What is quantum computing?
Quantum computing refers to data manipulation using quantum-mechanical phenomena such as superposition and entanglement. Going beyond Boolean linear algebra functions on 1s and 0s, quantum computing allows uses quantum bits (Qubits), made up of numbers, vectors, and matrices. Quantum computing is in its early years and has quite a way to go before it matures into a useable technology, when they may transition from a research topic to a classical computer replacement.
Quantum machine learning
The cross-disciplinary Quantum machine learning (QML) is of two research areas: classical machine learning and quantum computing. QML is becoming increasingly popular mainly due to mid-range quantum devices. The current era of quantum computing is known as “Noisy Intermediate-Stage Quantum” or NISQ. This emerging field describes learning models that apply computing conveniences to quantum devices. These models, if used in machine learning, can be potentially improved, however, QML applied on NISQ devices as yet cannot outdo classical techniques.
Tensorflow and quantum computing
The TensorFlow Quantum (TFQ) for Python by Google is a library of advanced insights into the company's Sycamore device. TFQ is a rapid prototyping ML library for hybrid quantum-classical ML models combining TensorFlow (TF) library capabilities and a quantum computing mindset. TFQ partners two Python libraries, Sympy and Cirq, using symbolic math and quantum logic circuit design. When combined, TFQ permits users to develop QML models effortlessly. Cirq provides abstractions for working with modern NISQ environments, and Sympy enters teachable quantum parameters for basic quantum calculations. The following content elaborates its workings.
Figure 1: high-level abstract overview of the computational steps
1. Quantum data to a quantum dataset: Data is converted to quantum data, represented as a multi-dimensional array (quantum tensor). TensorFlow creates a useful dataset by processing these tensors.
2. Select quantum neural network models: Quantum neural network models are selected based on the characteristics of the quantum data structure. Quantum processing is used to decode information hidden in an entangled state.
3. Sample or Average: Classical information in the form of samples from the classical distribution is extracted via measurement of quantum states. Averaging several results sets from steps 1 and 2 can be done in TFQ.
4. Assess a classical neural networks model – Deep learning methods are used to determine the correlation between data, now that quantum data has been converted to classical data.
Possibilities of applying quantum computing in ML
The near-term goal for quantum AI is to improve classical algorithms using quantum algorithms. Different theories exist on how quantum AI improves classical AI.
1. Acceleration of linear algebra: The core of ML is linear algebra. Basic Linear Algebra Subprograms (BLAS) is a collection of linear algebra applications that are an important part of ML algorithms. Matrix multiplication, linear system, and Fourier transforms are a part of these processes, and can potentially be sped up with quantum algorithms.
2. Parallel processing: Quantum superposition is a unique property of quantum computing; allowing a Qubit to be both 1 and 0 at the same time. This enables parallel processing, providing an exponential speedup when compared with classical algorithms. The training process is more efficient if the user is able to combine every possibility to teach the model.
3. Quantum game theory: Quantum game theory is the extension of classical game theory, a process of modeling popular in AI. It adds to classical game theory with superposed initial states, quantum entanglement of initial states, and superposition of strategies used on the initial states. Quantum game theory has the potential to improve algorithms where classical game theory is commonly used.
4. Improving Search Algorithms: Search algorithms are currently designed for classical computing, however, the unique properties of quantum computing, such as superposition and entanglement, may allow quantum search algorithms to be an order of magnitude faster than classical ones.
