Once properly stated, a problem can be formulated as an objective function
to be solved on a D-Wave solver. The example in the Introduction
chapter states a communication-networks problem as the well-known graph problem,
vertex cover.
D-Wave provides several resource containing many reference examples:
For beginners, Ocean documentation
provides a series of code examples, for different levels of experience, that
explain various aspects of quantum computing in solving problems.
This chapter provides a sample of problems in various fields, and the available
resources for each (at the time of writing); having a relevant reference problem
may enable you to use similar solution steps when solving your own problems on
D-Wave solvers.
Many of these are discrete optimization, also known as combinatorial optimization,
which is the optimization of an objective function defined over a set of discrete
values such as Booleans.
Keep in mind that there are different ways to model a given problem; for example,
a constraint satisfaction problem (CSP) can have various domains, variables,
and constraints. Model selection can affect solution performance, so it may
be useful to consider various approaches.
Table 10 A Sample of Reference Problems in D-Wave’s Resources#
Whether or not you see a relevant problem here, it’s recommended you check out
the examples in D-Wave’s
collection of code examples and
corporate website for the latest examples of
problems from all fields of study and industry.
Fault diagnosis attempts to quickly localize failures as soon as
they are detected in systems such as sensor networks, process monitoring, and safety
monitoring. Circuit fault diagnosis attempts to identify failed gates during
manufacturing, under the assumption that gate failure is rare
enough that the minimum number of gates failing is the most likely cause of the
detected problem.
Computer vision develops
techniques to enable computers to analyse digital images, including video. The
field has applications in industrial manufacturing, healthcare, navigation,
miltary, and many other areas.
[Arr2022] compares quantum and classical approaches to motion segmentation.
[Bir2021] discusses a quantum algorithm for solving a synchronization problem,
specifically permutation synchronization, a non-convex optimization problem in
discrete variables.
[Gol2019] derive an algorithm for correspondence problems on point sets.
[Li2020] translates detection scores from bounding boxes and overlap ratio between
pairs of bounding boxes into QUBOs for removing redundant object detections.
[Ngu2019] demonstrates good prediction performance of a regression algorithm
for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q.
A satisfiability (SAT) filter is a small data structure that enables fast
querying over a huge dataset by allowing for false positives (but not false
negatives).
The factoring problem is to decompose a number into its factors. There is no known
method to quickly factor large integers—the complexity of this problem has
made it the basis of public-key cryptography algorithms.
Artificial intelligence (AI) is transforming the world. You see it every day at home,
at work, when shopping, when socializing, and even when driving a car. Machine
learning algorithms operate by constructing a model with parameters that can be
learned from a large amount of example input so that the trained model can make
predictions about unseen data.
Most of the transformation that AI has brought to-date has been based on deterministic
machine learning models such as feed-forward neural networks. The real world, however,
is nondeterministic and filled with uncertainty. Probabilistic
models explicitly handle this uncertainty by accounting for gaps in our knowledge and
errors in data sources.
A probability distribution is a mathematical function that assigns a probability
value to an event. Depending on the nature of the underlying event, this function
can be defined for a continuous event (e.g., a normal distribution) or a discrete
event (e.g., a Bernoulli distribution). In probabilistic models, probability
distributions represent the unobserved quantities in
a model (including noise effects) and how they relate to the data. The distribution of
the data is approximated based on a finite set of samples. The model infers from the
observed data, and learning occurs as it transforms the prior distributions, defined
before observing the data, into posterior distributions, defined afterward. If the
training process is successful, the learned distribution resembles the actual distribution
of the data to the extent that the model can make correct predictions about unseen
situations—correctly interpreting a previously unseen handwritten digit, for example.
In short, probabilistic modeling is a practical approach for designing machines that:
Learn from noisy and unlabeled data
Define confidence levels in predictions
Allow decision making in the absence of complete information
Infer missing data and latent correlations in data
Machine learning algorithms operate by constructing a model with parameters that can be
learned from a large amount of example input so that the trained model can make
predictions about unseen data.
A Boltzmann distribution is an energy-based discrete distribution that defines
probability, \(p\), for each of the states in a binary vector.
Assume \(\vc{x}\) represents a set of \(N\) binary random variables.
Conceptually, the space of \(\vc{x}\) corresponds to binary representations of
all numbers from 0 to \(2^N - 1\). You can represent it as a column vector,
\(\vc{x}^T = [x_1, x_2, \dots, x_N]\), where \(x_n \in \{0, 1\}\) is the
state of the \(n^{th}\) binary random variable in \(\vc{x}\).
