Hsin-Yuan (Robert) Huang @RobertHuangHY Twitter profile | Pikagi

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Hsin-Yuan (Robert) Huang

@RobertHuangHY

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Thinking about how machines could discover new physics in our quantum universe 🎲 🎲 🎲

Cambridge, MA

https://t.co/w84TeYoT7x

Joined September 2011

Don't wanna be here? Send us removal request.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

1 year

My PhD journey ends today🧑‍🎓 I will continue my quest to study how quantum machines can learn new facets of our universe as a scientist @GoogleQuantumAI & a visiting researcher @MIT @Stanford @UCBerkeley 🌈 In 2025, I will be back @Caltech as an Assistant Professor of Physics🔭

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

☃️Is it easy to find local minima?☃️ When a physical system is cooled❄️, Nature doesn't always find the ground state but finds a local minimum In , we prove that a machine that can cool things to local minima is a universal quantum computer (1/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Are there exponential advantages in using quantum machines to learn about the physical world?🎯 We establish the affirmative through rigorous proofs + physical experiments on the Sycamore quantum processor in . [📜1/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

1 month

Just attended the #Caltech2024 commencement for my PhD🎓 The Clauser prize recipient was kept secret until the very last moment. I was stunned when the president announced my name. Heartfelt thanks to my advisor @preskill , friends, and family for their unwavering support!

@preskill

John Preskill

1 month

I’m so proud of ⁦ @RobertHuangHY ⁩, who received the Clauser Prize ⁦ @Caltech ⁩ for his PhD thesis “Learning in the Quantum Universe,” which was judged “to have the greatest potential to open new avenues of human thought and endeavor.” #Caltech2024

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

🤖 Can a machine *efficiently* learn and predict quantum dynamics with arbitrarily high complexity (e.g, exponentially high)? In our new paper with @sitanch and @preskill , we give an ML algorithm and prove that it accomplishes this wild task (📜1/7)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

This is my first time to publish at @ScienceMagazine !🔬 We show that quantum machines🤖 can learn exponentially faster about the physical world🔭. Coincidentally, today happens to be my birthday😋 and this is the one of the coolest present I got🎁

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Quantum advantage in learning from experiments

Quantum-enhanced strategies can provide a dramatic performance boost in learning useful information from quantum experiments.

www.science.org

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

❓Can classical machine learning models solve challenging problems in physics that no classical algorithms could solve? We prove the affirmative (for quantum many-body problems) in with @RichardKueng , @giactorlai , @victorvalbert , @preskill [🧵1/13]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

6 months

❓How to learn/train quantum circuits? Shallow quantum circuits are classically hard to simulate⚡️Despite extensive study, no efficient algorithm for learning/training them is known💀 In , we show how to learn any shallow quantum circuit efficiently💡

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

I am really happy to receive 2021 Google Ph.D. fellowship in Quantum Computing! 🤩

@GoogleAI

Google AI

3 years

Continuing our tradition of supporting outstanding graduate students in their pursuit of research in computer science and related fields, we congratulate our 13th annual PhD Fellowship Program recipients! See the list of 2021 Fellowship recipients below:

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

1 year

Last summer, @preskill and I worked with an amazing undergrad, Laura Lewis. She proved that an ML algo in our recent paper on @ScienceMagazine can be exponentially improved: Reducing the sample complexity from a large poly(n) to only log(n)! See 🧵1/7

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

Can we tell apart short-time & exponential-time quantum dynamics? In , we found the answer to be "No" even though it seems easy. This discovery leads to new implications in quantum advantages, faster learning, hardness of recognizing phases of matter ...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

Our proof that a classical ML model🤖 can learn to solve quantum problems⚛️ better than any classical algorithm🖥️ is now published in @ScienceMagazine 🔬 It is unbelievable to have two papers📜 out on Science in less than 4 months🤯🤯 This is surreal...

