Heterogeneous integration of spin-photon interfaces with a scalable CMOS
platform
- URL: http://arxiv.org/abs/2308.14289v2
- Date: Wed, 20 Dec 2023 22:48:57 GMT
- Title: Heterogeneous integration of spin-photon interfaces with a scalable CMOS
platform
- Authors: Linsen Li, Lorenzo De Santis, Isaac Harris, Kevin C. Chen, Yihuai Gao,
Ian Christen, Matthew Trusheim, Hyeongrak Choi, Yixuan Song, Carlos
Errando-Herranz, Jiahui Du, Yong Hu, Genevieve Clark, Mohamed I. Ibrahim,
Gerald Gilbert, Ruonan Han and Dirk Englund
- Abstract summary: General-purpose quantum computing using local quantum communication networks will require millions of physical qubits to encode thousands of logical qubits.
We introduce a scalable hardware modular architecture "Quantum System-on-Chip" (QSoC)
QSoC features compact two-dimensional arrays "quantum microchiplets" (QMCs) containing tin-vacancy (SnV-) spin qubits integrated on a cryogenic application-specific integrated circuit (ASIC)
- Score: 1.2253948665073315
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Color centers in diamonds have emerged as a leading solid-state platform for
advancing quantum technologies, satisfying the DiVincenzo criteria and recently
achieving a quantum advantage in secret key distribution. Recent theoretical
works estimate that general-purpose quantum computing using local quantum
communication networks will require millions of physical qubits to encode
thousands of logical qubits, which presents a substantial challenge to the
hardware architecture at this scale. To address the unanswered scaling problem,
in this work, we first introduce a scalable hardware modular architecture
"Quantum System-on-Chip" (QSoC) that features compact two-dimensional arrays
"quantum microchiplets" (QMCs) containing tin-vacancy (SnV-) spin qubits
integrated on a cryogenic application-specific integrated circuit (ASIC). We
demonstrate crucial architectural subcomponents, including (1) QSoC fabrication
via a lock-and-release method for large-scale heterogeneous integration; (2) a
high-throughput calibration of the QSoC for spin qubit spectral inhomogenous
registration; (3) spin qubit spectral tuning functionality for inhomogenous
compensation; (4) efficient spin-state preparation and measurement for improved
spin and optical properties. QSoC architecture supports full connectivity for
quantum memory arrays in a set of different resonant frequencies and offers the
possibility for further scaling the number of solid-state physical qubits via
larger and denser QMC arrays and optical frequency multiplexing networking.
Related papers
- Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML)
At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
arXiv Detail & Related papers (2024-11-13T23:56:20Z) - 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) - A scalable cavity-based spin-photon interface in a photonic integrated
circuit [0.15178488157371034]
We show integration of nanophotonic cavities containing tin-vacancy (SnV) centers in a photonic integrated circuit (PIC)
We find with near-term improvements this multiplexed architecture can enable high-fidelity quantum state transfer.
arXiv Detail & Related papers (2024-02-28T05:26:32Z) - 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) - A fully packaged multi-channel cryogenic module for optical quantum
memories [0.2967921768537104]
We report on a cryogenically stable and network compatible quantum-emitter module for memory use.
This quantum-emitter module is a significant development towards advanced quantum networking applications such as distributed sensing and processing.
arXiv Detail & Related papers (2023-02-24T22:20:15Z) - Scalable deterministic integration of two quantum dots into an on-chip
quantum circuit [2.6542279914590465]
Integrated quantum photonic circuits (IQPCs) with deterministically integrated quantum emitters are critical elements for scalable quantum information applications.
We report on a monolithic prototype IQPC consisting of two pre-selected quantum dots deterministically integrated into nanobeam cavities at the input ports of a 2x2 multimode interference beam-splitter.
arXiv Detail & Related papers (2022-12-29T11:04:53Z) - Modelling semiconductor spin qubits and their charge noise environment
for quantum gate fidelity estimation [0.9406493726662083]
The spin of an electron confined in semiconductor quantum dots is a promising candidate for quantum bit (qubit) implementations.
We present here a co-modelling framework for double quantum dot (DQD) devices and their charge noise environment.
We find an inverse correlation between quantum gate errors and quantum dot confinement.
arXiv Detail & Related papers (2022-10-10T10:12:54Z) - 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) - Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems.
Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
arXiv Detail & Related papers (2021-08-30T23:50:05Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z)
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.