Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Maniraman, P."'
VQC can be understood through the lens of Fourier analysis. It is already well-known that the function space represented by any circuit architecture can be described through a truncated Fourier sum. We show that the spectrum available to that truncat
Externí odkaz:
http://arxiv.org/abs/2411.03450
Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
Autor:
Periyasamy, Maniraman, Plinge, Axel, Mutschler, Christopher, Scherer, Daniel D., Mauerer, Wolfgang
The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both classical and quantum components, are well-suited for noisy intermedia
Externí odkaz:
http://arxiv.org/abs/2404.15751
Autor:
Rietsch, Sebastian, Dubey, Abhishek Y., Ufrecht, Christian, Periyasamy, Maniraman, Plinge, Axel, Mutschler, Christopher, Scherer, Daniel D.
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 given unitary while minimizing circuit depth, total gate count, a spec
Externí odkaz:
http://arxiv.org/abs/2404.14865
Autor:
Meyer, Nico, Ufrecht, Christian, Periyasamy, Maniraman, Plinge, Axel, Mutschler, Christopher, Scherer, Daniel D., Maier, Andreas
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, especially for training variational quantum algorithms. Focusing on
Externí odkaz:
http://arxiv.org/abs/2404.06314
Autor:
Ufrecht, Christian, Herzog, Laura S., Scherer, Daniel D., Periyasamy, Maniraman, Rietsch, Sebastian, Plinge, Axel, Mutschler, Christopher
Publikováno v:
Phys. Rev. A 109, 052440 (2024)
Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here, we introduce a scheme for joint cutting of two-qubit rotation gates ba
Externí odkaz:
http://arxiv.org/abs/2312.09679
Autor:
Wiedmann, Marco, Hölle, Marc, Periyasamy, Maniraman, Meyer, Nico, Ufrecht, Christian, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
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 makes them a promising platform for demonstrating the capabilities of NI
Externí odkaz:
http://arxiv.org/abs/2305.00224
Autor:
Periyasamy, Maniraman, Hölle, Marc, Wiedmann, Marco, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerbated in the case of batch RL, where the agent is trained solely on a pr
Externí odkaz:
http://arxiv.org/abs/2305.00905
Autor:
Ufrecht, Christian, Periyasamy, Maniraman, Rietsch, Sebastian, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
Publikováno v:
Quantum 7, 1147 (2023)
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-intermediate scale quantum (NISQ) era. While previous work focused on cut
Externí odkaz:
http://arxiv.org/abs/2302.00387
Autor:
Meyer, Nico, Ufrecht, Christian, Periyasamy, Maniraman, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
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 quantum reinforcement learning - our interpretation of this term will
Externí odkaz:
http://arxiv.org/abs/2211.03464
Autor:
Periyasamy, Maniraman, Meyer, Nico, Ufrecht, Christian, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
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 intermediate scale quantum (NISQ) devices. Encoding high dimensional da
Externí odkaz:
http://arxiv.org/abs/2205.03057