A Quantum Dot Plot Generation Algorithm for Pairwise Sequence Alignment

• URL: http://arxiv.org/abs/2107.11346v1
• Date: Fri, 23 Jul 2021 16:48:29 GMT
• Title: A Quantum Dot Plot Generation Algorithm for Pairwise Sequence Alignment
• Authors: Joseph Clapis
• Abstract summary: Quantum Pairwise Sequence Alignment (QPSA) algorithm offers exponential speedups in data alignment tasks. It relies on an open problem of efficiently encoding the classical data being aligned into quantum superposition. We provide an alternative, explicit construction of this oracle called the Quantum Dot Plot (QDP) We evaluate QDP's operational complexity via analysis of the quantum machine instructions generated by the Q# and Qiskit software frameworks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Quantum Pairwise Sequence Alignment (QPSA) algorithm offers exponential speedups in data alignment tasks. However, it relies on an open problem of efficiently encoding the classical data being aligned into quantum superposition. Previous works suggest the use of specialized nonlinear Kerr media to implement a black-box oracle that achieves the superposition. We provide an alternative, explicit construction of this oracle called the Quantum Dot Plot (QDP), which is compatible with universal gate machines. We evaluate QDP's operational complexity via analysis of the quantum machine instructions generated by the Q# and Qiskit software frameworks for various sample circuits. Our analysis confirms that the data encoding scheme is exponentially difficult, precluding QDP's (and thus, QPSA's) employment on general-purpose quantum computers. Nevertheless, we find utility in estimating the circuit depth and run time of both QDP and QPSA for IBM's superconducting architecture and AQT's trapped ion architecture for direct comparison and overall practicality purposes.

Related papers

• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• YAQQ: Yet Another Quantum Quantizer -- Design Space Exploration of Quantum Gate Sets using Novelty Search [0.9932551365711049]
  We present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates. The developed software, YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates.
  arXiv  Detail & Related papers  (2024-06-25T14:55:35Z)
• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• A Hybrid Classical-Quantum HPC Workload [0.0]
  A strategy for the orchestration of hybrid classical-quantum workloads on supercomputers featuring quantum devices is proposed. An example application is investigated that offloads parts of computation to a quantum device. The present test bed serves as a basis for more advanced hybrid workloads eventually involving a real quantum device.
  arXiv  Detail & Related papers  (2023-12-08T09:54:51Z)
• Parallel Quantum Hough Transform [0.0]
  We propose a Parallel Quantum Hough transform (PQHT) algorithm that we execute on a quantum computer. The modules were developed using IBM Quantum Composer and tested using the IBM QASM simulator. The successful run results on Fraunhofer Q System One in Ehningen will be presented as a proof of concept for the PQHT algorithm.
  arXiv  Detail & Related papers  (2023-11-15T14:42:51Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Delegated variational quantum algorithms based on quantum homomorphic encryption [69.50567607858659]
  Variational quantum algorithms (VQAs) are one of the most promising candidates for achieving quantum advantages on quantum devices. The private data of clients may be leaked to quantum servers in such a quantum cloud model. A novel quantum homomorphic encryption (QHE) scheme is constructed for quantum servers to calculate encrypted data.
  arXiv  Detail & Related papers  (2023-01-25T07:00:13Z)
• The Basis of Design Tools for Quantum Computing: Arrays, Decision Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
  Quantum computers promise to efficiently solve important problems classical computers never will. A fully automated quantum software stack needs to be developed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
  arXiv  Detail & Related papers  (2023-01-10T19:00:00Z)
• Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow [7.619626059034881]
  We propose an efficient scheme for quantum circuit equivalence checking. The proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less.
  arXiv  Detail & Related papers  (2020-09-04T19:58:53Z)
• Supervised Learning Using a Dressed Quantum Network with "Super Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation [7.599675376503671]
  Implementation of variational Quantum Machine Learning (QML) algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices has issues related to the high number of qubits needed and the noise associated with multi-qubit gates. We propose a variational QML algorithm using a dressed quantum network to address these issues. Unlike in most other existing QML algorithms, our quantum circuit consists only of single-qubit gates, making it robust against noise.
  arXiv  Detail & Related papers  (2020-07-20T16:29:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.