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
- 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) - Multi-GPU-Enabled Hybrid Quantum-Classical Workflow in Quantum-HPC Middleware: Applications in Quantum Simulations [1.9922905420195367]
This study introduces an innovative distribution-aware Quantum-Classical-Quantum architecture.
It integrates cutting-edge quantum software framework works with high-performance classical computing resources.
It addresses challenges in quantum simulation for materials and condensed matter physics.
arXiv Detail & Related papers (2024-03-09T07:38:45Z) - 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.