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Provably efficient machine learning for quantum many-body problems

A classical machine learning algorithm is proven to be computationally efficient for some many-body problems.

www.science.org

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

Q: Can we see highly complex entanglement from a few single-qubit measurements? I always assumed this is impossible as entanglement can be too nonlocal for local measurements to see In , we prove that it can actually be done for almost all quantum states

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

What can we ultimately learn about our quantum world?👁️ If we lost all knowledge, how should we re-build our knowledge about quantum systems?📚🔬 With @S_Flammia and @preskill , we formalize these questions using learning theory and make progress towards answering them (🧵1/8)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

An efficient method to learn an interacting system with many particles is central to building better quantum computers and sensors📡⚛️ The physical law sets a limit on how fast we can learn. In , we give the first protocol to reach this limit (📜1/10)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

In today’s Quantum Colloquium at Berkeley hosted by Umesh Vazirani, we demystify our recent results with @sitanch and @preskill proving that ML models can efficiently learn and predict arbitrarily complex (even exponentially complex) quantum dynamics🕵️

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Learning to Predict Arbitrary Quantum Processes

Hsin-Yuan Huang (Caltech)https://simons.berkeley.edu/events/quantum-colloquium-learning-predict-arbitrary-quantum-processesQuantum ColloquiumThe talk will be...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

4 years

My first quantum information paper is published on @NaturePhysics ! We combine quantum scrambling and statistical learning to design theoretically-optimal procedure for constructing an efficient classical representation of the quantum many-body system.

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Predicting many properties of a quantum system from very few measurements

Nature Physics - An efficient method has been proposed through which the properties of a complex, large-scale quantum system can be predicted without fully characterizing the quantum state.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Our work with @GoogleAI Quantum on understanding when quantum ML could predict better than classical ML is published today! 🥳🥳🥳

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Power of data in quantum machine learning

Nature Communications - Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here,...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

With @preskill and @KuengRichard , we prove that "on average, learning from classical and quantum data are comparable, but for accurate predictions on all inputs, there is an exponential quantum advantage." Now out as editor's suggestion on PRL. 😊😊

Information-Theoretic Bounds on Quantum Advantage in Machine Learning

On average, classical and quantum machine learning techniques are comparable, but for accurate predictions on $a\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}l$ inputs, there can be exponential...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

11 months

Laura Lewis' new work on rigorous ML algorithms for solving quantum many-body problems has just been covered by @QuantaMagazine ! Really happy to see that classical shadow🔦 has made its way to Quanta🌟

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Machine Learning Aids Classical Modeling of Quantum Systems | Quanta Magazine

By using “classical shadows,” ordinary computers can beat quantum computers at the tricky task of understanding quantum behaviors.

www.quantamagazine.org

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

Understanding the power and limitation of what we can do in the NISQ era has been a big question❓for me since the beginning of my Ph.D. I am really happy that we finally have something rigorous and general to say about NISQ💡

@sitanch

Sitan Chen

2 years

What are noisy intermediate-scale quantum devices good for? In a new paper joint with @JordanCotler , @RobertHuangHY , and @jerryzli , we define and study a new complexity class, NISQ, that captures the computational power of these devices 🧵 (1/n)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

My first IQIM blog is out! I talk about my dream of a world where intelligent beings🧠 are enhanced by quantum sensors📡, quantum memories💾, and quantum computers⚛️, and how one can use math to peek into such a fantastic world.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

Can quantum ML models learn from simple input data and predict accurately on *much* harder data? Most existing theories say no. Here, we prove that after learning quantum dynamics on a few simple unentangled states, one can predict accurately on arbitrarily high entangled states

@IMathYou2

Matthias C. Caro

2 years

Here goes my first #QML paper tweet in 2022: "Out-of-distribution generalization for learning quantum dynamics" ()!

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

I gave a talk at Simons Institute in Berkeley on using quantum information, statistical learning, and random matrix theory to prove that classical computers could learn to solve challenging quantum many-body problems. The recording is out:

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Provably Efficient Machine Learning for Quantum Many-Body Problems

Hsin-Yuan Huang (Caltech)https://simons.berkeley.edu/talks/provably-efficient-machine-learning-quantum-many-body-problemsQuantum Wave in Computing Reunion

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Is randomness necessary to estimate M observables from only log M quantum measurements, e.g., as in ? In , we show that randomness could be removed to yield even better performance (with application to quantum chemistry).

