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Table of Contents



Release Notes

0.5.0

Prelude

Qiskit Optimization 0.5 supports the new algorithms introduced in Qiskit Terra 0.22 which in turn rely on the Qiskit Primitives. Qiskit Optimization 0.5 still supports the former algorithms based on qiskit.utils.QuantumInstance, but they will be deprecated and then removed, along with the support here, in future releases.

New Features

  • The GroverOptimizer class has a new keyword argument, sampler which is used to run the algorithm using an instance of the qiskit.primitives.BaseSampler interface to calculate the results. This new argument supersedes the the quantum_instance argument and accordingly, quantum_instance is pending deprecation and will be deprecated and subsequently removed in future releases.

Upgrade Notes

  • The previously deprecated VQEProgram and QAOAProgram classes have been removed. They were originally deprecated in the Qiskit Optimization 0.3.0 release.

Bug Fixes

  • Fixed an issue that parse_tsplib_format() did not parse TSPLIB files correctly in all cases; in particular if extra whitespace existed around keywords or if an EOF keyword was present.

0.4.0

New Features

  • Adds a method prettyprint() to QuadraticProgram to generate a pretty-printed string of the object.

    Here is an example of pretty printing.

    from qiskit_optimization import QuadraticProgram

qp = QuadraticProgram('problem 1')
qp.integer_var(-1, 2, 'x')
qp.integer_var(-1, 2, 'y')
qp.continuous_var(-1, name='z')
qp.binary_var('u')
qp.binary_var('v')
qp.binary_var_list(10)
qp.integer_var_list(3)
qp.continuous_var_list(3)
qp.minimize(constant=3, linear={'x': 2, 'y': 3}, quadratic={('u', 'x'): -1})
qp.linear_constraint({'x': 1, 'y': -2}, '>=', 2, name='lin_GE')
qp.linear_constraint({'x': 2, 'y': -1}, '==', 1, name='lin_EQ')
qp.quadratic_constraint({'x': 1, 'u': 1}, {(3, 4): 1, (5, 6): -1}, '<=', 1, name='quad_LE')
qp.quadratic_constraint({'x': 2, 'y': -1}, {('z', 'z'): -1}, '<=', 1)

print(qp.prettyprint())

    The output is as follows.

    Problem name: problem 1

Minimize
  -x*u + 2*x + 3*y + 3

Subject to
  Linear constraints (2)
    x - 2*y >= 2  'lin_GE'
    2*x - y == 1  'lin_EQ'

  Quadratic constraints (2)
    u*v - x5*x6 + u + x <= 1  'quad_LE'
    -z^2 + 2*x - y <= 1  'q1'

  Integer variables (5)
    -1 <= x <= 2
    -1 <= y <= 2
    0 <= x15
    0 <= x16
    0 <= x17

  Continuous variables (4)
    -1 <= z
    0 <= x18
    0 <= x19
    0 <= x20

  Binary variables (12)
    u v x5 x6 x7 x8 x9 x10 x11 x12 x13 x14

Upgrade Notes

  • If users set an empty variable name "" with binary_var(), integer_var(), and continuous_var(), they set the default variable name (e.g., x0) while they used to set the empty name as variable name.

    from qiskit_optimization.problems import QuadraticProgram

qp = QuadraticProgram()
x = qp.binary_var(name="")
y = qp.integer_var(name="")
z = qp.continuous_var(name="")
print(x.name)  # x0
print(y.name)  # x1
print(z.name)  # x2

  • Added support for running with Python 3.10. At the the time of the release, Cplex didn’t have a python 3.10 version and Docplex failed inside docplex.mp.model.Model.binary_var_list().

  • Updates the text format of str and repr of the following objects so that the output is one line.

    If users want to display a multi-line text of QuadraticProgram and OptimizationResult, please use QuadraticProgram’s prettyprint() and OptimizationResult’s prettyprint(), respectively.