The Boltzmann distribution defines a probability for each possible state that
\(\vc{x}\) can take using[1]
where \(E(\vc{x};\theta)\) is an energy function parameterized by \(\theta\),
which contains the biases, and
\begin{equation}
Z = \sum_x{\exp(-E(\vc{x};\theta))}
\end{equation}
is the normalizing coefficient, also known as the partition function, that ensures
that \(p(\vc{x})\) sums to 1 over all the possible states of \(x\); that is,
Note that because of the negative sign for energy, \(E\), the states with high
probability correspond to states with low energy.
The energy function \(E(\vc{x};\theta)\) can be represented as a QUBO:
the linear coefficients bias the probability of individual binary variables
in \(\vc{x}\) and the quadratic coefficients represent the correlation
weights between the elements of \(\vc{x}\). The D-Wave architecture, which
natively processes information through the Ising/QUBO models (linear coefficients
are represented by qubit biases and quadratic coefficients by coupler strengths),
can help discrete energy-based machine learning.
Sampling from energy-based distributions is a computationally intensive task
that is an excellent match for the way that the D-Wave system solves
problems; that is, by seeking low-energy states. Samples from the D-Wave QPU
can be obtained quickly and provide an advantage over sampling from classical
distributions.
When training a probabilistic model, you need a well-characterized distribution;
otherwise, it is difficult to calculate gradients and you have no guarantee of
convergence. While both classical Boltzmann and quantum Boltzmann distributions
are well characterized, all but the smallest problems solved by the QPU should
undergo postprocessing to bring them closer to a Boltzmann distribution; for example,
by running a low-treewidth postprocessing algorithm.
As in statistical mechanics, \(\beta\) represents inverse temperature:
\(1/(k_B T)\), where \(T\) is the thermodynamic temperature in kelvin and
\(k_B\) is Boltzmann’s constant.
The D-Wave QPU operates at cryogenic temperatures, nominally \(15\) mK, which
can be translated to a scale parameter \(\beta\). The effective value of
\(\beta\) varies from QPU to QPU and in fact from problem to problem
since the D-Wave QPU samples are not Boltzmann and time-varying phenomena
may affect samples. Therefore, to attain Boltzmann samples, run the
Gibbs chain for a number of iterations starting from quantum computer samples.
The objective is to further anneal the samples to the correct temperature of
interest \(T = 1/{\beta}\), where \(\beta = 1.0\).
In the D-Wave software, postprocessing refines the returned solutions to
target a Boltzmann distribution characterized by \(\beta\), which is represented
by a floating point number without units. When choosing a value for \(\beta\),
be aware that lower values result in samples less constrained to the lowest energy
states. For more information on \(\beta\) and how it
is used in the sampling postprocessing algorithm, see the QPU Solver Datasheet guide.
Probabilistic Sampling: RBM
A restricted Boltzmann machine (RBM) is a special type of Boltzmann machine
with a symmetrical bipartite structure; see Figure 40.
It defines a probability distribution over a set of binary variables that are
divided into visible (input), \(\vc{v}\), and hidden, \(\vc{h}\), variables,
which are analogous to the retina and brain, respectively.[2]
The hidden variables allow for more complex dependencies among visible variables
and are often used to learn a stochastic generative model over a set of inputs.
All visible variables connect to all hidden variables, but no variables in the
same layer are linked. This limited connectivity makes inference and therefore
learning easier because the RBM takes only a single step to reach thermal
equilibrium if you clamp the visible variables to particular binary states.
During the learning process, each visible variable
is responsible for a feature from an item in the dataset to be learned. For
example, images from the famous MNIST dataset of handwritten digits[3] have
784 pixels, so the RBMs that are training from this dataset require 784 visible
variables. Each variable has a bias and each connection between variables
has a weight. These values determine the energy of the output.
Without the introduction of hidden variables, the energy function \(E(\vc{x})\)
by itself is not sufficiently flexible to give good models. You can write
\(\vc{x}=[\vc{v},\vc{h}]\) and denote the energy function as \(E(\vc{v},\vc{h})\).
which you can obtain by marginalizing over the hidden variables, \(\vc{h}\).
A standard training criterion used to determine the energy function is to maximize
the log likelihood (LL) of the training data—or, equivalently, to minimize the
negative log likelihood (NLL) of the data. Training data is repetitively fed to
the model and corresponding improvements made to the model.
When training a model, you are given \(D\) training (visible) examples
\(\vc{v}^{(1)}, ..., \vc{v}^{(D)}\), and would like to find a setting for
\(\theta\) under which this data is highly likely. Note that \(n^{th}\)
component of the \(d^{th}\) training example is \(v_n^{(d)}\).
