a = QuantumFloat(i) b = QuantumFloat(i) qbl = QuantumBool() h(qbl) h(a) b[:] = a with control(qbl): inpl_adder(j, a) statevector_arr = a.qs.compile().statevector_array() angles = np.angle( statevector_arr[ np.abs(statevector_arr) > 1 / 2 ** ((a.size) / 2 + 1) ] ) assert np.sum(np.abs(angles)) < 0.1, f"Controlled classical-quantum adder produced a faulty phase shift on input size {i}." mes_res = multi_measurement([a,b, qbl]) for a, b, qbl in mes_res.keys(): if qbl: assert (b + j)%(2**i) == a, f"Controlled classical-quantum addition result was incorrect for input values {b} += {j} on input size, {i}." else: assert b == a, f"Controlled classical-quantum addition behaviour was incorrect; an operation was performed without the control qubit in |1> state. Faulty input sizes: {i}"