2023 Opportunities
The Physics & Informatics (PHI) Lab is eager to welcome our next class of interns. NTT Research was launched in 2019 for the sole purpose of fostering fundamental research. Our internship program allows every participant to actively contribute to that research while learning from leading minds.
Internships duration varies from 3 months to one year depending on the project nature and
intern preference. Applicants should be physicists, computer scientists, brain scientists and electrical engineers enrolled in PhD programs and have an interest in exploring the interdisciplinary space between quantum information science and neuroscience using optical technologies.
Internship forms should be submitted by March 10, 2023 to assure full consideration. In addition to financial compensation, summer housing assistance and travel allowances are available to qualified candidates.
Examples of some potential projects:
| Feasibility study for an experimental coherent Ising machine based on measurement feedback in a novel few-photon limit |
| The role of noise in diffusion model-based generative modeling |
| Physical neural networks with lithium niobite nanophotonics |
| Delocalized photonic training of DNNs using coherent Netcast |
| Dissecting the mechanistic basis of concept learning |
| Temporal Trapping for Ultrafast Optical Strong Coupling |
| Squeezed vacuum for quantum communication and control |
| Physical neural networks with CMOS electronics |
| TFLN vertical-cavity surface-emitting OPOs |
| Novel measurement schemes for 2D materials |
| Inverse design of nonlinear waveguides |
| Brain vs Machine: How do neural networks perform logical computation with language? |
| Physics of driven-dissipative non-linear networks in the presence of single-photon on-site non-linearity |
| Driving nonlinear topological photonics by structured non-Hermitian lattices |
| Benchmarking against other quantum machines and modern heuristics |
| Dynamics of topological and non-Hermitian photonic systems |
| Single photon quantum non-linearity from gate-defined dots |
| High-Speed Time-Multiplexed Optical Computing Systems |
| In-situ periodic poling of ferroelectric thin films |
| Advanced approaches to chip-to-fiber coupler |
| Hardware error correction in photonics |
| Monolithic sync pumped TFLN OPOs |
| Electronic-photonic integration for machine learning |
| Frequency-multiplexed combinatorial optimization solver utilizing optical frequency combs |
Form for PHI Lab Internships
2022 Summer Internship Overview
A total of 26 graduate and undergraduate students from the United States, France, Italy and Japan participated in the PHI Lab internship program. Their project titles included:
| Geometric Considerations for Normalization Layers in Equivariant Neural Networks |
| Learning representation of the brain activity via convex recurrent neural networks |
| Energy Landscape and Landau Theory of Self-Supervised Learning |
| A Mechanistic Lens on Mode-Connectivity in Neural Network Loss Landscapes |
| Image sensing with Optical Neural Networks |
| Towards scalable physical neural networks with thin-film lithium niobate |
| A Photonic Ising Machine using Ultra-Low Optical Energy |
| Hybrid Ising machines combining dissipative and conservative dynamics |
| Optimal Curriculum Learning Strategies for Sequential and Trail-Tracking Tasks |
| SCoherent SAT Solvers |
| Photonic Landau levels in an exciton-polariton microcavity |
| Scaling analysis of Grover’s algorithm for 3-SAT problems |
| Squeezed-Quantum-Noise-Assisted Optimization for Quadratic Binary Problems by CIM-CAC |
| Effects of quantum noise on the performance of optimization on OPO networks |
| Creating lateral quantum wells in monolayer semiconductors by an electrostatic confinement mechanism |
| Frequency-Multiplexed C-band Free-Space Optical Platform for Matrix-Vector Multiplication |
| Simulation of a Quantum Time-multiplexed Photonic Resonator Network |
| Algorithm and Error Analyses of the Simulation of Time-Dependent Hamiltonians |
| Measuring Geometry of Waveguide Using AFM |
| A Self-Similar Sine-Cosine Fractal Architecture for Multiport Interferometers |
| Hard mask development for next-generation lithium niobate processing |
| Efficient Hamiltonian Encodings for Simulation of Electronic Structure
Hamiltonian |
| Modular Quantum Algorithms as Functional Transforms |
| Measurements' technique on 2D semiconductors |
| IR cavity caracterisationand outlook |
2021 Summer Internship Overview
A total of 22 graduate and undergraduate students from the United States, Canada, United Kingdom, France, India, and Japan took part in the PHI Lab internship program. Their project titles included:
| Neural Mechanics in Deep Learning Dynamics |
| Neural Network Pruning Algorithms |
| Deep Learning with CIM |
| Quantum Analog Computing for k-SAT Problems |
| Griffiths Phase in Residual Neural Networks |
| Weakly Quantum Physical Neural Networks |
| Max-Cut Quantum Algorithm Implementation on GPU |
| Max-Cut Quantum Algorithm Implementation on FPGA |
| Experimentally Demonstrating a CIM with an Optical Matrix-Vector Multiplier |
| Cyber CIM |
| Cyber CIM Architecture |
| Topological Physics in Optical Systems |
| Compressed Sensing Quantum Algorithm on GPU |
| Analog Feedback CIM experiment |
| Optical Simulation with NOPOs |
| Soliton Frequency Comb Experiment |
| Tensor Product Theory |
| Quantum and Classical Discrimination |
| Enhancement of Polariton-Polariton Interactions Via a Mediating Material |
Publications Co-authored by 2022 Intern Students
"What shapes the loss landscape of self-supervised learning?"
