"""
\********************************************************************************
* 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
********************************************************************************/
"""
from qrisp.core.gate_application_functions import x
from qrisp import gate_wrap
@gate_wrap(is_qfree=True, permeability="args")
def q_int_div(numerator, divisor, adder="cuccaro", n=None, log_output=True):
# This function performs integer division based on the following algorithm
# Which is a reformulation of the division algorithm presented in
# https://en.wikipedia.org/wiki/Division_algorithm#Non-restoring_division
# Q = 2**(n)-1
# D *= 2**(n-1)
# R = int(N)
# import numpy as np
# from qrisp.misc import int_as_array
# for i in range(n-1, -1, -1):
# D = D/2
# if not np.sign(D)*np.sign(R) != -1:
# Q -= 2**(i)
# if int_as_array(Q, n)[::-1][i]:
# R = R-D
# else:
# R = R+D
# Q -= 2**(n-1)
# Q += 0.5
# if D < 0:
# Q += -0.5
# R = R - D
# else:
# Q += -0.5
# R = R + D
# D *= 2
# The D multiplications/divisions are realized by bitshifts
# The conditioned Q -= 2**i operation is performed by NOTing the appropriate
# qubit controlled on the condition
# The R = R+-D operation is realized by wrapping applying NOT gates on the R
# qubits before and after the addition controlled on the corresponding Q-qubit
# This yields the desired behavior because (x' + y)' = x - y
# where the prime denotes negation
# The Q -= 2**(n-1) is simply performed by flipping the significance n-1 bit
# The Q = Q + 0.5 is performed by adding an aditional qubit at the -1 significance
# of Q and turning it on
# The final conditional is performed similar to the previous condition
# Where the variables which are being operated on are wrapped in CNOTs
qs = numerator.qs
if numerator.exponent < 0 or divisor.exponent < 0:
raise Exception(
"Tried to call integer division with non integer quantum floats"
)
# n is the bitsize of the quotient
# The largest possible number the quotient can take is
# 2**(numerator_max_sig - divisor_min_sig)
# We need one additional bit (why?)
if n is None:
n = int(numerator.mshape[1] - divisor.exponent) + 1
# The quotient bits will successively be calculated/
# added starting at the most significant bit.
# As the algorithm produces empty bits in the remainder at the same rate as
# quotient bits are required, we initialize the quotient as a 1 bit variable
# Since the highest significance ist calculated fist, this bit needs significance n.
# We initialize this float with a single Qubit which will be freed up
# once the algorithm produced the first bit
# We can't initialize variables with 0 qubits
from qrisp.qtypes.quantum_float import QuantumFloat
quotient = QuantumFloat(1, n, signed=True)
# In order to determine the datashape of the remainder, we note that
# within the algorithm, there are steps where the remainder variable needs to hold
# larger+finer numbers than the final result for the remainder
# The crucial step is the addition
# remainder += divisor
# Regarding the exponent of the remainder, we know that it has to be
# less than the exponent of the numerator (so we can properly initialize)
# But also less than the divisor exponent - 1 in order to properly execute
# the last iteration which is a D/2 addition
remainder_exponent = min(numerator.exponent, divisor.exponent - 1)
# For the maximum significance,
# we note that the divisor is initially bit shifted by n
divisor.exp_shift(n - 1)
# Therefore, the required maximum significance is acquired
# with the formula for addition max_sig_add = max(max_sig_a, max_sig_b) + 1
remainder_max_sig = max(numerator.mshape[1], divisor.mshape[1]) + 1
# Create remainder float
remainder_size = remainder_max_sig - remainder_exponent
remainder = QuantumFloat(remainder_size, remainder_exponent, signed=True)
# We now can initialize the remainder as the value of the numerator
remainder.init_from(numerator)
