Demonstrating NISQ Era Challenges in Algorithm Design on IBM's 20 Qubit Quantum Computer

• URL: http://arxiv.org/abs/2003.01009v3
• Date: Mon, 31 Aug 2020 15:46:43 GMT
• Title: Demonstrating NISQ Era Challenges in Algorithm Design on IBM's 20 Qubit Quantum Computer
• Authors: Daniel Koch, Brett Martin, Saahil Patel, Laura Wessing, Paul M. Alsing
• Abstract summary: We present results from experiments run on IBM's 20-qubit Poughkeepsie' architecture. Results demonstrate various qubit qualities and challenges that arise in designing quantum algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As superconducting qubits continue to advance technologically, the realization of quantum algorithms from theoretical abstraction to physical implementation requires knowledge of both quantum circuit construction as well as hardware limitations. In this study we present results from experiments run on IBM's 20-qubit `Poughkeepsie' architecture, with the goal of demonstrating various qubit qualities and challenges that arise in designing quantum algorithms. These include experimentally measuring $T_1$ and $T_2$ coherence times, gate fidelities, sequential CNOT gates, techniques for handling ancilla qubits, and finally CCNOT and QFT$^{\dagger}$ circuits implemented on several different qubit geometries. Our results demonstrate various techniques for improving quantum circuits which must compensate for limited connectivity, either through the use of SWAP gates or additional ancilla qubits.

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 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 Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• 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)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Performance of Superconducting Quantum Computing Chips under Different Architecture Design [0.0]
  We study the quantum processor performance under different qubit connectivity and topology. It is shown that a high-performance architecture almost always comes with a design with a large connectivity. Different quantum algorithms show different dependence on quantum chip connectivity and topologies.
  arXiv  Detail & Related papers  (2021-05-13T03:36:25Z)
• Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era [0.0]
  We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis for noise quantum computers with limited number of qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.
  arXiv  Detail & Related papers  (2021-04-27T16:11:27Z)
• Variational quantum compiling with double Q-learning [0.37798600249187286]
  We propose a variational quantum compiling (VQC) algorithm based on reinforcement learning (RL) An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices.
  arXiv  Detail & Related papers  (2021-03-22T06:46:35Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.