32 citations · 174 across the 17 of their papers we have counts for
15 papers · 1 filter
Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin +4
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, a…
A Transferable Machine Learning Approach to Predict Optimized Orbitals for Electronic Structure Problems
Lucas van der Horst, Maniraman Periyasamy, Abhishek Y. Dubey +3
Variational quantum eigensolver ansätze hold considerable promise for ground-state energy calculations on near-term quantum hardware, yet most promising ansatz designs currently st…
Soft-Quantum Algorithms
Basil Kyriacou, Mo Kordzanganeh, Maniraman Periyasamy +1
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parame…
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy +1
Efficient data loading remains a bottleneck for near-term quantum machine learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-…
CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting
Maniraman Periyasamy, Christian Ufrecht, Daniel D. Scherer +1
Whether QML can offer a transformative advantage remains an open question. The severe constraints of NISQ hardware, particularly in circuit depth and connectivity, hinder both the…
Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
Maniraman Periyasamy, Axel Plinge, Christopher Mutschler +2
The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both…
Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
Sebastian Rietsch, Abhishek Y. Dubey, Christian Ufrecht +4
This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a…
Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +4
Quantum computer simulation software is an integral tool for the research efforts in the quantum computing community. An important aspect is the efficiency of respective frameworks…
Optimal joint cutting of two-qubit rotation gates
Christian Ufrecht, Laura S. Herzog, Daniel D. Scherer +4
Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here,…
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading
Maniraman Periyasamy, Marc Hölle, Marco Wiedmann +3
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerba…
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients
Marco Wiedmann, Marc Hölle, Maniraman Periyasamy +5
VQA have attracted a lot of attention from the quantum computing community for the last few years. Their hybrid quantum-classical nature with relatively shallow quantum circuits ma…
Cutting multi-control quantum gates with ZX calculus
Christian Ufrecht, Maniraman Periyasamy, Sebastian Rietsch +3
Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-int…
A Survey on Quantum Reinforcement Learning
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +3
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on…
Incremental Data-Uploading for Full-Quantum Classification
Maniraman Periyasamy, Nico Meyer, Christian Ufrecht +3
The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy i…
Uncovering Instabilities in Variational-Quantum Deep Q-Networks
Maja Franz, Lucas Wolf, Maniraman Periyasamy +5
Deep Reinforcement Learning (RL) has considerably advanced over the past decade. At the same time, state-of-the-art RL algorithms require a large computational budget in terms of t…