# If the divisor is signed we need to flip the quotient sign bit (why?)
if divisor.signed:
qs.cx(divisor[-1], quotient[-1])
from qrisp.alg_primitives.arithmetic import inpl_add
# Perform iterations
for i in range(n - 1, -1, -1):
if log_output:
R = list(remainder.get_measurement().keys())[0]
print("R: ", R)
D = list(divisor.get_measurement().keys())[0]
print("D: ", D)
Q = list(quotient.get_measurement().keys())[0]
print("Q: ", Q)
# print("Q: " + bin_rep(int(Q/2.**quotient.exponent),
# quotient.size-1) + "(" + str(Q) + ")")
print("---")
# The sign bit of the remainder is the newly calculated bit
# of the quotient of this iteration
# Therefore we need to move this bit from the remainder to the quotient
# We do this in a very "hacky" fashion
# Remove from remainder
remainder_sign_bit = remainder.reg.pop(-1)
remainder.size -= 1
remainder.mshape[1] -= 1
# Add to quotient at position 0
quotient.reg.insert(0, remainder_sign_bit)
quotient.size += 1
quotient.mshape[1] += 1
# This next instruction is a bit involved to understand
# If we didn't use the technique of transfering the bits from the remainder
# to the quotient, we would have to call quotient.x() in order to realize
# the initial Q = 2**n - 1 statement. In this case we would then afterwards
# CNOT from remainder sign bit it the quotient sign bit from this iteration
# Since x gate on the quotient qubit and the CNOT commute,
# we can also perform the x gate after the CNOT
# Translating this to our case we replaced the CNOT with the bit transfer,
# we arive at the neccessity of an x at this point
x(quotient[0])
# Since the new bit was added at the least significant end,
# we need to lower the exponent
quotient.exp_shift(-1)
# This is to handle the D part of the statement "np.sign(D)*np.sign(R) != 1"
if divisor.signed:
qs.cx(divisor[-1], quotient[0])
# We constructed Q with one initial qubit, which will be freed up after
# the first iteration. This does not produce a qubit overhead,
# because the upcomming add function needs more than one ancilla anyway
if i == n - 1:
quotient.reduce(quotient[1])
# Perform the D = D/2 instruction
divisor.exp_shift(-1)
# We wrap the remainder in CNOT gates to make use of
# (R' + D)' = (R - D)
# where the prime denotes the negation
for j in range(remainder.size):
qs.cx(quotient[0], remainder[j])
# This perform the addition R += D or R -= D
inpl_add(
remainder,
divisor,
ignore_rounding_error=True,
ignore_overflow_error=True,
adder=adder,
)
# Instead of executing this layer of CNOT gates,
# we use the layer from the next iteration and only
# CNOT the control qubit from this layer
# for j in range(remainder.size):
# qs.cx(quotient[0], remainder[j])
# We still need to make up for the fact that the next layer of CNOT
# gates doesnt include remainder[-1]
# qs.cx(quotient[0], remainder[-1])
# However remainder[-1] is also the control qubit of the next iteration
# implying our plan of CNOTting the next control qubit cancels the
# previous line
# qs.cx(quotient[0], remainder[-1])
# In any iteration apart from the first, quotient[0] does not contain
# the desired value because of the sheenanigans of the previous lines.
# In detail: It contains the desired value (+) the quotient[0] qubit
# of the previous iteration. We cancel with this command
if i != n - 1:
qs.cx(quotient[1], quotient[0])
# Since there is no more upcoming iteration, we simply perform the CNOT layer
# without any further tricks.
for j in range(remainder.size):
qs.cx(quotient[0], remainder[j])
# This performs the Q -= 2**(n-1) instruction (the -1 qubit is the sign bit =>
# the -2 qubit is the significance n-1 bit)
x(quotient[-2])
# Handle the case that the divisor is signed
if divisor.signed:
# Create Qubit to account for the fact that we will add 0.5 to the quotient,
# even though it only supports integers
quotient.extend(1, position=0)
quotient.exp_shift(-1)
x(quotient[0])
# Add/Subtract 0.5 executed via (Q' + 0.5)' = Q - 0.5
qs.cx(divisor[-1], quotient.reg)
quotient.incr(-(2**quotient.exponent))
qs.cx(divisor[-1], quotient.reg)
# Remove 0.5 significance qubit (contains 0 now)
quotient.reduce(quotient[0])
quotient.exp_shift(1)
# Incase the divisor is not only signed but actually negative, we CNOT the
# remainder such that the upcoming addition circuit results in a subtraction
qs.cx(divisor[-1], remainder.reg)
# Perform R = R +- D
inpl_add(
remainder,
divisor,
ignore_rounding_error=True,
ignore_overflow_error=True,
adder=adder,
)
if divisor.signed:
# Undo the CNOT
pass
qs.cx(divisor[-1], remainder.reg)
# Perform D = 2*D
divisor.exp_shift(1)
# Finally, if the numerator was signed, we flip the quotient sign bit
if numerator.signed:
qs.cx(numerator[-1], quotient[-1])
return quotient, remainder
# Wrapper for the integer division
# Supports division for quantum float of arbitrary exponent.
# And allows to give a precision threshold (prec)
# The resulting quotient variable Q_res has the property |Q_res - Q_real| < 2**prec
[docs]
def q_divmod(numerator, divisor, adder="thapliyal", prec=0):
"""
Performs division up to arbitrary precision. Returns the quotient and the remainder.
Parameters
----------
numerator : QuantumFloat
The QuantumFloat to divide.
divisor : QuantumFloat
The QuantumFloat to divide by.
adder : str, optional
The type of adder to use. Available are "thapliyal" and "cuccarro".
The default is "thapliyal".
prec : int, optional
The precision of the division. If the precision is set to $k$,
the approximated quotient $q_{apr}$ and the true quotient $q_{true}$
satisfy $|q_{apr} - q_{true}|<2^{-k}$. The default is 0.
Returns
-------
quotient : QuantumFloat
The approximated quotient.
remainder : QuantumFloat
The remainder which satisfying $q*d + r = n$.
