A blueprint for demonstrating quantum supremacy with superconducting qubits

By C. Neill, P. Roushan, Kostyantyn Kechedzhi, Sergio Boixo, Sergei V. Isakov, Vadim N. Smelyanskiy, A. Megrant, B. Chiaro, A. Dunsworth, K. Arya and 22 more. Science, 2017

489 citations recorded by the source corpus.

Scaling up to supremacy Quantum information scientists are getting closer to building a quantum computer that can perform calculations that a classical computer cannot. It has been estimated that such a computer would need around 50 qubits, but scaling up existing architectures to this number is tricky. Neill et al. explore how increasing the number of qubits from five to nine affects the quality of the output of their superconducting qubit device. If, as the number of qubits grows further, the error continues to increase at the same rate, a quantum computer with about 60 qubits and reasonable fidelity might be achievable with current technologies. Science, this issue p. 195 Scaling of errors and output with the number of qubits is explored in a five- to nine-qubit device. A key step toward demonstrating a quantum system that can address difficult problems in physics and chemistry will be performing a computation beyond the capabilities of any classical computer, thus achieving so-called quantum supremacy. In this study, we used nine superconducting qubits to demonstrate a promising path toward quantum supremacy. By individually tuning the qubit parameters, we were able to generate thousands of distinct Hamiltonian evolutions and probe the output probabilities. The measured probabilities obey a universal distribution, consistent with uniformly sampling the full Hilbert space. As the number of qubits increases, the system continues to explore the exponentially growing number of states. Extending these results to a system of 50 qubits has the potential to address scientific questions that are beyond the capabilities of any classical computer.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/7aade870d1f72092e6ce8531f63b7e2935e9c90a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Suguru Endo
2. A. Nahum
3. Matthew Fisher
4. Xiao Yuan
5. V. Khemani
6. Simon C. Benjamin
7. Ying Li
8. S. Vijay

In Atomic and Molecular Physics, and Optics

1. A. Nahum
2. Matthew Fisher
3. V. Khemani
4. S. Vijay
5. Suguru Endo
6. Xiao Yuan
7. Simon C. Benjamin
8. Ying Li

Citations in the corpus

• QAOA for Max-Cut requires hundreds of qubits for quantum speed-up (2018, background): “A valid schedule must satisfy three constraints: (1) the logical dependencies between the gates forming the algorithm, (2) the exclusive activation of a qubit, which can be involved in at most one gate at a time, and (3) the fact that two-qubit gates can be performed only between connected qubits19,”
• Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices (2018, background): “trapped ions [7, 57–59], and superconducting qubits [5, 6, 60, 61].”
• Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers (2020, background): “Crosstalk noise is prevalent across many of the leading qubits including superconducting and trapped ion qubits [12, 20, 31, 46, 50].”
• Rapid High-fidelity Multiplexed Readout of Superconducting Qubits (2018, methodology): “Multiplexed readout with Purcell protection has been achieved by coupling multiple readout resonators to a single resonator based Purcell filter [28].”