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Predicting Many Properties of a Quantum System from Very Few Measurements

Predicting properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

I will be playing devil's advocate😈 and speaking about how powerful classical AI could be (based on recent theoretical developments) and whether quantum AI should be agitated by the power of classical AI?

@PennyLaneAI

PennyLane

2 years

#QHack2022 Speaker 🎤 Join Hsin-Yuan Huang (Robert — @RobertHuangHY ) for a talk on: "How powerful is classical AI from the standpoint of quantum AI?" Tuesday Feb 15 at 12:00 Noon EST

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

I had great fun🥳at the Quantum Colloquium hosted by Umesh Vazirani, where I talked about making predictions in a quantum world. The renown of the previous speakers (Scott Aaronson, John Preskill, Ewin Tang, Mikhail Lukin...) is insane😮

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Making Predictions in a Quantum World | Quantum Colloquium

Hsin-Yuan Huang (Caltech)Quantum Colloquium, Nov 30, 2021https://simons.berkeley.edu/events/quantum-colloquium-making-predictions-quantum-worldMany scientifi...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

@NatRevPhys recently invited me to write a short article introducing classical shadows: a technique for learning a succinct classical representation of a quantum state. This technique has undergone many remarkable generalizations from the quantum community in the past year🫂👨‍🔬👩‍🔬

@NatRevPhys

Nature Reviews Physics

3 years

Tools of the Trade: Learning quantum states from their classical shadows. Hsin-Yuan Huang explains how to characterize quantum states using very few measurements, thanks to classical shadow tomography

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Neural network🤖 designs a new functional for DFT that improves some existing functionals and comes close to one of the best human-designed functionals. It seems plausible that AI could outperform the best human-designed functionals in the future✨

@GoogleDeepMind

Google DeepMind

@GoogleDeepMind

3 years

Can machine learning explain the world at the quantum level? New in @ScienceMagazine , with the code also released, our team shows that neural networks can radically improve density functionals with #DM21 . Read more: Code: 1/

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

I am curious to know what people think about the new perspective by Maria Schuld and Nathan Killoran😃 I think our ultimate goal is still quantum advantage (else we can just use classical computers) but we need some near-term goals to measure progress.

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Is quantum advantage the right goal for quantum machine learning?

Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

I will be giving a talk at QIP 2021 () combining "Information-theoretic bounds on quantum advantage in machine learning" with Richard Kueng and @preskill , and "Power of data in quantum machine learning" with @JarrodMcclean and other Google friends! 😆😆

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Friday | QIP2021

24th Annual Conference on Quantum Information Processing

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

1 year

@preskill @sitanch I squeezed in some time to conduct numerical experiments for learning quantum processes with up to 50 qubits and 10^6 evolution time; see the newest version of . The open-source code👩‍💻🧑‍💻is at (📜8/7)

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GitHub - hsinyuan-huang/learning-quantum-process: Using very few experiments to efficiently learn...

Using very few experiments to efficiently learn an approximate model of an n-qubit quantum process. - hsinyuan-huang/learning-quantum-process

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Thrilled to see that classical shadow is now on PennyLane!

@PennyLaneAI

PennyLane

3 years

"To light a quantum candle is to cast a classical shadow" -- Ursula K. Le Guin, probably Check out our newest demo, and learn all about the idea of classical shadows 💡⚛️🌒 Based on the paper by @RobertHuangHY , Richard Kueng, & @preskill

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

1 year

Credit to Chi-Yun Cheng🌟 for an artist's impression🎨 of me studying a machine🤖⚛️ living in the quantum universe (together with a beaver) --- inspired by my previous IQIM post:

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Peeking into the world of quantum intelligence