    # An example of OptimizationResult
from qiskit_optimization.problems import QuadraticProgram
from qiskit_optimization.algorithms import OptimizationResult, OptimizationResultStatus

qp = QuadraticProgram()
x = qp.binary_var_list(3)
result = OptimizationResult([1.0,2.0,3.0], 10.0, x, OptimizationResultStatus.SUCCESS)

print(repr(result))
# <OptimizationResult: fval=10.0, x0=1.0, x1=2.0, x2=3.0, status=SUCCESS>

print(str(result))
# fval=10.0, x0=1.0, x1=2.0, x2=3.0, status=SUCCESS

print(result.prettyprint())
# objective function value: 10.0
# variable values: x0=1.0, x1=2.0, x2=3.0
# status: SUCCESS

    from qiskit_optimization.problems import QuadraticProgram

qp = QuadraticProgram('problem 1')
qp.integer_var(-1, 2, 'x')
qp.integer_var(-1, 2, 'y')
qp.continuous_var(-1, name='z')
qp.minimize(constant=3, linear={'x': 2, 'y': 3}, quadratic={('z', 'x'): -1})
qp.linear_constraint({'x': 1, 'y': -2}, '>=', 2, name='lin_GE')
qp.linear_constraint({'x': 2, 'y': -1}, '==', 1, name='lin_EQ')
qp.quadratic_constraint({'x': 2, 'y': -1}, {('z', 'z'): -1}, '<=', 1)

print(repr(qp))
# <QuadraticProgram: minimize -x*z + 2*x + 3*y + 3, 3 variables, 3 constraints, 'problem 1'>

print(str(qp))
# minimize -x*z + 2*x + 3*y + 3 (3 variables, 3 constraints, 'problem 1')

print(qp.prettyprint())
# Problem name: problem 1
#
# Minimize
#   -x*z + 2*x + 3*y + 3
#
# Subject to
#   Linear constraints (2)
#     x - 2*y >= 2  'lin_GE'
#     2*x - y == 1  'lin_EQ'
#
#   Quadratic constraints (1)
#     -z^2 + 2*x - y <= 1  'q0'
#
#   Integer variables (2)
#     -1 <= x <= 2
#     -1 <= y <= 2
#
#   Continuous variables (1)
#     -1 <= z

  • The previously deprecated BaseBackend class has been removed. It was originally deprecated in the Qiskit Terra 0.18.0 release.

  • Enable installation of CPLEX for Python 3.10.

  • Support for running with Python 3.6 has been removed. To run Optimization you need a minimum Python version of 3.7.

Bug Fixes

  • Fixed an issue that from_ising() raises an error when Pauli I is given.

  • Fixed an issue that to_ising() returns a wrong operator when there is no variable in an input problem.

Other Notes

  • Shows a warning message if non-printable strings are set to QuadraticProgram as problem name, variable name, or constraint name.

  • Updated the documentation of SUCCESS of OptimizationResultStatus. SUCCESS means the obtained solution is feasible, but not always optimal because some algorithms do not guarantee the optimality.

  • Reword the documentation of all methods and the multi-line text format of OptimizationResult as follows because some algorithms do not guarantee the optimality.

    • “optimal function value” → “objective function value”

    • “optimal value” → “variable values”

0.3.0

New Features

  • Added the runtime client QAOAClient to execute the QAOA algorithm on Qiskit runtime. This runtime program leverages QAOA dedicated transpiler passes such as swap strategies and pulse-efficient transpiler passes for cross-resonance based hardware. Both these optimizations can significantly reduce circuit depth and improve execution time and results. Further, the QAOA runtime also allows using CVaR expectation values, which can improve the performance of ground state calculations in optimization settings.

    The client can for instance be used as

    from qiskit import IBMQ
from qiskit.algorithms.optimizers import COBYLA
from qiskit.opflow import I, Z
from qiskit_optimization.runtime import QAOAClient

# get the provider and backend we use to run the program
IBMQ.load_account()
provider = IBMQ.get_provider(hub="ibm-q", group="open", project="main")
backend = provider.get_backend("ibmq_qasm_simulator")

# define diagonal Hamiltonian whose minimum eigenvalue we want to find
op =  (Z ^ Z ^ I ^ I ^ I) - (I ^ I ^ Z ^ Z ^ I) 

# set up the client and solve the problem
client = QAOAClient(
    reps=2,  # use p=2 repetitions in the QAOA ansatz
    optimizer=COBYLA(), 
    alpha=0.75,  # use CVaR expectation with 75% of the best readouts
    provider=provider, 
    backend=backend
)
result = client.compute_minimum_eigenvalue(op)

    See also the new QAOA Runtime tutorial in docs/tutorials/12_qaoa_runtime.ipynb for more details.

