What is quantum machine learning? How it works

How does quantum machine learning work?

QML employs principles of quantum physics, such as superposition and entanglement, to perform complex calculations on quantum data sets. The goal is to perform those calculations in less time while using less energy as well as reveal deeper patterns and associations in the data to build better-performing ML models. QML is most effective with complex problems and enormous data sets that classical computer systems cannot represent or process effectively.

• Data is encoded into qubits and represented by the alignment of the qubit's subatomic particles -- its spin. Classical data can be encoded into qubits so that the quantum computer can process it.
• Data is processed through a quantum algorithm. Numerous algorithms exist, including quantum support vector machines (QSVMs) and quantum neural networks (QNNs). The algorithms are implemented to run on a quantum computer and perform complex calculations -- such as data classification or clustering -- on the quantum data.
• Quantum algorithms are implemented using quantum logic gates to build a quantum circuit, similar to how digital logic gates are composed to create large and complex digital logic circuits. These quantum gates process and manipulate the qubits. The quantum algorithm and corresponding quantum circuit become the quantum machine learning model.
• The quantum ML model, embodied by the quantum algorithm and quantum circuit, is trained to find optimal parameters that generally provide the ideal results and improve the model's operation.

This process essentially duplicates the ideas of machine learning using classical computers and algorithms. While classical elements are replaced or enhanced with quantum computers and algorithms, the underlying goals are identical.

Types of quantum machine learning

Quantum machine learning can be implemented in various ways, primarily defined by how quantum computers are used and integrated into the ML process. Six general approaches are being explored today using classical data and computers and mixing with quantum data and computers:

• Classical data sets processed on classical computers. This is traditional machine learning and does not use quantum computers or algorithms.
• Quantum data sets processed on classical computers. This approach uses classical ML algorithms to learn from the results of quantum states. For example, a quantum experiment might return data about the experiment, which can then be expressed as a data set that a classical computer and ML algorithm can process and use to train an ML model.
• Classical data sets processed on quantum computers. A business might use a quantum computer to process its current data to accelerate a training process, such as training more complex models, or to find new or stronger patterns in current data -- insights that might not have been possible to glean from classical computer systems. Common examples include QSVMs and QNNs.
• Quantum data sets processed on quantum computers. This is the most comprehensive and dedicated approach to QML where quantum data is processed directly on quantum computers, and the results are used to train ML models directly. This approach is also in its earliest stages of maturity, but is expected to thrive as quantum computers standardize and become more reliable and readily available.
• Quantum-inspired machine learning. The focus here is not actual computing, but rather the development of new and innovative ML algorithms intended to mimic the approach used by quantum computers. For example, quantum-inspired algorithms might be developed to explore image or data classification or resource optimizations. Although these algorithms typically operate on classical computers, the benefit is how models are envisioned and developed.
• Hybrid quantum machine learning. This approach uses both quantum and classical computers, enabling classical ML tools to benefit from quantum systems. The challenge here is effective quantum-to-classical interfacing and data exchanges.

Dedicated quantum machine learning relies on quantum algorithms. There are eight major quantum algorithm types available for QML, including the following:

• Quantum annealing uses superposition and entanglement to solve optimization problems.
• Quantum error correction uses ML algorithms to support quantum error correction tasks.
• QNN uses quantum computing to handle computations within neural networks.
• Quantum principal component analysis uses quantum algorithms to reduce the data's dimensionality and simplify its expression.
• Quantum reinforcement learning uses quantum algorithms to reinforce learning tasks.
• QSVM uses quantum algorithms for tasks such as data classification.
• Quantum-enhanced optimization uses quantum algorithms to enhance and improve machine learning models.
• Variational quantum circuits employ a category of QML methods to build algorithms on current quantum computing systems.