Intelligent beings have the ability to receive, process, store information, and based on the processed information, predict what would happen in the future and act accordingly. An illustration of r…

quantumfrontiers.com

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

One of the coolest parts😎 is that we demonstrated the significant quantum advantage on a real quantum computer🖥️⚛️: Sycamore superconducting processor built by @GoogleQuantumAI . Kudos🙏 to Michael Broughton on fighting with the spiteful quantum noise in a real device. [📜6/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Implication for quantum machine learners: People have begun to consider quantum ML models for quantum problems (instead of classical). This work shows that classical ML models can also solve quantum problems well and gives a strong baseline that QML needs to surpass. [🧵12/13]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

We believe this work presents a new possibility illustrating how the combination of quantum technology⚛️ and machine learning🤖 could enable powerful new strategies to learn about nature🍀. [📜9/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

In the 1st experiment, we consider a hybrid quantum-classical ML model (quantum-enhanced experiments🔬 + classical recurrent neural network🤖) for predicting observables. We observe >1000x improvement over the best classical strategy using the hybrid QML model. [📜7/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

1 year

@preskill Thank you, @preskill ! I had great fun in my PhD 🥳 It’s amazing to have you and Thomas Vidick as my PhD advisors!

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

The results are particularly surprising when we compare them with classical dynamics. In order for 1D classical circuits to look random, a linear depth is required (see Fig.). Strangely, 1D quantum dynamics only need log depth to look random!

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

A nice thread by Jerry Li about our new works viewed from a classical (in contrast to quantum) CS theory person. One of which is my first FOCS paper!🦊

@jerryzli

Jerry Li

3 years

Hi! So I was coerced into writing this thread---er I mean let me tell you a little bit about quantum learning and testing, and about some of the papers @sitanch , Jordan Cotler, @RobertHuangHY and I just posted on arxiv: 1/

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Since I started my Ph.D. at Caltech in 2018, almost all of my main projects are driven by my aspiration to unravel this problem. It is really nice to find a concrete answer after three years!😊😊 [🧵2/13]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

In the 2nd experiment, we consider the hybrid of quantum-enhanced experiments and unsupervised classical ML for uncovering symmetry♓️♑️ in 1D and 2D dynamics. The best known classical strategy failed❌ but the hybrid quantum-classical ML model succeeded⭐️. [📜8/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

1 year

It is great fun to mentor Laura during the summer. Every week there is new and exciting progress! She is going to talk about this work at QIP 2023 For QIP folks interested in rigorous ML for quantum physics, you should check out her talk! 🧵7/7

Quantum Information Processing 2023

26th Conference on Quantum Information ProcessingA message from TQC:The TQC Steering Committee is seeking a local organizer for the year 2024 and invites individuals who are interested in hosting the...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

Finding ground states is known to be hard for both classical and quantum computers🤖 As a result, ground states are not always "physical," as in they are not always physically observable When Nature cools a system in a cold bath❄️, she finds a local minimum of energy (2/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

6 months

While the paper in its current form is purely theoretical📜📜📜, our preliminary experiments on quantum computers show that the algorithm is amenable to training large-scale shallow quantum circuits🦣🦣 and many applications follow🌈 So, stay tuned for Version 2! 🌟🌟

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Implications for physicists: While ML has gathered a lot of attention recently, it has been unclear if ML can tackle challenging problems in physics that traditional algorithms fail. This work provides a rigorous foundation for future prospects of ML in physics. [🧵11/13]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

In conventional experiments, classical agents (scientists👨‍🔬👩‍🔬, ML models🤖, etc.) retrieve/store/process classical information0⃣1⃣ from experiments to learn about the physical world (using physical measurements🔭, classical memory📔& classical computers🖥️). [📜2/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

As quantum technology advances, we can begin to consider quantum agents/machines🦾 that retrieve/store/process quantum information⚛️ from experiments🧪 to learn about the world🌏 (using quantum sensors, quantum memory & quantum computers). [📜3/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

The measurement protocol is extremely simple. Every time, pick a random qubit and measure it in a random X/Y/Z basis; measure all the rest of the qubits in a fixed basis (e.g., Z). For an n-qubit system, do this for poly(n) times. That's it!