To find \(\theta\), maximize the likelihood of the training data:
The likelihood is \(L(\theta) = \prod_{d=1}^D p(v^{(d)};\theta)\)
It is more convenient, computationally, to maximize the log likelihood:
You can use the gradient descent method to minimize the \(NLL(\theta)\):
Starting at an initial guess for \(\theta\) (say, all zero values), calculate
the gradient (the direction of fastest improvement) and then take a step in that
direction.
Iterate by taking the gradient at the new point and moving downhill again.
To calculate the gradient at a particular \(\theta\), evaluate some
expected values: \(E_{p(\vc{x};\theta)} f(\vc{x})\) for a set of functions
\(f(\vc{x})\) known as the sufficient statistics. The expected values cannot be
determined exactly, because you cannot sum over all \(2^N\) configurations;
therefore, approximate by only summing over the most probable configurations,
which you can obtain by sampling from the distribution given by the current
\(\theta\).
Energy-Based Models
Machine learning with energy-based models (EBMs) minimizes an objective function
by lowering scalar energy for configurations of variables that best represent
dependencies for probabilistic and nonprobabilistic models.
For an RBM as a generative model, for example, where the gradient needed to
maximize log-likelihood of data is intractable (due to the partition function
for the energy objective function), instead of using the standard
Gibbs’s sampling, use samples from the D-Wave system. The training will
have steps like these: a. Initialize variables. b. Teach visible nodes with
training samples. c. Sample from the D-Wave system. d. Update and repeat as
needed.
Support Vector Machines
Support vector machines (SVM) find a hyperplane separating data into classes
with maximized margin to each class; structured support vector machines (SSVM)
assume structure in the output labels; for example, a beach in a
picture increases the chance the picture is of a sunset.
Boosting
In machine learning, boosting methods are used to combine a set of simple,
“weak” predictors in such a way as to produce a more powerful, “strong”
predictor.
[Bia2010] discusses using quantum annealing for machine learning applications
in two modes of operation: zero-temperature for optimization and finite-temperature
for sampling.
[Ben2017] discusses sampling on the D-Wave system.
[Inc2022] presents a QUBO formulation of the Graph Edit Distance problem and
uses quantum annealing and variational quantum algorithms on it.
[Muc2022] proposes and evaluates a feature-selection algorithm based on QUBOs.
[Per2022] presents a systematic literature review of 2017–21 published papers
to identify, analyze and classify different algorithms used in quantum machine
learning and their applications.
[Vah2017] discusses label noise in neural networks.
RBMs:
[Ada2015] describes implementing an RBM on the D-Wave system to generate samples
for estimating model expectations of deep neural networks.
[Dum2013] discusses implementing an RBM using physical computation.
Map coloring is an example of a
constraint satisfaction problem (CSP).
CSPs require that all a problem’s variables be assigned values, out of a finite
domain, that result in the satisfying of all constraints. The map-coloring CSP
is to assign a color to each region of a map such that any two regions sharing a
border have different colors.
One promise of quantum computing lies in harnessing programmable quantum devices
for practical applications such as efficient simulation of quantum materials and
condensed matter systems; for example, simulation of geometrically frustrated
magnets in which topological phenomena can emerge from competition between quantum
and thermal fluctuations.
Protein folding refers to the way protein chains structure themselves in the
context of providing some biological function. Although their constituent
amino acids enable multiple configurations, proteins rarely misfold (such
proteins are a cause of disease) because the standard configuration
has lower energy and so is more stable.
[Kin2021] report on experimental observations of equilibration in simulation
of geometrically frustrated magnets.
[Mni2021] reduces the molecular Hamiltonian matrix in Slater determinant basis
to determine the lowest energy cluster.
[Per2012] discusses using the D-Wave system to find the lowest-energy
configuration for a folded protein.
[Tep2021] uses a quantum-classical solver to calculate excited electronic
states of molecular systems. Note that the paper uses the
qbsolv package, which
has since been discontinued in favor of Leap hybrid solvers and the
dwave-hybrid package.
The well-known job-shop scheduling
problem is to maximize priority or minimize schedule
length (known as a makespan, the time interval between starting the first
job and finishing the last) of multiple jobs done on several machines, where a
job is an ordered sequence of tasks performed on particular
machines, with constraints that a machine executes one task at a time and
must complete started tasks.
[Ike2019] describes an implementation of nurse scheduling.
[Kur2020] describes an implementation of job-shop scheduling on a D-Wave QPU
solver.
[Liu2020] proposes to use deep reinforcement learning on job-shop scheduling.
[Ven2015] describes an implementation of job-shop scheduling on the D-Wave system,
which includes formulating the problem, translating to QUBO, and applying
variable reduction techniques. It also talks about direct embedding of local
constraints.
One form of the traffic-flow optimization problem is to minimize the travel time
of a group of vehicles from their sources to destinations by minimizing congestion on
the roads being used.