QUANTUM
Machine Learning

Version 1.0, November 2021

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Why Quantum Machine Learning?
Cambridge Quantum aims at making near-term
quantum computers useful for hard tasks of practicalrelevance in machine learning and optimization.
Despite advances in classical algorithms andhardware those tasks require large compute resources
and cause high energy consumption. Quantum machinelearning (QML) comprises a range of flexible techniques
for tackling this challenge. On one hand, QML models
can perform tasks that go beyond the classically possible.
On the other hand, QML allows the workload to be split
optimally between quantum and classical processing
units in order to make the most of the quantum
computers available today.

Better models for prediction
and reasoning
Machine learning, in essence, can be broken into three parts:
data, models, and training algorithms. A training algorithm
learns from data in order to perform and improve on a task.
Typical tasks include prediction, classification, decision making,data generation, and anomaly detection.
Augmenting complex models or costly data generation
with quantum computing resources could lead to greater
accuracy in downstream tasks. The intuition is that quantum
computers naturally excel at certain key ingredients thatdefine machine learning – modelling complex probability
distributions and generating hard-to-simulate data.
Many traditional machine learning models can beunderstood as probabilistic models of observable data andunobservable features or variables. For example, a trader
may want to base their buy/sell decisions on the state of theequity market: is it bull or bear? While they cannot observe thisabstract state directly, they can observe equity returns fromtraded assets. Based on their domain knowledge they build
a probabilistic model that describes how the market state
influences equity returns. Given this model and the return data,
they can infer and reason about the unobserved market stateusing statistical methods and machine learning. Performing
this inference exactly and quickly becomes intractable forcomplex models.
Near-term quantum computers are naturally suitedfor representing such complex probability distributions. Once
trained the quantum computer can be used in downstreamtasks that rely on sampling complex data. The example of thetrader performing inference is one such downstream task.

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INPUT (OPTIONAL)OUTPUT

Learnable distribution
Data sample

Samples from learned distribution

PRE-PROCESSING

PARAMETERIZED QUANTUM CIRCUITPOST-PROCESSING

MkInput:xx

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U (x)

P(x)

U0

{M k x ,0}

K
k=1
K

{ M k x ,0 } k=1

Machine Learning
Classical Challenge

Not natively probabilistic as classical computers
are deterministic. Resource intensive and datahungry.

Quantum
Improvement

More expressive models and enhanced datageneration

PracticalImplementations

Cambridge Quantum team are world pioneers inimplementing generative models with quantum
hardware with many successful public results

Industrial Applications

Financial forecasting, synthetic data generation,
decision support system

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OUR WORK INQUANTUM MACHINELEARNINGIMAGE RESTORATION AND DATA RECOVERY
ORIGINAL

CORRUPTEDANOMALY DETECTION
CREDIT CARD FRAUD DETECTION

MODELLING CORRELATED CURRENCY PAIRSBORN MACHINESRESTORED

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Generator iterations
Generator is classical quantum

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Circuit depth

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A pragmatic, near-term
results-driven approach
Our approach to quantum machine learning is to focus scarcequantum resources on hard subtasks in machine learning algorithms.
Consequently, our pioneering research centres around hybrid
quantum-classical probabilistic methods for unsupervised learning,generative models, and inference.
A common approach to unsupervised learning is to learna probabilistic model of data. These models can be trained on
unlabelled datasets. Generative modelling is one example ofunsupervised learning wherein the model automatically learns thepatterns of input data in such a way that it can be used to generate
new data samples that would seemingly match the statistical
distribution of the original dataset.

Quantum circuit Born machines are an example of generativemodels and are based on parameterized quantum circuits. They
can represent probabilistic models and sample complex probability
distributions more efficiently than classical computers for certain
tasks or datasets. We can train these Born machines for a diverse
range of downstream tasks such as reasoning under uncertainty,data augmentation, and anomaly detection. Generative models
can also make machine learning more transparent by incorporating
domain expertise.
We implement quantum machine learning methods in a
pragmatic and hardware-agnostic way. This allows us to implement
small-scale models on today’s limited quantum hardware andimprove the models as quantum hardware matures.

1

Benedetti et alhttps://arxiv.org/abs/2103.06720
2021.

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Applications of Quantum
Machine Learning
Finance– Quantum-enhanced variational inference on
hidden Markov models for time-series data
– Born Machines for foreign exchange spot
return modelling– Sampling financial data for Monte Carlo
pricing using quantum GANs and Bornmachines

Pharmaceuticals andHealthcare– Meta-heuristics for faster biomarker
discovery in drug development based on
quantum circuit Born machines– Medical diagnosis with quantum-enhanced
inference on Bayesian networks

Energy– Crude oil flow classification with quantum
support vector machines– Inference-based risk assessment of windturbines– Generative modelling for renewable energy
scenario generation

Materials andChemistry– Molecular discovery with quantum-enhanced
generative models

Some of our collaborations
PHARMACEUTICALJSR Life Science / CrownBio
Novel biomarker discovery
Cambridge Quantum’s quantum machine learningalgorithms enable more accurate biomarker
discovery for cancer drug development.

FINANCE
Large investment firm
Pricing for risk management
Sampling financial data for Monte Carlo pricing
using quantum generative adversarial networks
and Born machines.

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WHY GET STARTEDWITH QML TODAY?
Work towards quantum advantage for yourintractable machine learning problems
Accelerate R&D efforts and showcase leading
capabilities
Develop pathway for easy scalability as hardware
matures
Educate your workforce in quantum computing
methodologies

CONTACT US
Please contact our team at
Email: support@cambridgequantum.com

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We set out our vision to positively transform the worldusing the power of quantum computing back in 2014.
Today, we are recognised as one of the foremost quantum
computing companies, delivering science-led,enterprise-driven solutions to tackle hard problems
across a diverse range of industries.
Cambridge Quantum designs, engineers and deploys
algorithms and enterprise application libraries,
translating cutting-edge research into industry leading
technologies through a product-centric focus. TKET, ourhardware-agnostic software development platform, andother technologies are currently utilised by an expansive
and ever-growing user base.
The team at Cambridge Quantum has been developing
the theoretical foundations of quantum computing
for over 25 years, forging ahead with breakthroughsin the fields of quantum chemistry, quantum artificial
intelligence, quantum cybersecurity and quantum
algorithms.
At present, we have the deepest roster of researchers,developers and engineers, working to democratisequantum computation and realise the benefits for thegreatest possible number of people.

FOR MORE INFORMATION
LinkedIn
CambridgeQuantum.com

Published by Cambridge Quantum

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