Efficient Quantum Circuit Encoding of Object Information in 2D Ray Casting

• URL: http://arxiv.org/abs/2405.16132v1
• Date: Sat, 25 May 2024 08:54:28 GMT
• Title: Efficient Quantum Circuit Encoding of Object Information in 2D Ray Casting
• Authors: Seungjae Lee, Suhui Jeong, Jiwon Seo,
• Abstract summary: Quantum computing holds the potential to solve problems that are practically unsolvable by classical computers. We aim to harness this potential to enhance ray casting, a pivotal technique in computer graphics for simplifying the rendering of 3D objects.
• Score: 7.262444673139455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computing holds the potential to solve problems that are practically unsolvable by classical computers due to its ability to significantly reduce time complexity. We aim to harness this potential to enhance ray casting, a pivotal technique in computer graphics for simplifying the rendering of 3D objects. To perform ray casting in a quantum computer, we need to encode the defining parameters of primitives into qubits. However, during the current noisy intermediate-scale quantum (NISQ) era, challenges arise from the limited number of qubits and the impact of noise when executing multiple gates. Through logic optimization, we reduced the depth of quantum circuits as well as the number of gates and qubits. As a result, the event count of correct measurements from an IBM quantum computer significantly exceeded that of incorrect measurements.

Related papers

• 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 Resonant Dimensionality Reduction and Its Application in Quantum Machine Learning [2.7119354495508787]
  We propose a quantum resonant dimension reduction (QRDR) algorithm based on the quantum resonant transition to reduce the dimension of input data. After QRDR, the dimension of input data $N$ can be reduced into desired scale $R$, and the effective information of the original data will be preserved. Our algorithm has the potential to be utilized in a variety of computing fields.
  arXiv  Detail & Related papers  (2024-05-21T09:26:18Z)
• Scalable Quantum Algorithms for Noisy Quantum Computers [0.0]
  This thesis develops two main techniques to reduce the quantum computational resource requirements. The aim is to scale up application sizes on current quantum processors. While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning.
  arXiv  Detail & Related papers  (2024-03-01T19:36:35Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• QuBEC: Boosting Equivalence Checking for Quantum Circuits with QEC Embedding [4.15692939468851]
  We propose a Decision Diagram-based quantum equivalence checking approach, QuBEC, that requires less latency compared to existing techniques. Our proposed methodology reduces verification time on certain benchmark circuits by up to $271.49 times$.
  arXiv  Detail & Related papers  (2023-09-19T16:12:37Z)
• Tensor-Network Simulations of Noisy Quantum Computers [0.0]
  We simulate the execution of three quantum algorithms on noisy quantum computers. We find that they can be executed with high fidelity even at a moderate loss of entanglement.
  arXiv  Detail & Related papers  (2023-04-04T12:42:18Z)
• Sample-size-reduction of quantum states for the noisy linear problem [0.0]
  We show that it is possible to reduce a quantum sample size in a quantum random access memory (QRAM) to the linearithmic order. We achieve a shorter run-time for the noisy linear problem.
  arXiv  Detail & Related papers  (2023-01-08T05:53:17Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Adiabatic Quantum Graph Matching with Permutation Matrix Constraints [75.88678895180189]
  Matching problems on 3D shapes and images are frequently formulated as quadratic assignment problems (QAPs) with permutation matrix constraints, which are NP-hard. We propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware. The proposed algorithm has the potential to scale to higher dimensions on future quantum computing architectures.
  arXiv  Detail & Related papers  (2021-07-08T17:59:55Z)
• Boundaries of quantum supremacy via random circuit sampling [69.16452769334367]
  Google's recent quantum supremacy experiment heralded a transition point where quantum computing performed a computational task, random circuit sampling. We examine the constraints of the observed quantum runtime advantage in a larger number of qubits and gates.
  arXiv  Detail & Related papers  (2020-05-05T20:11:53Z)

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.