The Future of Quantum Computing with Superconducting Qubits

• URL: http://arxiv.org/abs/2209.06841v2
• Date: Mon, 28 Nov 2022 04:29:46 GMT
• Title: The Future of Quantum Computing with Superconducting Qubits
• Authors: Sergey Bravyi, Oliver Dial, Jay M. Gambetta, Dario Gil, and Zaira Nazario
• Abstract summary: We see a branching point in computing paradigms with the emergence of quantum processing units (QPUs) Extracting the full potential of computation and realizing quantum algorithms with a super-polynomial speedup will most likely require major advances in quantum error correction technology. Long term, we see hardware that exploits qubit connectivity in higher than 2D topologies to realize more efficient quantum error correcting codes.
• Score: 2.6668731290542222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For the first time in history, we are seeing a branching point in computing paradigms with the emergence of quantum processing units (QPUs). Extracting the full potential of computation and realizing quantum algorithms with a super-polynomial speedup will most likely require major advances in quantum error correction technology. Meanwhile, achieving a computational advantage in the near term may be possible by combining multiple QPUs through circuit knitting techniques, improving the quality of solutions through error suppression and mitigation, and focusing on heuristic versions of quantum algorithms with asymptotic speedups. For this to happen, the performance of quantum computing hardware needs to improve and software needs to seamlessly integrate quantum and classical processors together to form a new architecture that we are calling quantum-centric supercomputing. Long term, we see hardware that exploits qubit connectivity in higher than 2D topologies to realize more efficient quantum error correcting codes, modular architectures for scaling QPUs and parallelizing workloads, and software that evolves to make the intricacies of the technology invisible to the users and realize the goal of ubiquitous, frictionless quantum computing.

Related papers

• How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities [3.864855748348313]
  Small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits.
  arXiv  Detail & Related papers  (2024-11-15T18:22:46Z)
• Technology and Performance Benchmarks of IQM's 20-Qubit Quantum Computer [56.435136806763055]
  IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. The focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.
  arXiv  Detail & Related papers  (2024-08-22T14:26:10Z)
• 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)
• 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)
• 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)
• 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)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation [5.381727213688375]
  We are still a long way from reaching the maturity of a full-fledged quantum computer. An outstanding challenge is to come up with an application that can reliably carry out a nontrivial task. Several near-term quantum computing techniques have been proposed to characterize and mitigate errors.
  arXiv  Detail & Related papers  (2022-11-16T07:53:15Z)
• 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)
• Full-stack quantum computing systems in the NISQ era: algorithm-driven and hardware-aware compilation techniques [1.3496450124792878]
  We will provide an overview on current full-stack quantum computing systems. We will emphasize the need for tight co-design among adjacent layers as well as vertical cross-layer design.
  arXiv  Detail & Related papers  (2022-04-13T13:26:56Z)

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.