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

The key discovery💡 in this work is that: Given a quantum unitary/process with arbitrarily-high complexity, a low-complexity model🎟️ for predicting the outcome of the quantum process always exists. The ML model only has to discover that low-complexity model (📜4/7)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

One may wonder how ML algorithms can be computationally more powerful than non-ML algorithms 🧐. This is the topic of study in my prior work . Short answer: the computational power is in the data ML learns from. [🧵10/13]

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Power of data in quantum machine learning

Nature Communications - Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here,...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

We rigorously established the exponential quantum advantage🌟🔯💫 for predicting non-commuting observables, performing quantum PCA, and learning quantum dynamics. We utilized mathematical tools in with some new proof techniques. [📜5/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

A independent work by @felix_led and @NDLaRacuente focusing on designs provide another very interesting perspective via communication : Two parties can produce unitary designs by exchanging only O(1) qubits. This is also not true in a classical world!

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Approximate Unitary $k$-Designs from Shallow, Low-Communication Circuits

Random unitaries are useful in quantum information and related fields but hard to generate with limited resources. An approximate unitary $k$-design is an ensemble of unitaries and measure over...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

In this work, we compare the ability of classical and quantum machines🤖⚛️ to learn about the physical world. In particular, we prove that in various tasks, quantum machines can learn from exponentially fewer experiments than classical machines. [📜4/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

We then prove that our ML model can learn the low-complexity model🎟️ using the classical shadows of the unknown quantum unitary/process *extremely efficiently*. A key step in the proof resolves a conjecture on quantum Bohnenblust-Hille inequality (📜5/7)

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Quantum Talagrand, KKL and Friedgut's theorems and the...

We extend three related results from the analysis of influences of Boolean functions to the quantum setting, namely the KKL Theorem, Friedgut's Junta Theorem and Talagrand's variance inequality...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Congrats to @AnuragAnshu4 , @Qsrinivasan_1 , Tomotaka Kuwahara, @MSoleimanifar ! It is one of my favorite papers last year 🤩🤩

@NaturePhysics

Nature Physics

3 years

Sample-efficient learning of interacting quantum systems

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

@HyperboIeva I am here! I would also like to see our community continue to strive here. There is more random stuff on the feed nowadays, and it seems harder to find posts I missed.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

Our results resolved many problems in ML + quantum: (1) Quantum advantage showed exp. advantage in learning systems with highly nonlocal correlations. Our results show superpoly advantages in learning low-complexity system with only local correlations.

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Quantum advantage in learning from experiments

Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data from a physical...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

@jenseisert @preskill I thought I misheard! 🤣🤣

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Finally, big thanks to @giactorlai and @awscloud for the large-scale numerical experiments, @victorvalbert for insights into quantum phases of matter. And a huge toast 🍻🍻 to my best quantum friends @RichardKueng and @preskill for the extremely fun journey! 🥳🥳 [🧵13/13]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

I really enjoyed this collaboration with Chi-Fang Chen, @preskill , and Leo Zhou🍻 It is impossible to prove the result without the unique expertise each of them brings in I have wondered about the question for a long time. Really happy that it is now resolved!😌 (9/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

For local unitary perturbations, we show that the optimization (energy) landscape🌆 over the entire quantum state space has a large barren plateau🙁 This allows us to prove that finding local minima under local unitary perturbation is easy with a classical computer💻 (5/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Big thanks🫂 to all of my collaborators for realizing the fantastic results🌟! I really love this eclectic team ranging from classical learning theorists, theoretical physicists to quantum computing engineers, who have all contributed greatly. [📜10/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

While the main theorem is purely conceptual, we feel the protocol may be practically relevant. So, we spent a long time coding things up numerically. I'm a bit rusty on coding now👨‍💻 but I had a lot of fun. Thank you, @MSoleimanifar and @preskill , for working on this together!

