Our Vision
Our research vision is to create a symbiotic partnership between these two transformative technologies, where machine learning optimizes and accelerates quantum hardware development, while quantum computing unleashes new paradigms for machine learning algorithms. We envision ML algorithms not only designing and tailoring quantum circuits for specific tasks, but also actively managing and correcting noise in real-time, enabling a leap in quantum coherence and fidelity.
Why Quantum Machine Learning?
Quantum Machine Learning (QML) in the broadest sense is the combination of QC and ML in a synergistic manner that can benefit either or both fields. The potential of QML is two-fold – (1). It can bring a computation advantage to tasks such as classical machine learning, quantum simulation, etc, and (2). It allows the possibility to achieve quantum advantage on current Near Intermediate Scale Quantum (NISQ) devices.
Hybrid Quantum-Classical algorithms have been widely accepted as the most promising approach to achieving quantum advantage on NISQ devices. Most QML algorithms fall within this subset of quantum algorithms. QML in particular also provides additional potential advantages with noise resilience due to the nature of learned algorithms.
Finally, QML provides a new way of thinking about fields, such as quantum information theory, quantum error correction, and quantum foundations. The data science perspective of classical machine learning opens new possibilities for the still-nascent field of quantum computing.
Some of Our Work
Quantum Polar Metric Learning
The architecture of Quantum Polar Metric Learning (QPMeL), a hybrid classical-quantum model for learning embeddings in Hilbert space.
The system learns as a single loop with gradients for the classical network propagating through the quantum circuit to learn functions in the quantum Hilbert space accurately.
The classical network generates 2 sets of vectors that are used as angles for orthogonal rotations in quantum space.
The main components are:
- Classical Head: Learns the image to vector mapping to be encoded onto the quantum computer.
- Embedding Circuit: Quantum Circuit to map data from the classical to quantum space.
- Training Circuit: Wrapped circuit around the embedding circuit to calculate losses and generate gradients
- Loss Function: Hybrid quantum-classical loss based on triplet loss, utilizing state fidelity similarity.
(1). Classical Head:
It consists of the CNN Backbone and the “Angle Prediction Layer“. The classical head uses convolution blocks consisting of CONV + ReLU + MaxPool layers, a dense block with 3 Dense + GeLU layers with reducing dimensionality. The polar form of a qubit can be described in terms of 2 angles- 𝜃 and𝛾 which can be encoded via the 𝑅𝑦 and 𝑅𝑧 gates respectively. QPMeL aims to learn “Rotational Representations” for classical data by creating 2 embeddings for the 𝜃 and 𝛾 parameters respectively per qubit from the classical head.
(2). Embedding Circuit:
The encoding circuit is used to create the state |𝜓⟩ from the classical embeddings. The structure consists of 𝑅𝑦 and 𝑅𝑧 gates separated by a layer of cyclic 𝑍𝑍(𝜃) gates for entanglement.
(3). Training Circuit:
The encoding circuit is used to create the state |𝜓⟩ from the classical embeddings. The structure consists of 𝑅𝑦 and 𝑅𝑧 gates separated by a layer of cyclic 𝑍𝑍(𝜃) gates for entanglement. QPMeL uses separate circuits for training and inference, with 2 main differences- (1). The SWAtest extension requires 2 copies of the encoding circuit (2). Residual Corrections that are only used in the training process. In order to compute the fidelity we use the SWAP test.
(4). Loss Function:
QPMeL uses a quantum extension to triplet loss, which uses State Fidelity as the distance metric. We simplify our loss function by separating the comparison and distance formulation, favoring 2 calls to a much thinner and shallower circuit. This is more practical on NISQ devices with lower coherence time. QPMeL measures distances in Hilbert space using state fidelity and then computes the difference classically.
Future Work
Our future work aims to look towards solving the issue of generalization in quantum machine learning and look towards developing a new probability distribution centric approach to quantum machine learning that emphasis evolution of distributions to enable learning and prediction.