Rare opportunity to be considered for USRA quantum team. Very flexible towards a variety of profiles (junior to senior). Work to be done in USA (but UK possible). Topics: AI Reasoning, QEC/FT Architectures, Quantum Algos, Quantum Thermodynamics. Likely NASA affiliation.
workforcenow.adp.com/mascsr/…
Boeing, Axiom Space, Vescent, Qrypt, USRA join Infleqtion, Accenture, and Nebula Space Enterprises in DOE’s Quantum in Space MOU! Learn more: bit.ly/3PExDGP
HPE, qolab, 1QBit, UCSB, Synopsis, AMAT, USRA/NASA, FNAL punching hard in this latest position paper on quantum supercomputing! community.hpe.com/t5/blogs/b…
The eventual integration of quantum technologies into the IT/HPC infrastructure of the world is inevitable. Whatever the future holds, today there is a new force bringing stakeholders together to develop and deploy solutions that are
inherently modular and transparently engineered. Good luck to TreQ, the quantum engineering company!
Happy to advertise the great work led by our @phase_filip in collaboration with @UDelaware (Safro group) and @rigetti - to be presented in a few weeks at the IEEE High Performance Extreme Computing Conference: A Multilevel Approach For Solving Large-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization arxiv.org/pdf/2408.07793 - truly one of the largest scale experimental demo of hybrid quantum computing with remarkable performance and outlook for improvement.
The paper creates a new framework that can be profitably executed leveraging Ising machines of arbitrary size to solve problems of arbitrary size. We have some numerical evidence that for soc-epinions graphs (26k nodes MaxCut) if subproblems can work on thousands of variables then the decomposition method beats the state-of-art. We also have initial experimental evidence that quantum solvers as implemented by us are qualitatively different and can potentially allow faster convergence in some "oracle" terms.
Lots to built upon. It is painful to work in the era of utility scale "augmented NISQ" we live in - but we are up to the challenge because lessons from theory cannot be the only guidance. Lots of QPU hours to burn. Lot of noise to tame. No pain no gain. Even if reliable advantage is not behind the corner, with every sweat drop we learn something valuable. We are far past running toy problems.
More discussions on these general considerations at the IEEE QCE24 "The Next NISQ Phase" Panel: qce.quantum.ieee.org/2024/pr…
If you think Ising solvers do not intersect this next wave of gen-AI think again! In our recent preprint arxiv.org/abs/2407.00071 with Icosa and HPE we look at the statistics from LLM trying to think, exploiting it to define a combinatorial cost function that if optimized could lead to a powerful chain-of-thought. Intriguing!
Finally out the review paper summarizing the scientific output of the NASA Quantum AI Lab for the last 4 years… sciencedirect.com/science/ar… - the easiest way to answer the question “what is NASA doing in quantum computing” :) we should do this every 2-3 years going forward (see also papers in 2017
sciencedirect.com/science/ar… and 2019 books.google.com/books?hl=en…)
Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping
scirate.com/arxiv/2404.01412
Say you want to ≈solve a diagonal Hamiltonian but only have access to a quantum device that is fairly noisy (like, well, everyone who has access to quantum devices) 1/n
What you need is to encode your Hamiltonian evolution in dual-rail subspaces (doubling the number of qubits), average over N orderings of the encoding and then correct the measured estimators with a prefactor. The end result beats symmetry-verification discarding and probabilistic error cancellation. 2/3
But it is more general than that! You don’t need dual rail (any k-hot work), you don’t need qubits (qudits work) and the theory is formulated very generally for Lindblads in analogy to decoherent free subspaces. Work sponsored by @sqmscenter 3/3
In our recent paper “Uniformly Decaying Subspaces for Error Mitigated Quantum Computation” led by @NishchaySuri we present a nice way to correct noisy expectation values of observables quantum or digital simulation in the presence of inhomogeneous dissipative noise arxiv.org/abs/2403.00163 1/3
Greetings from sunny Minneapolis #APSMarchMeeting . As customary gentle advertising in the first reply with links to 14 (!) NASA Quantum AI Lab talks! (Soon will do🧵 on #1 and #5 😉🤓) - check out also the 28 (!!!) talks of @sqmscenter members by searching SQMS in the Epitome