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

This motivates the following question: How easy is it to cool physical systems to a local minimum of energy?☃️❄️ To answer this question, we develop new concepts/tools by building on optimization theory📊 quantum thermodynamics🔥 and Hamiltonian complexity🪢 (3/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

In contrast, a quantum computer can always find a local minimum efficiently This is shown using a thermal gradient descent algorithm📉 that mimics cooling in Nature☃️ Together, this proved that finding local minima is classically hard and quantumly easy (8/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

Our results on PRUs rely on an upcoming paper by @fermi_ma and me. While PRUs have been conjectured to exist, there are no known proof. This conjecture is resolved in this upcoming paper. The proof is presented in these talks at @SimonsInstitute :

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Workshop on pseudorandom unitaries

www.youtube.com

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

4 months

@letonyo Yes! This is state certification, which one can do by running a deep circuit that inverts the target state The main goal of this work is to understand whether single-qubit measurements could certify efficiently (yes for almost all states; still open if we want all states)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

I am super thankful to my amazing collaborators🌟 @RichardKueng , @giactorlai , @victorvalbert , and @preskill for making this happen!🙏

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

@preskill I share a similar sentiment. But I think a better way of stating this is: To summarize, we conclude that "currently", there is no indication that "existing" quantum ML will improve on data sets, "which classical ML already performs well".

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

The picture changes drastically when we consider thermal perturbations❄️🌡️ We develop a set of new techniques to analyze energy landscapes🔋 in thermodynamics Using these techniques, we prove that in many 2D systems, the landscape is nice & has no suboptimal local minima (6/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

@katzgraber @JarrodMcclean @dabacon @ScienceMagazine @preskill @GoogleQuantumAI I don’t know how to access the article as an author too😂 Here is the arXiv:

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Quantum advantage in learning from experiments

Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data from a physical...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

Our proof is eclectic 🛠️🪝🗝️📐 We give a better quantum optimization algo.💻 leading to new ineq., including quantum Bohnenblust-Hille. We then design an ML model🤖 using the ineq. to learn the low-complexity model🎟️ from the classical shadow🔦 of the quantum dynamics (📜6/7)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

We focus on two important kinds of quantum many-body problems: predicting ground states and classifying phases of matter [🧵4/13]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

6 months

Shallow quantum circuits are quantum circuits with a constant depth⚡️⚛️ Despite being constant depth, these quantum circuits can generate classical distributions that are hard to sample from🎇 using classical computers🖥️⌨️ See the proofs in .

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

6 months

We prove that any shallow quantum circuits⚡️⚛️ (with arbitrary unknown structure) can be efficiently learned/trained from a classical dataset🔖 This result provides a new hope✨for the negative results / dark vibes👿 around QML and variational quantum algorithms in recent years

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

4 years

Thanks to Jarrod McClean, Michael Broughton, and all the awesome collaborators for helping me realize this work! I had great fun at @Google during the summer (virtually)!

@JarrodMcclean

Jarrod McClean

4 years

It's gonna be tough to unravel even a fraction of the cool results in our recent paper in understanding quantum advantage in the presence of data - spearheaded by the amazing @MoMoRobertHuang but I'll try! (1/n)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

While our first proof is long, it has now become a very simple few-page proof. So check it out if interested! It has been an exciting journey with Tommy Schuster and Jonas Haferkamp @haferjonas ! I am really happy that many open problems I care deeply about are now resolved! 💖

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

I like to think of shadow overlap as the combination/enhancement of XEB (cross entropy benchmark) and classical shadow In many cases, shadow overlap behaves like a Hamming distance version of fidelity, similar to local fidelity

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

6 months

A surprising aspect🪄🦄 is that Learning can be done efficiently on a normal computer, i.e., training the quantum circuit is classically easy. However, a quantum computer⚛️ is needed to run and make predictions using the trained quantum circuit because of classical hardness.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

We prove that in n-qubit systems, > k-designs form in roughly k log(n) time on any geometry, including 1D. > PRUs form in poly log(n) time on 1D and poly log log(n) time on all-to-all geometry. The n scaling improved exponentially over what people previously know.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