NeurIPS 2022 Workshop: Self-Supervised Learning - Theory and Practice,
https://arxiv.org/abs/2210.00638
Liu Ziyin, Ekdeep Singh Lubana, Masahito Ueda, and Hidenori Tanaka
|
"Mechanistic Mode Connectivity"
NeurIPS 2022 Workshop: Distribution Shifts (DistShift) Connecting Methods and Applications
https://arxiv.org/abs/2211.08422
Ekdeep Singh Lubana, Eric J. Bigelow, Robert Dick, David Krueger, and Hidenori Tanaka
|
"Geometric Considerations for Normalization Layers in Equivariant Neural Networks"
NeurIPS 2022 Workshop: AI for Accelerated Materials Design
https://openreview.net/forum?id=p9fKD1sFog8
Max Aalto, Ekdeep Singh Lubana, and Hidenori Tanaka
|
"Image sensing with multilayer, nonlinear optical neural networks"
https://arxiv.org/abs/2207.14293
Tianyu Wang, Mandar M. Sohoni, Logan G. Wright, Martin M. Stein, Shi-Yuan Ma, Tatsuhiro Onodera, Maxwell G. Anderson, Peter L. McMahon |
"A Self-Similar Sine-Cosine Fractal Architecture for Multiport Interferometers"
https://arxiv.org/abs/2209.03335
Jasvith Raj Basani, Sri Krishna Vadlamani, Saumil Bandyopadhyay, Dirk R. Englund, Ryan Hamerly |
"Coherent SAT Solver"
Sam Reifenstein, Timothee Leleu, Tim McKenna, Marc Jankowski, Myoung-Gyun Suh, Edwin Ng, Farad Kyoyratee, Yoshitaka Inui, Zoltan Toroczkai,
and Yoshihisa Yamamoto |
"Benchmark study on quantum algorithms for combinatorial optimization problems"
https://arxiv.org/abs/2105.03528
Krishanu Sankar, Artur Scherer, Satoshi Kako, Sam Reifenstein, Navid Ghadermarzy, Willem B. Krayenhoff, Yoshitaka Inui, Edwin Ng, Tatsuhiro
Onodera, Pooya Ronagh and Yoshihisa Yamamoto |
Publications Co-authored by 2021 Intern Students
"Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks"
Advances in Neural Information Processing Systems (NeurIPS), 2021
https://arxiv.org/abs/2105.02716
Hidenori Tanaka, Daniel Kunin
|
"Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning"
Advances in Neural Information Processing Systems (NeurIPS), 2021
NeurIPS 2022 Workshop: Distribution Shifts (DistShift) Connecting Methods and Applications
https://proceedings.neurips.cc/paper/2021/file/2578eb9cdf020730f77793e8b58e165a-Paper.pdf
Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka
|
"Geometric Considerations for Normalization Layers in Equivariant Neural Networks"
NeurIPS 2022 Workshop: AI for Accelerated Materials Design
https://openreview.net/forum?id=p9fKD1sFog8
Max Aalto, Ekdeep Singh Lubana, and Hidenori Tanaka
|
"Image sensing with multilayer, nonlinear optical neural networks"
https://arxiv.org/abs/2207.14293
Tianyu Wang, Mandar M. Sohoni, Logan G. Wright, Martin M. Stein, Shi-Yuan Ma, Tatsuhiro Onodera, Maxwell G. Anderson, Peter L. McMahon |
"A Self-Similar Sine-Cosine Fractal Architecture for Multiport Interferometers"
https://arxiv.org/abs/2209.03335
Jasvith Raj Basani, Sri Krishna Vadlamani, Saumil Bandyopadhyay, Dirk R. Englund, Ryan Hamerly |
"Coherent SAT Solver"
Sam Reifenstein, Timothee Leleu, Tim McKenna, Marc Jankowski, Myoung-Gyun Suh, Edwin Ng, Farad Kyoyratee, Yoshitaka Inui, Zoltan Toroczkai,
and Yoshihisa Yamamoto |
"Benchmark study on quantum algorithms for combinatorial optimization problems"
https://arxiv.org/abs/2105.03528
Krishanu Sankar, Artur Scherer, Satoshi Kako, Sam Reifenstein, Navid Ghadermarzy, Willem B. Krayenhoff, Yoshitaka Inui, Edwin Ng, Tatsuhiro
Onodera, Pooya Ronagh and Yoshihisa Yamamoto |
Last Summer’s Lecture Schedule
The 2021 series consisted of eight lectures over eight weeks during July and August and covered multiple subjects pertaining to quantum information and neural networks.
| Week 1 |
Yoshihisa Yamamoto |
Fundamental Postulates in Quantum Mechanics: Quantization, Projection, and Symmetrization Postulates |
| Week 2 |
Edwin Ng |
Digital Software for Simulating Closed/Open Quantum Systems, QuTiP and Beyond |
| Week 3 |
Adil Gangat |
Tensor Network Theory: How to Efficiently Simulate a Quantum Spin Chain on Digital Platform |
| Week 4 |
Jess Riedel |
Quantum Darwinism: When Classical Reality Surfaces from Quantum Substrate |
| Week 5 |
Tatsuhiro Onodera |
Introduction to Differential Programming |
| Week 6 |
Ryan Hamerly |
Design Principles for Machine Learning Hardware |
| Week 7 |
Logan Wright |
Open Questions and Challenges for the Physics of Intelligence |
| Week 8 |
Hidenori Tanaka |
Physical Principles of Learning Dynamics |
Finally, on November 30th there was an Intern Reunion at NTT OneVision Center in Sunnyvale, CA that featured presentations by all the interns and the awarding of the NTT Quantum Researcher Certificates to each.