Examples
--------
We calculate 10/8 with varying precision:
>>> from qrisp import QuantumFloat, q_divmod, multi_measurement
>>> num = QuantumFloat(4)
>>> div = QuantumFloat(4)
>>> num[:] = 10
>>> div[:] = 8
>>> quotient, remainder = q_divmod(num, div, prec = 1)
>>> multi_measurement([quotient, remainder])
{(1.0, 2.0): 1.0}
Now with higher precision
>>> num = QuantumFloat(4)
>>> div = QuantumFloat(4)
>>> num[:] = 10
>>> div[:] = 8
>>> quotient, remainder = q_divmod(num, div, prec = 3)
>>> multi_measurement([quotient, remainder])
{(1.25, 0.0): 1.0}
"""
# The idea is to bit shift numerator and divisor by s such that,
# they are both integers. To increase the precision we shift N by -prec
# (we'll see shortly why this increases the precision).
# We write
# N_tilde = 2**(s-prec) * N
# D_tilde = 2**s * D
# We then perform integer division giving Q_tilde, R_tilde such that
# N_tilde/D_tilde = Q_tilde + R_tilde/D_tilde
# where |R_tilde| < |D_tilde|
# We now set
# Q = Q_tilde 2**(prec)
# and insert, which yields
# N_tilde/D_tilde = 2**(-prec) * N/D = 2**(-prec) * Q + R_tilde/D_tilde
# => N/D = Q + 2**prec*R_tilde/D_tilde
# So the error is
# |2**prec*R_tilde/D_tilde| < 2**prec
# Because R_tilde < D_tilde
prec = -prec
s = -min(numerator.exponent, divisor.exponent)
num_exp_shift = s - prec
div_exp_shift = s
numerator.exp_shift(num_exp_shift)
divisor.exp_shift(div_exp_shift)
# print("N_tilde: ", numerator.get_measurement())
# print("D_tilde: ", divisor.get_measurement())
quotient, remainder = q_int_div(numerator, divisor, adder=adder, log_output=False)
# print("Q_tilde: ", quotient.get_measurement())
# print("R_tilde: ", remainder.get_measurement())
quotient.exp_shift(prec)
remainder.exp_shift(-num_exp_shift)
divisor.exp_shift(-div_exp_shift)
numerator.exp_shift(-num_exp_shift)
return quotient, remainder
[docs]
def q_div(numerator, divisor, prec=None):
"""
Performs division up to arbitrary precision and uncomputes the remainder.
Parameters
----------
numerator : QuantumFloat
The QuantumFloat to divide.
divisor : QuantumFloat
The QuantumFloat to divide by.
prec : int, optional
The precision of the division. If the precision is set to $k$,
the approximated quotient $q_{apr}$ and the true quotient $q_{true}$
satisfy $|q_{apr} - q_{true}|<2^{-k}$.
By default, a suited precision will be determined from the other inputs.
Returns
-------
quotient : QuantumFloat
The result of the division.
Examples
--------
We calculate 10/8:
>>> from qrisp import QuantumFloat, q_div
>>> num = QuantumFloat(4)
>>> div = QuantumFloat(4)
>>> num[:] = 10
>>> div[:] = 8
>>> quotient = q_div(num, div, prec = 2)
>>> print(quotient)
{1.25: 1.0}
"""
from qrisp import U_g_inpl_adder, h, hybrid_mult
if prec is None:
prec = divisor.size - numerator.exponent
quotient, remainder = q_divmod(numerator, divisor, prec=prec)
hybrid_mult(quotient, divisor, remainder, init_op="qft", terminal_op=None)
U_g_inpl_adder(remainder, numerator, mult_factor=-1)
h(remainder)
# QFT(remainder, inv = True, exec_swap = False)
# remainder += -1
remainder.delete()
return quotient
[docs]
def qf_inversion(qf, prec=None):
"""
Calculates the multiplicative inverse of a QuantumFloat.
Parameters
----------
qf : QuantumFloat
The QuantumFloat to invert.
prec : int, optional
The precision of the inversion. If the precision is set to $k$,
the approximated inverse $q_{apr}$ and the true inverse $q_{true}$ satisfy
$|q_{res} - q_{true}|<2^{-k}$.
By default, a suited precision will be determined from the other input.
Returns
-------
result : QuantumFloat
A QuantumFloat containing the inverse.
Examples
--------
We calculate the inverse of 0.75
>>> from qrisp import QuantumFloat, qf_inversion
>>> qf = QuantumFloat(2, -2)
>>> qf[:] = 0.75
>>> qf_inv = qf_inversion(qf, prec = 4)
>>> print(qf_inv)
{1.3125: 1.0}
>>> 0.75**-1
1.3333333333333333
"""
if prec is None:
prec = qf.size
from qrisp import QuantumFloat, x
numerator = QuantumFloat(1, signed=False)
numerator.encode(1)
result = q_div(numerator, qf, prec)
x(numerator)
numerator.delete()
return result