We define a local minimum of an n-qubit system to be any state that has the minimum energy under small perturbations A natural choice from quantum computing (e.g., VQE) is local unitary perturbations🔢 However, physically, such perturbations should be thermal❄️🌡️ (4/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

The class of ML algorithms includes training neural networks that output an exponentially large density matrix. Interestingly, all of these can be done efficiently on a classical computer by using classical shadows of quantum states [🧵6/13]

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Predicting Many Properties of a Quantum System from Very Few Measurements

Predicting properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

For those who cannot go through the paywall, here is a thread and the arXiv link to our latest paper on @ScienceMagazine .

@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Are there exponential advantages in using quantum machines to learn about the physical world?🎯 We establish the affirmative through rigorous proofs + physical experiments on the Sycamore quantum processor in . [📜1/10]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

For example, shadow overlap can accurately benchmark a quantum device when the popular metric XEB fails. We also show that by combining shadow overlap with neural networks, we can rigorously predict properties of quantum systems that would normally require exp(n) measurements

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

6 months

I want to thank my collaborators @lyc1178 , @JarrodMcclean , @Isaac__kim , @AnuragAnshu4 , Michael Broughton, and Zeph Landau for working together to resolve this big open question in my heart💖💖💖

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

It has always been extremely fun to work with @preskill and @sitanch !!🤩 Thank you both for all the exciting discussions that led to this unbelievable result 🙏🙏 (📜7/7)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

This problem has been on my mind for 1.5+ years After meeting with @YT59529321 4-5 months ago, and with the help from Di Fang and Yuan Su, I am thrilled that we have resolved this problem🌟 (📜10/10)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

17 days

Our paper contains many other applications, e.g., the power of time reversal, anti-concentration of 2D random circuits, etc. There are likely more to be discovered! Rumor has it that our result can be used to show that spacetime can hide blackholes (!)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

10 months

This applies to a class of 2D systems where the ground states are highly entangled🧶 and are classically hard to find When the landscape is nice, cooling to a local minimum☃️ is the same as finding ground states So finding local minima is classically hard (7/9)

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

Some interesting generalizations: by Zhao, Rubin, Miyake. by Chen, Yu, Zeng, Flammia. by Hu, Choi, You. by Helson et al. from Jens Eisert's group.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

We design a class of ML algorithms that can predict ground states after learning from examples of some physical systems. Thm. 1 proves that these ML algorithms can learn/predict accurately and efficiently. [🧵5/13]

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

2 years

The exponential complexity🕸️ makes learning very challenging ⚔️ All known algorithms for learning a general unitary, e.g., our previous analysis in & , require exponential data size or runtime🫠 (📜3/7)

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Generalization in quantum machine learning from few training data

Nature Communications - The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the...

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

The certification is done by computing a number E[ω] between 0 and 1 from these randomized measurements. We call E[ω] the "shadow overlap". If the measured state is close to Ψ, E[ω] is close to 1, and vice versa. So, we can perform certification by looking at E[ω].

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

6 months

@Qottmann Was recovering from the extraordinarily fun and intense week from QIP xD TLDR is in the cooking now!

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

Now, say you think of an n-qubit state Ψ and ask: Is the state I just measured close to Ψ? (Certification) Maybe you are trying to synthesize a state in the lab to be as close to Ψ as possible. Or maybe you have created the model Ψ by training neural nets or tensor networks.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 months

Our main theorem states that for almost all Ψ, the data allows us to correctly answer the question. In particular, we can accurately certify any state Ψ that is a superposition over "well-connected" classical states/bitstrings.

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@RobertHuangHY

Hsin-Yuan (Robert) Huang

3 years

The YouTube video is out now! 😄😄

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QIP2021 | Fundamental aspects of solving quantum problems with...

Authors: Hsin-Yuan Huang, Richard Kueng, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, Jarrod McClean and John PreskillAffili...

www.youtube.com

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