Benefits and challenges of quantum machine learning

Organizations seeking to adopt quantum machine learning techniques must consider various potential benefits and limitations carefully. Common benefits associated with QML include the following:

• Faster ML. Quantum computers running quantum algorithms can offer significantly faster data processing involving enormous data sets and complex calculations. In effect, QML can help when an ML task's processing and computational demands exceed the practical capabilities of traditional computing resources.
• Better accuracy. Quantum computing can use quantum behaviors such as superposition and entanglement to enhance machine learning tasks like data regression, clustering and classification. This can identify patterns in data more accurately and efficiently, leading to better outcomes such as improved pattern recognition.
• Deeper optimizations. The strength of quantum computing is its ability to consider many potential paths simultaneously. QML finds more correlations in data sets by exploring far more permutations. This can enable quantum algorithms to pursue optimization tasks -- such as better logistics -- far more effectively than common ML algorithms, leading to better workflow and resource utilization optimization.
• Broader ML use cases. Quantum computing and QML can support research and computational results for problems that are not practical or intractable for conventional computers, such as quantum research, biochemistry, climate modeling and high-energy physics. This leads to enhanced simulation and modeling tasks.

Despite the potential for significant benefits, quantum machine learning faces serious challenges that can limit its practicality for many organizations. Current QML challenges include the following:

• Quantum computing hardware. Quantum machine learning requires a quantum computer, but quantum computers are still early in their maturity and lack many architectural and interface standards common in traditional computers. They are plagued by issues such as limited qubits, quantum errors and scalability. Quantum computer hardware must become more reliable and standardized to make QML attractive on a broad scale.
• Algorithm limitations. Machine learning demands effective and efficient models and algorithms, but quantum algorithm development is still nascent. Strong quantum algorithm design is critical to outperforming traditional computer algorithms. Still, this requires access to well-built and reliable quantum computers and well-defined quantum programming standards -- all of which require further advancement.
• Limited data. All machine learning faces data quality and availability issues. Data needs to be available, accurate, complete and bias-free. This is challenging enough for current ML efforts, but can be severely limiting when QML can process far more data; data quality problems multiply in quantum computing and QML.
• Few integrations. Quantum computing rarely operates alone. QML can coexist with or enhance traditional ML training. However, few standardized integration methods are suited to connect quantum computers and QML tasks with traditional computer networks and infrastructures. Better integrations are essential for smooth data exchanges between quantum and traditional computer systems.

Quantum machine learning use cases

QML presents three broad types of practical uses:

• Accelerate training. Quantum computers can execute quantum algorithms capable of processing enormous data sets and performing advanced mathematics on that data far faster than traditional computers running common ML algorithms.
• Deeper analysis. The speed at which quantum computers can process quantum information can enable QML techniques to address tremendous volumes of complex and unstructured data to identify patterns or discover relationships that are too difficult for traditional computers and algorithms.
• Better algorithms. Quantum computers' capabilities can enable the development of powerful and comprehensive ML algorithms. This supports greater innovation and enhances the competitive landscape of ML models and AI platforms by enabling developers to build better algorithms that provide more accurate, nuanced and meaningful results.

These three broad types of QML uses can be further broken down into more specific examples related to varied industry use cases, including the following:

• Environmental. QML can bring more insight and nuance to weather forecasting, environmental modeling and disaster prediction, also leading to better disaster planning and response.
• Financial modeling. QML can enhance the detail and accuracy of financial modeling, risk evaluation and financial fraud detection.
• Government. QML can boost government services such as public transportation and public health modeling.
• Logistics. QML can provide deep insight into patterns that can be used to optimize logistics, such as transportation and inventory management.
• Manufacturing. QML can help manufacturing activity by optimizing production schedules, planning resource procurement and allocation, and boosting quality control by improving monitoring and defect detection.
• Pharmaceutical development. QML can simulate complex behaviors such as molecular behaviors and interactions, which can help with drug research and discovery.
• Retail. QML can enhance retail-related activities such as inventory management, demand forecasting and intelligent agents for purchase recommendation systems.
• Security. QML can enhance data security by identifying potential threats and malicious activity, and employing quantum computing for powerful cryptography and secure data communication.
• Utilities. QML can optimize energy grids by predicting energy demand based on factors such as weather and then planning energy storage and distribution accordingly.