Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
1,240
result(s) for
"Harris, Nicholas"
Sort by:
Deep learning with coherent nanophotonic circuits
Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach–Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.
Programmable silicon nanophotonic processor empowers optical neural networks.
Journal Article
أطلس الناشئة
يبين هذا الكتاب الرائع مدى التشابه والاختلاف بين الشعوب والأماكن في عالمنا حيث تعد \"روسيا\" أكبر دول العالم مساحة فهل تعرف اسم أصغر دول العالم مساحة؟ وهل تعرف اسم أكبر منطقة طبيعية في العالم؟ وهل تعرف أيضا أن هناك أماكن في العالم لا تشرق الشمس عليها لعدة أسابيع؟ وهل تعرف أين يوجد أطول مبنى في العالم؟ ربما لاتكون إجاباتك الإجابات الصحيحة مع هذا الأطلس تستطيع أن تقوم بجولة حول العالم دون أن تبرح مكانك ففي انتظارك عالم من الطرائف والغرائب داخل هذا الأطلس.
BOOK
Quantum transport simulations in a programmable nanophotonic processor
Environmental noise and disorder play critical roles in quantum particle and wave transport in complex media, including solid-state and biological systems. While separately both effects are known to reduce transport, recent work predicts that in a limited region of parameter space, noise-induced dephasing can counteract localization effects, leading to enhanced quantum transport. Photonic integrated circuits are promising platforms for studying such effects, with a central goal of developing large systems providing low-loss, high-fidelity control over all parameters of the transport problem. Here, we fully map the role of disorder in quantum transport using a nanophotonic processor: a mesh of 88 generalized beamsplitters programmable on microsecond timescales. Over 64,400 experiments we observe distinct transport regimes, including environment-assisted quantum transport and the ‘quantum Goldilocks’ regime in statically disordered discrete-time systems. Low-loss and high-fidelity programmable transformations make this nanophotonic processor a promising platform for many-boson quantum simulation experiments.
A large-scale, low-loss and phase-stable programmable nanophotonic processor is fabricated to explore quantum transport phenomena. The signature of environment-assisted quantum transport in discrete-time systems is observed for the first time.
Journal Article
Universal photonic artificial intelligence acceleration
Over the past decade, photonics research has explored accelerated tensor operations, foundational to artificial intelligence (AI) and deep learning
1
,
2
,
3
–
4
, as a path towards enhanced energy efficiency and performance
5
,
6
,
7
,
8
,
9
,
10
,
11
,
12
,
13
–
14
. The field is centrally motivated by finding alternative technologies to extend computational progress in a post-Moore’s law and Dennard scaling era
15
,
16
,
17
,
18
–
19
. Despite these advances, no photonic chip has achieved the precision necessary for practical AI applications, and demonstrations have been limited to simplified benchmark tasks. Here we introduce a photonic AI processor that executes advanced AI models, including ResNet
3
and BERT
20
,
21
, along with the Atari deep reinforcement learning algorithm originally demonstrated by DeepMind
22
. This processor achieves near-electronic precision for many workloads, marking a notable entry for photonic computing into competition with established electronic AI accelerators
23
and an essential step towards developing post-transistor computing technologies.
A photonic processor capable of running advanced artificial intelligence models with near-electronic precision is introduced, marking a substantial step towards post-transistor computing technologies.
Journal Article
On-chip detection of non-classical light by scalable integration of single-photon detectors
Photonic-integrated circuits have emerged as a scalable platform for complex quantum systems. A central goal is to integrate single-photon detectors to reduce optical losses, latency and wiring complexity associated with off-chip detectors. Superconducting nanowire single-photon detectors (SNSPDs) are particularly attractive because of high detection efficiency, sub-50-ps jitter and nanosecond-scale reset time. However, while single detectors have been incorporated into individual waveguides, the system detection efficiency of multiple SNSPDs in one photonic circuit—required for scalable quantum photonic circuits—has been limited to <0.2%. Here we introduce a micrometer-scale flip-chip process that enables scalable integration of SNSPDs on a range of photonic circuits. Ten low-jitter detectors are integrated on one circuit with 100% device yield. With an average system detection efficiency beyond 10%, and estimated on-chip detection efficiency of 14–52% for four detectors operated simultaneously, we demonstrate, to the best of our knowledge, the first on-chip photon correlation measurements of non-classical light.
The integration of single-photon detectors, as superconducting nanowire single-photon detectors, in photonic-integrated circuits is a goal of quantum information science. Here, Najafi
et al.
introduce a micrometer-scale flip-chip process enabling such a integration in a scalable way.
Journal Article
Intensifying Substance Use Trends among Youth: A Narrative Review of Recent Trends and Implications
Purpose of Review
Substance use among adolescents and young adults remains a critical public health concern, with patterns shifting dramatically in recent years. This narrative review examines trends in substance use behaviors during and following the COVID-19 pandemic.
Recent Findings
Epidemiologic evidence shows declines in the proportion of youth who are using most substances but intensified consumption patterns with rising levels of disorder among adolescents who use substances. This picture may reflect the greater potency, availability and accessibility of substances, vulnerabilities related to poor mental health, minoritization, as well as social factors including pandemic stressors, commercial and regulatory forces – in short, features of the agent (substance), host (person), and environment (context), consistent with a public health formulation.
Summary
Understanding trends in youth substance use and related problems, especially in the context of contributing factors, is critical for informing clinical care strategies and public health interventions to improve outcomes for youth across diverse populations.
Journal Article