  • Introduced the Sherrington-Kirkpatrick (SK) model [1] qiskit_optimization.applications.SKModel. The model has all-to-all ferromagnetic and antiferromagnetic interactions given by a random disorder and represents a mean-field approximation of a spin glass.

    Let \(x\in\{\pm 1\}^n\) be a configuration of spins. The SK model Hamiltonian on \(n\) sites is

    \[\begin{array}{} H(x)=-1/\sqrt{n} \sum_{i<j} w_{i,j}x_ix_j,\text{ where } i,j\in [n], \end{array}\]

    \(w_{i,j}\in\{\pm 1\}\) are called disorder and are chosen independently and uniformly at random.

    The computational problem associated with this class is to find the ground state of the SK Hamiltonian instance and its energy.

    [1]: Dmitry Panchenko. “The Sherrington-Kirkpatrick model: an overview”, https://arxiv.org/abs/1211.1094

Upgrade Notes

  • The deprecated methods QuadraticProgram.from_docplex and QuadraticProgram.to_docplex have been removed and no longer exist. These methods were deprecated as part of the 0.2.0 release. Instead you should use from_docplex_mp() and to_docplex_mp().

Deprecation Notes

  • Rename the runtime “programs” to runtime “clients” to avoid name confusions and reflect the fact that they are an interface for code executed in the cloud. The classes VQEProgram, QAOAProgram and VQEProgramResult have been renamed to VQEClient, QAOAClient and VQERuntimeResult, respectively.

Bug Fixes

  • Allow Qiskit’s Optimizer classes as input for the optimizer in the VQEProgram and QAOAProgram instead of only dictionaries.

0.2.0

New Features

  • Adds the support of indicator constraints (e.g. x=1 -> y+z=1) in from_docplex_mp() using the big-M formulation.

  • Adds translators between Ising Hamiltonian and qiskit_optimization.problems.QuadraticProgram, from_ising() and to_ising().

  • Introduced a new converter class qiskit_optimization.converters.LinearInequalityToPenalty. It converts the following inequality constraints to penalty terms where x, y, \(x_i\) are binary variables and P is a penalty factor.

    \[\begin{split}\begin{array}{} \text { Inequality constraint } & & \text { Penalty term } \\ x \leq y & \rightarrow & P(x-x y) \\ x \geq y & \rightarrow & P(y-x y) \\ \sum_{i=1}^n x_i \leq 1, n \geq 2 & \rightarrow & P \sum_{i, j : i < j} x_i x_j\\ \sum_{i=1}^n x_i \geq n-1, n \geq 2 & \rightarrow & P \sum_{i, j : i < j} (1 - x_i) (1 - x_j) \end{array}\end{split}\]

  • Allow leveraging Qiskit Runtime to execute VQE and QAOA in the cloud using the VQEProgram and QAOAProgram.

Upgrade Notes

  • QuadraticProgram.pprint_as_string and QuadraticProgram.prettyprint have been removed, which were deprecated in Qiskit Aqua 0.8.0 release (October 2020).

  • Changes qiskit_optimization.algorithms.MinimumEigenOptimizer.solve() to return the best solution in terms of the original problem, i.e., MinimumEigenOptimizationResult.samples[0], as qiskit_optimization.algorithms.MinimumEigenOptimizationResult.x(). It used to be the best solution in terms of the converted QUBO problem, i.e., MinimumEigenOptimizationResult.raw_samples[0].

Deprecation Notes

Bug Fixes

  • Sorts the order of result.get_counts(qc) by bitstring in qiskit_optimization.algorithms.GroverOptimizer when qasm_simulator is used so that the algorithm behaves deterministically. The previous version sorts the counts by probabilities, but some bitstrings may have the same probability and the algorithm could behave probabilistically.